Bahasa "Pull" Solution for Distribution and Sales

"Pull" Solution for Distribution and Sales

"Pull" Solutions for Projects

“Pull” Solutions for Manufacturing

Nuances of TOC Concepts

Scientific Thinking

Systems Thinking & Innovation

TOC Leadership Paradigms

BahasaAny business which manages stock to meet customer needs faces a key challenge in stock management – i.e. how much to hold or in other words what is the right inventory. It would seem that almost all the mess of shortages and surpluses is caused by not knowing how much inventory to keep. The apparent conflict seems to be one between keeping more inventories to protect sales or keeping fewer inventories to control costs (and other damages of high inventory viz. obsolescence, etc.)The conventional approach to solve this conflict has been a compromise between holding low inventory at month end (to ensure good reporting) and high inventory in between. This approach however has no impact on the working capital requirements of the company. So what we need is a breakthrough that provides adequate protection with lower inventory. We know the level of protection is very much dependent on a combination of two important factors – the rate of sales and the supply lead-time. So the only way to reduce inventory while providing protection is to reduce the supply lead-time. Many customers feel that reducing supplier’s lead time is not within their focus of control but the fact remains that the bigger part of the supply lead time has nothing to do with the supplier manufacturing lead time. It has to do with the ordering behavior of the customer.Over the years, Min-Max and Reorder Point (ROP) based ordering mechanism has become well entrenched in the mental model of most managers. The problem of erroneous ordering behavior is institutionalized in the ROP model.To analyse the problem with the ROP systems, we need to understand its basic principles.When the inventory level of an item goes down to a pre-specified minimum level, an “economic order quantity” (EOQ) is ordered to replenish the system. This EOQ is intended to be a balanced quantity that is optimal between the requirement to lower purchasing costs (or production costs) and the requirement to lower the carrying cost of the inventory. If the EOQ ordered arrives instantly, then the “maximum” inventory for that item is reached.A significant drawback of this model is that it assumes the demand is steady and constant. The assumption that the demand will deplete the stock at the same rate as used in original calculations, while the order is being processed, may be grossly flawed. It can so happen that the actual demand has slowed down significantly and the time to deplete the safety stock during the supply lead time has actually gone up many times, more than the actual supply lead time. So there is no need to even place an order at the reorder point level since stocks are moving very slowly.On the other hand, waiting for the re-order point may be too late when demand is moving at a much faster rate (than initially calculated) and the chances of a stock out is very high as stocks are likely to be wiped out much faster during the supply lead time (the calculated min will not provide the required protection). So when the demand rate is highly variable, ROP-based ordering behavior can neither give protection on stock–outs nor prevent the problem of unwanted inventory.In many cases, the EOQ does not lead to any significant decrease in costs, either on ordering or on the goods ordered. The damage that it creates is quite significant; it delays ordering and limits the ability of the system to react to a sudden upsurge in demand or exposes the system to a temporal deterioration of supply lead time, thus leading to a possible loss of sale.The only way out is to have a system of Order Daily and Replenish Frequently to a Norm, which is set to a maximum possible consumption during supply lead-time. Or in other words, we have a system where the minimum is exactly equal to the maximum. There is no ROP; every consumption from the norm generates an order.The only way out is to have a system of Order Daily and Replenish Frequently to a Norm, which is set to a maximum possible consumption during supply lead-time. Or in other words we have a system where the min is exactly equal to the max. There is no ROP; every consumption from the norm generates an order.To understand the new paradigm one needs to get out of the following erroneous paradigms of an ROP system.<ul><li>Order of small lots as per daily consumption does not inflate costs. It is just the transmission of consumption information on a daily basis. Any existing IT system can help transmit this information</li><li>Every order need not be matched by a back-to-back delivery</li><li>Every order need not be delivered in a fixed lead time – when the stock is adequate a higher lead time is fine but if there is stock out, much lower than standard lead time is preferred</li></ul>So what the customer wants from the supplier is not delivery on a fixed lead time but a commitment to prevent stock-outs for the customer. These are two different commitments with differing paradigms of behavior expected from both supplier and customer. For example, the supply lead time can be higher if the stocks are high and vice-versa. At the same time, when the customer is translating the daily consumption information to the supplier, the batching decisions are not set by the customer but it is best left to the supplier. The actual batching considerations in most supply chains are actually combinations across SKUs both in production &amp; transportation, so there is no need for the customer to upfront batch for an SKU for a supplier. A customer cannot and need not try to create batching for the supplier.When an SKU is not required to be delivered by a fixed time and the supplier is provided the flexibility to react to it, he can use the consumption information to decide his batching requirements. For example, many SKUs can be sent in one dispatch container rather than big batches of a few to fill up the same dispatch container. Or a supplier can batch small requirements of one customer with another customer to create his production batch requirements.So as a customer, we need to provide complete transparency of the stock situation on a daily basis which in turn will provide the relative urgency of the requirement of every SKU.When SKUs are too many, we need color tagging systems to quickly conclude on the adequacy of stocks across SKUs. For example, if the stocks are too low (about zero to 1/3rd of the norm) they can be tagged as red and if it is reasonably high (between 2/3rd of the norm till the top of norm) they can be tagged as green and the middle band can be in yellow. A supplier getting multiple orders across SKUs can prioritize the orders and take the best decisions which will help him to optimize his production and transportation while ensuring the stock outs are prevented at the customer’s end.Such a system also requires an alternative approach towards the supplier. A promiscuous relationship based on negotiating every deal to get the best out of multiple suppliers for the same item will not work in this case.The suggested model requires that the customer treat his supplier as a long-term partner with period-based pricing arrangements rather than order-based pricing.This is the only way to establish a real Win-Win for the supplier and the customer.

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Effective inventory management is vital for businesses. Traditional Reorder Point (ROP) systems often can't adapt to demand changes, causing shortages or surpluses. An innovative daily ordering system is suggested that can align seamlessly with market demand while reducing overall inventory in the organization.

A New Inventory Management Paradigm for Made to Stock Companies

Any business which manages stock to meet customer needs faces a key challenge in stock management – i.e. how much to hold or in other words what is the right inventory. It would seem that almost all the mess of shortages and surpluses is caused by not knowing how much inventory to keep. The apparent conflict seems to be one between keeping more inventories to protect sales or keeping fewer inventories to control costs (and other damages of high inventory viz. obsolescence, etc.)The conventional approach to solve this conflict has been a compromise between holding low inventory at month end (to ensure good reporting) and high inventory in between. This approach however has no impact on the working capital requirements of the company. So what we need is a breakthrough that provides adequate protection with lower inventory. We know the level of protection is very much dependent on a combination of two important factors – the rate of sales and the supply lead-time. So the only way to reduce inventory while providing protection is to reduce the supply lead-time. Many customers feel that reducing supplier’s lead time is not within their focus of control but the fact remains that the bigger part of the supply lead time has nothing to do with the supplier manufacturing lead time. It has to do with the ordering behavior of the customer.Over the years, Min-Max and Reorder Point (ROP) based ordering mechanism has become well entrenched in the mental model of most managers. The problem of erroneous ordering behavior is institutionalized in the ROP model.To analyse the problem with the ROP systems, we need to understand its basic principles.When the inventory level of an item goes down to a pre-specified minimum level, an “economic order quantity” (EOQ) is ordered to replenish the system. This EOQ is intended to be a balanced quantity that is optimal between the requirement to lower purchasing costs (or production costs) and the requirement to lower the carrying cost of the inventory. If the EOQ ordered arrives instantly, then the “maximum” inventory for that item is reached.A significant drawback of this model is that it assumes the demand is steady and constant. The assumption that the demand will deplete the stock at the same rate as used in original calculations, while the order is being processed, may be grossly flawed. It can so happen that the actual demand has slowed down significantly and the time to deplete the safety stock during the supply lead time has actually gone up many times, more than the actual supply lead time. So there is no need to even place an order at the reorder point level since stocks are moving very slowly.On the other hand, waiting for the re-order point may be too late when demand is moving at a much faster rate (than initially calculated) and the chances of a stock out is very high as stocks are likely to be wiped out much faster during the supply lead time (the calculated min will not provide the required protection). So when the demand rate is highly variable, ROP-based ordering behavior can neither give protection on stock–outs nor prevent the problem of unwanted inventory.In many cases, the EOQ does not lead to any significant decrease in costs, either on ordering or on the goods ordered. The damage that it creates is quite significant; it delays ordering and limits the ability of the system to react to a sudden upsurge in demand or exposes the system to a temporal deterioration of supply lead time, thus leading to a possible loss of sale.The only way out is to have a system of Order Daily and Replenish Frequently to a Norm, which is set to a maximum possible consumption during supply lead-time. Or in other words, we have a system where the minimum is exactly equal to the maximum. There is no ROP; every consumption from the norm generates an order.The only way out is to have a system of Order Daily and Replenish Frequently to a Norm, which is set to a maximum possible consumption during supply lead-time. Or in other words we have a system where the min is exactly equal to the max. There is no ROP; every consumption from the norm generates an order.To understand the new paradigm one needs to get out of the following erroneous paradigms of an ROP system.<ul><li>Order of small lots as per daily consumption does not inflate costs. It is just the transmission of consumption information on a daily basis. Any existing IT system can help transmit this information</li><li>Every order need not be matched by a back-to-back delivery</li><li>Every order need not be delivered in a fixed lead time – when the stock is adequate a higher lead time is fine but if there is stock out, much lower than standard lead time is preferred</li></ul>So what the customer wants from the supplier is not delivery on a fixed lead time but a commitment to prevent stock-outs for the customer. These are two different commitments with differing paradigms of behavior expected from both supplier and customer. For example, the supply lead time can be higher if the stocks are high and vice-versa. At the same time, when the customer is translating the daily consumption information to the supplier, the batching decisions are not set by the customer but it is best left to the supplier. The actual batching considerations in most supply chains are actually combinations across SKUs both in production &amp; transportation, so there is no need for the customer to upfront batch for an SKU for a supplier. A customer cannot and need not try to create batching for the supplier.When an SKU is not required to be delivered by a fixed time and the supplier is provided the flexibility to react to it, he can use the consumption information to decide his batching requirements. For example, many SKUs can be sent in one dispatch container rather than big batches of a few to fill up the same dispatch container. Or a supplier can batch small requirements of one customer with another customer to create his production batch requirements.So as a customer, we need to provide complete transparency of the stock situation on a daily basis which in turn will provide the relative urgency of the requirement of every SKU.When SKUs are too many, we need color tagging systems to quickly conclude on the adequacy of stocks across SKUs. For example, if the stocks are too low (about zero to 1/3rd of the norm) they can be tagged as red and if it is reasonably high (between 2/3rd of the norm till the top of norm) they can be tagged as green and the middle band can be in yellow. A supplier getting multiple orders across SKUs can prioritize the orders and take the best decisions which will help him to optimize his production and transportation while ensuring the stock outs are prevented at the customer’s end.Such a system also requires an alternative approach towards the supplier. A promiscuous relationship based on negotiating every deal to get the best out of multiple suppliers for the same item will not work in this case.The suggested model requires that the customer treat his supplier as a long-term partner with period-based pricing arrangements rather than order-based pricing.This is the only way to establish a real Win-Win for the supplier and the customer.

A new inventory management paradigm for made to stock companies - Vector Consulting Group

Manufacturing companies all over the world have to deal with inventory. Usually, they end up with too much of it or too little of it – never seem to get the right amount at the right place at the right time. JIT or ‘just in time’ was developed way back in the 1970s by Taiichi Ohno to manage inventory at the Toyota Manufacturing Plant. When the automotive company Toyota became wildly successful, JIT and the rest of Toyota Production System (TPS) was duly credited for this success. Subsequently, over the years, JIT became the most popular technique for managing material flow and inventory, especially among automobile companies.The concept of JIT itself is beautifully simple. Consider implementing JIT in your day to day activities, e.g. yourself arriving for a meeting. The main focus of implementing JIT would be to remove wastages (in this case time) and reduce the difference between your arrival and the start of the meeting to as close to zero as possible. So, conceptually there is no problem – i.e. JIT would make you extremely efficient if you can exactly align your activities in your day. However, we all know how difficult this is to ensure in reality! Automobile companies, and all manufacturing companies, for that matter) are also finding out that this is not a simple task.So how does JIT work in manufacturing and automobile companies?Manufacturing companies have implemented this approach for managing material flow by producing at just the right time so that it reaches the retailer through the entire supply chain to be sold to the customer at just the right time. Suppliers and customers along the value chain are expected to co-ordinate in such a way that any finished, intermediate or final goods are immediately collected and processed further, rather than put on stock. Within a manufacturing plant also material is re-filled or scheduled to arrive exactly when needed for manufacturing. Any movement of inventory before the actual need for it can generate excess inventory, and any movement later will create shortages. In the JIT methodology, this movement or flow of material is controlled through the Kanban system (a material ‘pull’ mechanism) that provides the necessary need/ demand triggers. In order for manufacturing/ automobile company to avail the benefits of the JIT system, there should be optimum synchronization between all the nodes in the supply chain. Actions such as standardization, quality initiatives, set up time reduction, etc., are taken to reduce variability in the system and improve synchronization.However, even in the best of times, this synchronization is difficult to get right. The current pandemic put a major spoke in the wheels of JIT in manufacturing or automobile companies. So, in this article, I would like to discuss the basic assumptions of this concept and why the pandemic affected it so badly.Why did JIT fail during the COVIDJIT was introduced to enhance speed to market and to reduce operational costs. The underlying assumption is that if the material flow in the supply chain of the manufacturing/ automotive company increased, the desired objectives of the company (better inventory turns, sales, etc.) would be achieved. Also, since increased material flow would bring down the material waiting periods, it would eventually bring down inventory. Hence, JIT is also seen as a great tool for inventory reduction!However, in practice, the flow concept of JIT improved material flow only for the OEMs or original equipment manufacturers (within their factory), and, in fact, led to deterioration of material flow of the entire supply chain if you include the suppliers and dealerships in this value chain. A close examination of the supply chain of manufacturing companies will show that this methodology, instead of reducing overall inventory, manifested itself as a mechanism for inventory relocation within the supply chain in favor of the OEMs. Let me explain.Let us visualize the supply chain of any manufacturing/ automobile company as having three major entities – Suppliers, the OEM and Dealerships.<figure class="image"><img style="aspect-ratio:4500/4500;" src="https://new-stage-be.vectorconsulting.in/storage/editor_uploads//jo-blog-3-img-1-01-01-01-01-01_1716876701.png" width="4500" height="4500"></figure>Typically, when the OEM implements JIT principles to keep itself “lean”, it pushes out inventories on either side (Raw Materials [RM] at Suppliers and Finished Goods [FG] to Distributors). So, while the OEMs may run lean operations, their suppliers and dealerships almost always struggle with high inventories. Since the power equations are skewed in favour of the OEMs (especially in automobile companies) , they are able to ensure reliable supplies by insisting that the suppliers maintain enough material in their FG to support their production shifts. The actual amount of FG that the supplier maintains depends on the supplier’s degree of reliability. In my experience, this can vary anywhere from three days to 10 days of FG inventory across suppliers. (This is in addition to the WIP and RM inventory they carry for their operations).<figure class="image"><img style="aspect-ratio:4500/4500;" src="https://new-stage-be.vectorconsulting.in/storage/editor_uploads//jo-blog-3-img-2-01-01-01-01-01_1716876720.png" width="4500" height="4500"></figure>Downstream in the supply chain are the dealers. Each dealership maintains at least a month’s worth of inventory (this is a conservative estimate). During the pandemic, this has gone up to 3-4 months inventory and even more in some cases.Who bears the cost of inventory?The inventory they carry is naturally at a cost to both the automotive suppliers and dealerships. It is not only the inventory holding cost but also the associated costs of space, manpower and equipment that falls on them. What’s more, the money locked in inventory blocks working capital of both the entities. It is often a huge financial burden on them.If I have to put it in simple words, it is as if the OEMs have passed on their inventory and costs to their partners. So essentially this means that JIT has served not only as a mechanism for relocation of inventory but a relocation of costs as well! So, while the OEMs may operate with reduced inventory/ costs at their end, the rest of the automotive supply chain does not experience this benefit, and, may at times be running at pretty high inventory levels. So, as far as the suppliers and dealerships in the automotive/manufacturing companies’ supply chain were concerned, it was already an unequal paying field when COVID-19 hit.Impact of JIT on OEM’s supply chainThe impact of this pandemic on the supply chain was on two fronts – Operational and Financial.Operational – Most OEMs who have implemented JIT tend to have critical suppliers located near or inside their premises. However, many of their non-critical suppliers may be located in other parts of the country. During the pandemic, many OEMs found themselves located in areas that were open and operational, but some of their suppliers were under complete lockdown. The consequent disruption of supplies may have been mostly from some non-critical suppliers. But the problem with this is that OEMs are assembly plants. Assembly plants are where many parts come together to produce a vehicle. This means that even if a single part is missing (critical or otherwise) the assembly/ vehicle cannot be made. Moreover, since the OEMs have implemented JIT, it would be operationally very lean. It will have very low inventories in RM, WIP and FG. So, even if the suppliers were holding a lot of inventory (for their OEMs) and this inventory is badly required by the OEMs (remember relocation of inventory), there would be a delay in supplies, and the production of the OEMs would suffer.This is not a one-time story of a few suppliers. Since OEMs tend to have 500 or more suppliers, the pandemic would randomly disrupt supplies from different suppliers at different points in time. This precipitated a nightmare for the OEM buyers!! I cannot imagine how they are coping.Financial – Now let us discuss the financial aspect. As I mentioned previously, the significant part of the suppliers’ and dealerships’ working capital is as it is blocked by the inventory they carry. With the pandemic and the resultant lockdown, capital rotation also stopped. So, even after the lockdown lifted, many of them found themselves without the capital to restart operations. The inventory they carry is like a ticking time bomb for some dealerships – it will eventually explode, and many dealerships may have to close shop under the pressure of debt. Many suppliers may also close unable to survive this business stagnation (particularly tier 2 and 3 suppliers). If suppliers close down, this will again disrupt supplies to the OEMs.Even if these suppliers survive, because of these capital problems, the supplies from different suppliers will ramp up differently. Financially strong suppliers will ramp up fast. The weaker ones will ramp up very slowly. This implies that with suppliers at different stages of ramp up, component availability in Full Kit for assembly will continue to be a challenge.Impact of JIT on manufacturing operations of OEMsThe manufacturing companies with JIT processes took a hit not only in their supply chain but also have to face consequences in their operations as well. JIT advocates the concept of a balanced line. This essentially implies that all the work centres in a particular line all have the same capacity or balanced capacity. If you can operate with a balanced capacity line, no particular work centre is a bottleneck. The entire line is the bottleneck because the capacity of the line is defined by the capacity of its work centres, which is all equal. The problem with operating with a balanced line is that to get the expected output, all the work centres must operate with very high efficiencies. In such a line having numerous work centres, if even one person is absent or one work centre slows down, there is a significant risk of output from the line falling drastically or stopping altogether. Since the pandemic brought along with it severe resource crunch, particularly of manpower (either due to lack of availability or due to social distancing mandates), production managers of OEMs practicing JIT would have had a tough time managing the shop floor!Conceptual lacuna or implementation issues?With so many issues during the pandemic, there was an uproar by some in the industry about the conceptual standing of JIT. At the same time, many argued that these problems were really not due to a conceptual weakness but the result of poor implementation. I would argue that it is a bit of both. Let me explain.If we look at the automotive industry, it will be observed that it has been going through a major churn, even before the pandemic. The environment has become increasingly uncertain. Four major factors are contributing to this uncertainty – Competition, Technology Changes, Vehicle Norms/ Regulations and natural calamities. Competition is increasing and therefore the time to introduce new models is reducing. These days on an average, across segments, a new model or its variant gets launched every 2-3 years. This time between launches is expected to reduce even further in future. This implies that the OEMs portfolio of products is going to increase, and the churn of the entire portfolio will also be very high. In addition, technology is changing fast, and new vehicle norms are being introduced at a rapid pace. Also, the world is experiencing more natural calamities leading to disruptions in the entire supply chain. These calamities are not only increasing in terms of variety but also in terms of frequency. While implementing JIT involves actions to continuously reduce variability in the supply chain from supplier to plant, JIT’s Achilles heel is high environmental uncertainty and demand volatility – small disturbances can create a ripple effect. During the pandemic, there were severe uncertainty both on the demand side (spike, fall, spike, depending on intermittent lockdowns) and the supply side (labour shortage, production capacity shortage, container shortage, warehouse space shortage, etc.). JIT is just not designed for so much uncertainty – without any redundancy in the system, many supply chains fell apart.Now, let us examine the problem with its implementation. In India, across most OEMs (both automotive and manufacturing companies), the JIT principles are only deployed at the OEMs premises. They are not extended to Dealerships or Suppliers. For instance, a Kanban system is not extended to dealerships or deployed across tier I, II and III suppliers. Therefore, there are two separate worlds created within the supply chain – a JIT world of OEMs and the non-JIT world of its partners. This demarcation creates the problems for the supply chain. If implementation of JIT is not restricted thus, but also includes just in time manufacturing, then the agility of these companies to respond to vagaries and black swans will be much better. Some firms who have done both just in time manufacturing and supply , have been able to respond to this pandemic much better than others.Having said that, I believe JIT, in its current form, is not a robust concept / methodology / management philosophy for the future. Environmental uncertainties are on an increase. The need of the emerging VUCA (Volatile, Uncertain, Complex and Ambiguous) world is a supply chain methodology that ensures short lead times and agility, while also offering reasonable protection from uncertainty and volatility. For this, JIT’s objective of near zero inventory has to be re-thought. Zero inventory thinking is focused on the assumption that all variability in a system can be reduced and controlled. While good quality practices, standardization of components, etc. can bring many operational variables under control, uncertainty of the environment over which manufacturing/ automobile companies have very little sway cannot be disregarded. Severe price fluctuations of raw materials such as steel, change in regulations, semi-conductor chip shortage, geo-political issues, floods, etc. are just a few of the large number of events global supply chains have been recently subject to, even if we were to designate the pandemic as a black swan event. JIT conceptually ignores these uncertainties and hence it is a misfit.

Developed in the 1970s, JIT aligns production closely with demand to optimize inventory. This article examines its challenges this method poses for the automotive industry. This became even more evident during the COVID-19 pandemic. The blof also proposes a better direction for reducing inventory in the system without these negatives.

JIT, OEMs and the Pandemic

JIT inventory management method has shown its limitations during the Pandemic. Read to know more.

JIT, OEMs and the Pandemic | Vector Consulting Group

BAHASA  Read More

Individual targets aim to align efforts with organizational goals, but this leads to a culture of fear, impeding teamwork. The article recommends adopting 'Management by Tactics' (MBT), a strategy focused on choosing and appropriately executing tactics to address the goal of the system.

BAHASA  Management by Tactics

Management by Tactics

Management by Tactics is an effective tool for companies of all sizes. However, while embracing Management by Tactics, companies need to avoid the following pitfalls, read more.

Management by Tactics - Vector Consulting Group

Hi, I am Dr Shashank. I lead Product Development at Imperial Motors, an <a href="https://new-stage.vectorconsulting.in/en/research-and-publication/articles/an-sos-for-the-oems/">Auto OEM</a>. A few years ago, we embarked on a journey of transformation in our new product development department, using the guidelines and philosophy of <a href="https://new-stage.vectorconsulting.in/en/scientific-system-thinking/">Theory of Constraints</a> (TOC). Subsequently, we’ve achieved substantial improvement in both product delivery (a whopping 400% jump in output) and in product launch lead times (40%-50% reduction).&nbsp;The Board members and the top management are now quite pleased with NPD performance. However, my bosses continue to ask me, “Shashank, why are we not able to deliver/launch products on an agreed “launch date”?This is one of the most frequently asked questions in the world of product development for the auto sector. It has been dogging me for over three years. It stumps up even the most seasoned auto experts and consultants because the answer is not very simple.&nbsp;The quest for ‘launch date’ adherence Adhering to the launch date is important to ensure that the new product receives comprehensive marketing preparation when it makes its grand debut. For this, marketing teams must be given plenty of time to build-up for the D-Day. At the same time, developing a new product or new variant is a fairly complex process (refer Figure 1); hence, adhering to launch dates decided a year or two in advance is always a challenge for NPD (New Product Development). This was more so at Imperial Motors where the NPD (New Product Development) process was relatively slow.<figure class="image"><img style="aspect-ratio:4500/4500;" src="https://new-stage-be.vectorconsulting.in/storage/editor_uploads//ot-blog-4-img-1-01-01-01-01_1716877388.png" width="4500" height="4500"></figure>At Imperial, invariably, the launch date would be postponed repeatedly. The famous line from the film ‘Damini’–&nbsp; “tareekh pe tareekh, tareekh pe tareekh ”seems apt here. Finally, towards the fag end of product development cycle, when we see that we are going to miss the date (revised multiple times) again, we would be forced to compromise on scope.&nbsp;Scope compromise is counterproductive. It defeats the very purpose of developing and releasing a new product into the market. Fulfilling the defined scope is more important than adherence to due date (which was anyhow moved around multiple times). The latter ensures that the new product performs as expected and brings financial rewards for the company. However, quite often, many departments at Imperial Motors were forced to compromise on scope. For instance, the research and development team and programme manager could be forced to cancel development of a new colour and deliver the product in the colours already available. We would do this with significant misgivings, wondering if our decision would prove costly for the company.&nbsp;Is lead time reduction the solution? I had always believed that reducing lead times would be the answer to the conflict between meeting deadlines and ensuring scope. Unfortunately, that belief has been proven false. I now see that the two are not necessarily linked. Let me explain how I came to this conclusion:I believed that slow pace of NPD (New Product Development) process at Imperial Motors was at the root cause of the company’s inability to meet launch dates. So, I set out to change this. I made ‘improving speed of auto product development’ my primary objective. With the help of TOC thinking and solutions, we were able to achieve this (Read about solutions elements of TOC that enable drastic reduction of lead time). Wastage of time due to bad multitasking, rework, and interruptions was eliminated. Lead time of projects reduced significantly, and NPD output increased substantially. Consequently, most short-term projects were completed as planned. However, we were still unable to launch new products as per originally scheduled launch dates! The dates were being revised fewer times than before, but they were still being revised.Predicting project completion – a tall task After lead times were reduced as part of the TOC initiative, the company had begun to use the new average <a href="https://new-stage.vectorconsulting.in/en/research-and-publication/articles/de-stressing-npd/">NPD</a> lead times as reference for deciding launch dates. I wondered if the dates were now too aggressive. Speed is important but that alone isn’t enough to ensure that projects are delivered on the expected due date. It is important for us to know upfront when a project will be completed This helps in planning the launch date accordinglyi. So, I added one more objective to my list:<ol><li>Ensure that product development is completed as quickly as possible&nbsp;</li><li>Accurately predict the lead time for product launch&nbsp;</li></ol>Evaluating the objectives, I realised that these two have completely different goals. While objective 1 relates to process deployment for faster execution, objective 2 is about consistency in development lead times for accurately predicting project completion. Undoubtedly, to meet a launch deadline, NPD must be fast and efficient. More importantly, the date has to be set accurately. This is possible if each type of product can be developed in a predictable period of time.&nbsp;To check whether development time was consistent, I tried to understand how ’time’ had behaved in the past new product development cycles. On studying the Imperial Motors’ new product delivery performance of five years, I observed the following:<figure class="image"><img style="aspect-ratio:4500/4500;" src="https://new-stage-be.vectorconsulting.in/storage/editor_uploads//ot-blog-4-img-2-01-01_1716877460.png" width="4500" height="4500"></figure>Based on the table, I drew a few conclusions. Post TOC, lead times had crashed across product categories (objective 1 had been well achieved). However, neither pre-TOC nor post-TOC, lead times were consistent even within a product category. In fact, the lead times were always in a range. If the complexity is higher (new discrete parts, new suppliers, new plant and processes, new technology, new colour, or new materials etc.), the variation in lead times is higher (Refer Figure 2). For instance, lead time for even a small project (like colour refresh) varied between 3 and 4 months (post-TOC). The variability in development time was higher for completely new product categories. What is also important to note here is that even though the highest time taken post TOC for a completely new product development is 24 months, it is not the upper limit of lead time for any new product. Depending on the scope and complexity of the development, lead time of a new project can easily cross this number!&nbsp;<figure class="image"><img style="aspect-ratio:4500/4500;" src="https://new-stage-be.vectorconsulting.in/storage/editor_uploads//ot-blog-4-img-3-01_1716877495.png" width="4500" height="4500"></figure>Inherent uncertainty Although the deployment of TOC flow rules has ensured that tasks are completed very close to ‘touch time’ (without interruption or rework), this touch time cannot be accurately known beforehand. For example, the touch time for release of concept drawings of the same part (a bracket, for instance) required for two different projects of similar category (minor product improvements) can be significantly different.<ul><li>Why do touch times vary?</li><li>What factors in project environments affect touch time of a repetitive task?</li></ul>Any NPD project environment will experience the following:<ol><li>Variability in skill sets of resources working on the same project task (experience vs expertise)/li&gt;</li><li>Uncertainty due to varying complexity of project tasks – many more iterations than expected/planned for/li&gt;</li><li>Expansion in scope of project – Work expands while many conflicts need to be resolved before converging on project scope&nbsp;</li></ol>All these factors can impact task touch times independently as well as cumulatively. It is also seen that project tasks often ‘wait to be attended to’ in few of the functional resource groups (despite WIP control). At times, inputs from internal groups (support groups like Finite Element Analysis, CFD, Patent check etc.) or external groups (like suppliers/vendors, process engineering etc.) take longer than expected (this expands task execution times). These wait times are hidden, and eventually, add to project lead time. To summarise, project tasks experience ‘wait times’ because of&nbsp;<ol><li>Queuing – variability of load vs capacity for a given resource group/li&gt;</li><li>Integration delays due to varying flow rates of multiple functional resource groups/li&gt;</li><li>Variability in natural wait times (inputs take longer time than expected)/li&gt;</li></ol>Apart from the above factors, there are many other elements that also expand lead times. Examples are, variability in quality of task closures (unclean closure of intended scope) leading to rework, use of ‘time’ itself as a resource to negotiate for the cost of parts/tools etc. Such uncertainties are inherent to every project.&nbsp;This means that touch times cannot be predicted. The NPD lead time of any project is only accurately known in hindsight! Way forward I can now answer the question 'Why are we unable to deliver/launch the products on an agreed launch date?’ This happens because NPD lead times are not predictable. So, what’s the way forward?The answer hinges on how we view time in the context of project completion.Can we not estimate time? Of course, we can, if we understand the following:&nbsp;<ul><li>The estimated time is only an estimate. Estimates can change, sometimes drastically (I know this sounds obvious, but believe me, it’s not).</li><li>Attempt to meet milestones (for example, Vehicle Build for design verification) by force-fitting to the estimated timelines can lead to compromises in execution. Efforts can backfire on either the project scope or quality. For example, vehicle assembly can be done with discrete parts managed through R&amp;D proto shop as vendor has not supplied the parts yet.</li></ul>Once we understand and internalise the above, we can get to the business of trying to find the way to get the ‘best estimate’ (Please note that 'best’ is still an estimate). As a starting point, let’s look at Table 1 that has lead times achieved post TOC implementation. To build on this, I have grouped product categories to evaluate impact of each parameter.&nbsp;<figure class="image"><img style="aspect-ratio:4500/4500;" src="https://new-stage-be.vectorconsulting.in/storage/editor_uploads//ot-blog-4-img-4-01-01-01-01_1716877588.png" width="4500" height="4500"></figure>This implies that variability of time is minimal and predictable when agreement on scope has fewer challenges e.g., serial nos. 1 &amp; 2. In case of serial nos. 3 &amp; 4, where converging on scope requires multiple iterations (across program, design and engineering, marketing and so on) which cannot be pre-determined, trying to adhere to launch date (even a range) is akin to chasing a mirage. Hence, when these products are in concept phase, the ONLY option is to focus on faster execution (objective 1) instead of trying to arrive at some ‘accurate estimate’ of launch date (objective 2). This can be achieved by<ol><li>Creating visibility on conflicts and iterations – keep checking speed of convergence/li&gt;</li><li>Pro-actively cutting Parkinson (expansion of time) by high frequency observations and decision making/li&gt;</li><li>Assessing the impact of the above two on short-term expectations (vehicle layout completion, mock-up build etc.)</li></ol>In the concept phase, when convergence on scope takes too long and a solution doesn’t evolve despite multiple iterations, product viability/feasibility may be challenged. On the other hand, only scope finalisation in terms of initial vehicle layout/digital mock-up doesn’t guarantee product viability unless product is physically seen and undergoes one round of quality check (exhaustive vehicle testing). Hence, the process to arrive at the best estimate of launch date (range) for serial nos. 3&amp;4 (major product improvements and new product) is as per figure 3 and table 3.&nbsp;<figure class="image"><img style="aspect-ratio:4500/4500;" src="https://new-stage-be.vectorconsulting.in/storage/editor_uploads//ot-blog-4-img-5-01-01-01_1716877653.png" width="4500" height="4500"></figure><figure class="image"><img style="aspect-ratio:4500/4500;" src="https://new-stage-be.vectorconsulting.in/storage/editor_uploads//ot-blog-4-img-6-01-01-01_1716877699.png" width="4500" height="4500"></figure>&nbsp;<figure class="image"><img style="aspect-ratio:4500/4500;" src="https://new-stage-be.vectorconsulting.in/storage/editor_uploads//ot-blog-4-img-6_1716877751.png" width="4500" height="4500"></figure>Final word Lead times of specific complex NPD projects can be known accurately only in hindsight. In foresight, only rough estimates are possible. However, in annual business planning (for major product improvements and new products), companies can estimate launches based on actual status of product development (phase the product is in). The year and specific quarter within a financial year for the launch can be arrived at using the above guidelines.&nbsp;End Notes and References ⅰSometimes launch dates come as ‘diktats’ from the top. However, if lead times could be known upfront and are seen to be short, the team can take necessary steps like hiring extra resources.

BAHASA Read More

In auto NPD, lead times can be reduced; however, predicting them accurately is difficult. The way forward involves understanding that time estimates are subject to change and focusing on achieving the best possible estimate based on the current phase of product development.

On-time delivery, a misnomer in New Product Development

Meeting New product Development expectations and delivery requirements can sometimes prove to be challenging. Know how to handle such requirements here.

On-Time Delivery, a misnomer in New Product Development - Vector Consulting Group

BAHASA Industries like textiles, steel and pharma face wandering bottlenecks, which can lead to piled-up loads, either upstream or downstream of the critical constraint resource (CCR), reducing the system reliability. The solution lies in identifying and managing real-time CCRs to prevent emergent bottlenecks.

BAHASA Wandering bottleneck

Industries like textiles, steel and pharma face wandering bottlenecks, which can lead to piled-up loads, either upstream or downstream of the critical constraint resource (CCR), reducing the system reliability. The solution lies in identifying and managing real-time CCRs to prevent emergent bottlenecks.

Wandering bottleneck

A few steps both in planning and excecution can help in effectively managing wandering bottle necks on the shop floor. Read more

Wandering bottleneck - Vector Consulting Group

We see the world not as it is but as we are – or as we are conditioned to see it. - Stephen R Covey.&nbsp;It’s well understood that our prejudices colour our views. Perception interferes with objectivity. Behavioural scientists have come to conclude that we see what we believe more often than we believe what we see! Organisations have to contend with this problem every day even as they strive to mobilise the energies of a diverse set of people to achieve a common goal.To solve this issue of differentiating between truth and perception, managers tend to rely on data. This faith in data is deep-set, as evidenced by the overused line, “In God we trust, and for everything else, get the data.” Unfortunately, data does not help differentiate between truth and perception. For instance, two managers might have two different interpretations of the same data, or two opposing views can have fully reliable data supporting them.&nbsp;So, data, at best, helps validate the existence of an entity, but decisions are made based on the inferences (or deductions) from the data created in the minds of the managers. To illustrate this, let’s look at the specific issue of poor deliveries by a vendor, a common complaint of plant managers at manufacturing companies. It is very likely that an attempt to zero down on the root cause of this problem leads a manager to conclude that the root cause is the vendor’s “bad attitude”. Consequently, the manager may proceed with decisions and actions like counselling the vendor or even replacing him. What is important to note here is that while there is data to support the fact that deliveries were late, the conclusion is purely an inference which may or may not be accurate. There isn’t much by way of figures or numbers to evidence the “bad attitude”, so the decision was very likely coloured by the manager’s previous experiences either with this or other vendors!&nbsp;Why are many human decisions thusly, often led by perception-based inferences? Behavioural scientists have pointed out that we can do two kinds of thinking as a species - slow and fast. We are capable of slow, rigorous analytical evaluation. However, since the human species has often had to act quickly in the face of danger, we have also developed the ability to make quick decisions relying on intuition and heuristics. The latter method is obviously not very logical and subject to bias but is sufficient &nbsp;to address immediate dangers.&nbsp;While such urgent situations and dangers in our environment may have come down, human beings, during decision-making, often prefer taking the ‘lazy’, shorter route to conclusions -hopping on biases and leaping off past experiences. A decision made in a rush can have long-standing consequences for organisations. So, &nbsp;managers must be trained to use a more analytical approach to decision-making. For this, managers must escape the trap of erroneous mental models and biases. The good news is that our minds can be trained to do so if we keep three axioms in mind while analysing problems:&nbsp;1. Organizations are interconnected systemsIn an organisation, issues that crop up are rarely isolated incidents or problems. They may not even have their roots in the same department. Therefore, true power for a decision maker in this context lies in the ability to appreciate the complete pattern of interlinkages in the organisation and the cause-effect connections between its myriad parts. The problem of being able to grasp interconnections becomes even more difficult when cause and effect form non-linear loops, especially when the two are separated by time and space. For example, when a cause in one department has an impact on &nbsp;another, even after a considerable amount of time, the connection between the two can remain undetected.&nbsp;This knowledge of being able to see interconnections in and of itself is far more valuable than intimate knowledge of the many individual parts of a system. Managers, however, are trained to be specialists in their respective departments and areas of work. Moreover, as bosses of their domains, teams look up to them to rule and protect their turf, to shield them from blame. This culture of protecting self-interest compartmentalises the managers’ thinking and clouds their view of the big picture.&nbsp;<figure class="image"><img style="aspect-ratio:4500/4500;" src="https://new-stage-be.vectorconsulting.in/storage/editor_uploads//gb-blog-8-img-1-01-01-01-01_1716883166.png" width="4500" height="4500"></figure>This failure of managers to take a systemic view can be a major hurdle to good decision-making and can hurt not only the department but the whole organisation. So, a simple and effective way to train the mind is to use computer simulators– these can help collapse time and space dimensions (a real life day can be modeled as a second in computer time and a manager on a simulator can visualise the effects spanning across departments) to make managers aware of the non-linear loops of cause and effect across departments.&nbsp;2. Along with data, always comes noiseThe other aspect which interferes with clear thinking is ‘noise’. While you need information to make decisions, differentiating between relevant information and irrelevant noise is difficult. Sorting between the two is more complicated because our perception can amplify noise and make it seem relevant based on circumstances. Here’s an example, when you’re running late for a meeting and are slowed down by traffic, you are likely to notice every stop sign and every bad, congested patch on the road. And when you reach the meeting, you will complain about the horrid conditions of the roads. However, had you started early, you would have paid scant attention to these minor obstacles along the way.&nbsp;Similarly, managers struggling with poor <a href="https://new-stage.vectorconsulting.in/en/research-and-publication/blogs/on-time-delivery-a-misnomer-in-new-product-development-2/">on-time delivery performance</a> could perceive routine issues such as quality problems, absenteeism, machine breakdowns and so on as major roadblocks. When orders are running late, every ‘bump on the road’ created by quality issues, or a machine breakdown can suddenly loom larger. Distracted into trying to solve these noise-level issues can lead to the real problems being ignored – long queues of raw material waiting to be processed and the consequent wastage of buffers. The longer the wait at upstream work centres, shorter the time available to deal with natural uncertainties in the downstream work centres. It is not that managers are unaware of the problems with waiting time but when making decisions, the real issue often gets drowned out by the amplified noise of the minor ones.&nbsp;3. Falsification, not verification, is the true test of a hypothesisTaking the system view allows you to develop hypotheses which span across space and time. Being wary of ‘noise’ can help sort between relevant and irrelevant information that can go into these hypotheses. However, it is also important to be aware of confirmation bias while testing these hypotheses. The best way to avoid it is to take the approach of falsification. The falsification method involves identifying any contrary observation to prove a hypothesis wrong. This is because even if there are many observations that support, this cannot prove a hypothesis right. So, the best way to check reality is to subject it to tests of failure. If a hypothesis has survived many tests of failure, it can be assumed to be suitable for the time being. As managers, we must develop the ability to attempt and disprove our hypothesis rather than trying to confirm it.&nbsp;<img src="https://images.vectorconsulting.in/storage/editor_uploads//image_1700393785.png" width="503" height="263">If managers can keep these three axioms in mind when it comes to decision-making, they can anticipate and avoid traps of incorrect causality, amplified noise and confirmation bias. Without appreciating and &nbsp;these principles, data alone can be, at best, a crutch and, at worst, a handicap while trying to decipher between reality and perception. With the right management consulting training to think methodically and analytically, organisations can harness the wealth of intuition in their managers and use available data to provide the right signals.&nbsp;&nbsp;It is time companies invested in Thoughtware.

This insightful article reveals how biases and preconceived notions often colour managerial judgments, overshadowing objective data analysis. It also suggests three steps for more effective decision-making.

Going beyond data for decision-making

We see the world not as it is but as we are – or as we are conditioned to see it. - Stephen R Covey.&nbsp;It’s well understood that our prejudices colour our views. Perception interferes with objectivity. Behavioural scientists have come to conclude that we see what we believe more often than we believe what we see! Organisations have to contend with this problem every day even as they strive to mobilise the energies of a diverse set of people to achieve a common goal.To solve this issue of differentiating between truth and perception, managers tend to rely on data. This faith in data is deep-set, as evidenced by the overused line, “In God we trust, and for everything else, get the data.” Unfortunately, data does not help differentiate between truth and perception. For instance, two managers might have two different interpretations of the same data, or two opposing views can have fully reliable data supporting them.&nbsp;So, data, at best, helps validate the existence of an entity, but decisions are made based on the inferences (or deductions) from the data created in the minds of the managers. To illustrate this, let’s look at the specific issue of poor deliveries by a vendor, a common complaint of plant managers at manufacturing companies. It is very likely that an attempt to zero down on the root cause of this problem leads a manager to conclude that the root cause is the vendor’s “bad attitude”. Consequently, the manager may proceed with decisions and actions like counselling the vendor or even replacing him. What is important to note here is that while there is data to support the fact that deliveries were late, the conclusion is purely an inference which may or may not be accurate. There isn’t much by way of figures or numbers to evidence the “bad attitude”, so the decision was very likely coloured by the manager’s previous experiences either with this or other vendors!&nbsp;Why are many human decisions thusly, often led by perception-based inferences? Behavioural scientists have pointed out that we can do two kinds of thinking as a species - slow and fast. We are capable of slow, rigorous analytical evaluation. However, since the human species has often had to act quickly in the face of danger, we have also developed the ability to make quick decisions relying on intuition and heuristics. The latter method is obviously not very logical and subject to bias but is sufficient &nbsp;to address immediate dangers.&nbsp;While such urgent situations and dangers in our environment may have come down, human beings, during decision-making, often prefer taking the ‘lazy’, shorter route to conclusions -hopping on biases and leaping off past experiences. A decision made in a rush can have long-standing consequences for organisations. So, &nbsp;managers must be trained to use a more analytical approach to decision-making. For this, managers must escape the trap of erroneous mental models and biases. The good news is that our minds can be trained to do so if we keep three axioms in mind while analysing problems:&nbsp;1. Organizations are interconnected systemsIn an organisation, issues that crop up are rarely isolated incidents or problems. They may not even have their roots in the same department. Therefore, true power for a decision maker in this context lies in the ability to appreciate the complete pattern of interlinkages in the organisation and the cause-effect connections between its myriad parts. The problem of being able to grasp interconnections becomes even more difficult when cause and effect form non-linear loops, especially when the two are separated by time and space. For example, when a cause in one department has an impact on &nbsp;another, even after a considerable amount of time, the connection between the two can remain undetected.&nbsp;This knowledge of being able to see interconnections in and of itself is far more valuable than intimate knowledge of the many individual parts of a system. Managers, however, are trained to be specialists in their respective departments and areas of work. Moreover, as bosses of their domains, teams look up to them to rule and protect their turf, to shield them from blame. This culture of protecting self-interest compartmentalises the managers’ thinking and clouds their view of the big picture.&nbsp;<figure class="image"><img style="aspect-ratio:4500/4500;" src="https://new-stage-be.vectorconsulting.in/storage/editor_uploads//gb-blog-8-img-1-01-01-01-01_1716883166.png" width="4500" height="4500"></figure>This failure of managers to take a systemic view can be a major hurdle to good decision-making and can hurt not only the department but the whole organisation. So, a simple and effective way to train the mind is to use computer simulators– these can help collapse time and space dimensions (a real life day can be modeled as a second in computer time and a manager on a simulator can visualise the effects spanning across departments) to make managers aware of the non-linear loops of cause and effect across departments.&nbsp;2. Along with data, always comes noiseThe other aspect which interferes with clear thinking is ‘noise’. While you need information to make decisions, differentiating between relevant information and irrelevant noise is difficult. Sorting between the two is more complicated because our perception can amplify noise and make it seem relevant based on circumstances. Here’s an example, when you’re running late for a meeting and are slowed down by traffic, you are likely to notice every stop sign and every bad, congested patch on the road. And when you reach the meeting, you will complain about the horrid conditions of the roads. However, had you started early, you would have paid scant attention to these minor obstacles along the way.&nbsp;Similarly, managers struggling with poor <a href="https://new-stage.vectorconsulting.in/en/research-and-publication/blogs/on-time-delivery-a-misnomer-in-new-product-development-2/">on-time delivery performance</a> could perceive routine issues such as quality problems, absenteeism, machine breakdowns and so on as major roadblocks. When orders are running late, every ‘bump on the road’ created by quality issues, or a machine breakdown can suddenly loom larger. Distracted into trying to solve these noise-level issues can lead to the real problems being ignored – long queues of raw material waiting to be processed and the consequent wastage of buffers. The longer the wait at upstream work centres, shorter the time available to deal with natural uncertainties in the downstream work centres. It is not that managers are unaware of the problems with waiting time but when making decisions, the real issue often gets drowned out by the amplified noise of the minor ones.&nbsp;3. Falsification, not verification, is the true test of a hypothesisTaking the system view allows you to develop hypotheses which span across space and time. Being wary of ‘noise’ can help sort between relevant and irrelevant information that can go into these hypotheses. However, it is also important to be aware of confirmation bias while testing these hypotheses. The best way to avoid it is to take the approach of falsification. The falsification method involves identifying any contrary observation to prove a hypothesis wrong. This is because even if there are many observations that support, this cannot prove a hypothesis right. So, the best way to check reality is to subject it to tests of failure. If a hypothesis has survived many tests of failure, it can be assumed to be suitable for the time being. As managers, we must develop the ability to attempt and disprove our hypothesis rather than trying to confirm it.&nbsp;<img src="https://images.vectorconsulting.in/storage/editor_uploads//image_1700393785.png">If managers can keep these three axioms in mind when it comes to decision-making, they can anticipate and avoid traps of incorrect causality, amplified noise and confirmation bias. Without appreciating and &nbsp;these principles, data alone can be, at best, a crutch and, at worst, a handicap while trying to decipher between reality and perception. With the right management consulting training to think methodically and analytically, organisations can harness the wealth of intuition in their managers and use available data to provide the right signals.&nbsp;&nbsp;It is time companies invested in Thoughtware.

Keep mentioned three axioms in mind when it comes to decision making, they can anticipate and avoid traps of incorrect causality, amplified noise and confirmation bias. Know more.

Going beyond data for decision-making - Vector Consulting Group

Running too many initiatives simultaneously may be counter-productive.A reality check:It is not uncommon for companies to have multiple improvement initiatives/transformation programs underway at the same time. In fact, this is considered a good sign. It shows that these companies are forward-looking and raring to grow. However, these progressive initiatives might not be telling the whole story of whether the companies are truly poised for growth.&nbsp;Scratch the surface, and you might find that key people in the company spend their days running from meeting to meeting, catching up on the progress of each of these initiatives. When not in meetings, they are preparing for yet another initiative. Their bandwidth is used up by reviews, leaving little time for ‘actual work’.&nbsp;Each initiative brings along an entourage of mentors – consultants, academicians, IT experts, motivational speakers and so on. Each, daily, takes up precious time of the key people in the company. Much data is generated for their discussions. This data is circulated in many formats and presented on multiple platforms. As the men and women juggle the many appointments, emails, presentations, and numerous meetings, they might wonder, “Which part of all this really matters to me or would help me?” And that might, eventually, lead to the question, “Why so many transformation programs?”&nbsp;The top management’s answer could be one of the following.<ol><li>The company is going through a lean phase and is not as busy as it tends to be, and it wants to make the most of it before the next wave of growth comes.&nbsp;</li><li>Competitors are implementing an initiative. Company has ‘Fear Of Missing Out’.&nbsp;</li><li>The transformation program would be a stepping-stone for ‘Future Owners’ to get started in the company.&nbsp;</li><li>They are recommended by a mentor/strategist with whom the company has a long-term engagement.&nbsp;</li></ol>The untold storySadly, these initiatives running in companies could be cannibalising each other unbeknownst to the people running them. Take what happened in one such company, for example.&nbsp;A company-wide cost reduction program was initiated when it was found that the sales team’s travel allowance was 20% higher than that of their competitor. The allowance was slashed by 30% even as inflation was at an all-time high. In a few months, sales took a sudden dip. In response, the company hired a motivational speaker to boost the sales force’s morale. As may be guessed, the speaker’s visits did little about the flagging sales. That’s because the sales team’s significant needs were not being met. No amount of motivation could help. It goes to show that efforts made in multiple directions may not lead to any gain.&nbsp;In fact, often, the top management is left wondering why not even a single initiative is yielding the desired results! That makes them start planning another initiative to pull them out of this mess. The loop continues. Interestingly, since most companies can’t see this vicious loop, the question of why initiatives don’t deliver expected results remains a puzzle.&nbsp;<img src="https://images.vectorconsulting.in/storage/editor_uploads//image_1700393953.png" width="588" height="408">The missing pieces of the puzzle:An organisation, in its simplest form, is an interconnected system of functions. When a problem arises, we seek a local solution by seeing the problem in isolation. Traditional management methods try to break a problem into smaller, simpler parts. And then deploy multiple initiatives to solve each of those parts. Simplicity, is not about seeing the problem in parts but as a whole– a system approach to solving problems.&nbsp;When work in one department can impact work in another department, then it is possible that there aren’t multiple problems but a single problem at play. The multitude of problems could be nothing but symptoms of the underlying problem – the root cause! Until this root cause is caught and arrested, the problems (or symptoms) will continue to exist. That is why despite companies deploying multiple initiatives to address these symptoms, they find that either these initiatives don’t yield results, or that the problems resurface after some time.&nbsp;Any organisation can run only so many improvement initiatives at a time. Like ‘Risk Appetite’ organisations have ‘Transformation Appetite’ which is the quantum of transformational initiatives that a company can not only manage but also sustain. Sustained transformations are those wherein the new way of working becomes a habit, becomes second nature and gets ingrained in the DNA of the company.&nbsp;Considering these two facets of systems thinking and transformation appetite, it would make sense to have as few initiatives as possible and to focus on the root cause.&nbsp;“A chain is only as strong as its weakest link,” goes a popular saying. An initiative can be strongly followed in true spirit and be sustainable – provided that every individual in the company believes and follows the SOPs in true spirit. But this is not always the case. As these initiatives are often driven by the top management, they may not be followed with the right rigour by the people lower in the hierarchy. This is because companies do not invest in educating and obtaining the buy-in of all their employees. That is why few companies enjoy success even after adopting Lean, Six Sigma or TOC.&nbsp;<img src="https://images.vectorconsulting.in/storage/editor_uploads//image_1700394012.png" width="588" height="408">A structured approach:So, until we connect the pieces of this puzzle, the problem of poor initiatives results can never be solved. What would help is a structured, step-by-step approach to ’inducting’ a transformative process into the organisation.&nbsp;Step: 1 – PrioritiseIt is very important to prioritise transformational programs- not only in terms of which ones are more important but also in the sequence in which implementation should be done. As a company it makes sense to ‘focus’ on one initiative at any point of time to bring that initiative to fruition. By ‘focus’, we mean that the company may have to let go of various other potential initiatives under consideration or do them later.&nbsp;To decide on which one is the right initiative to focus on, it makes sense to check each initiative and answer the question, “What problem are we trying to solve with this?”. Once that is clear, it is also important to assess whether the problem we are trying to solve addresses a root cause or a symptom. If it is not addressing the root cause, we will be wasting our efforts by merely trying to address the situation at a superficial level. Always prioritise initiatives that address the root problems first. Other initiatives may be needed to ensure continuous improvements, but those will follow.&nbsp;Step: 2 – SynchroniseWhen an organisation decides to pursue multiple initiatives, it is necessary to consider whether any action taken for one initiative is a ‘Repetitive Action’ or whether it is conflicting with another. Sometimes, a single initiative can requiretwo conflicting actions. In such cases, the situation may deteriorate rather than improve.&nbsp;Step: 3 – ChanneliseOnce the initiatives are finalised, and the issues of synchronisation are resolved, the organisation must channel its entire attention towards implementing the Transformation Program with full vigour. While doing so, Top Management must look out for lead indicators to ensure that the actions are giving the desired results.&nbsp;A Word of CautionOrganisations are driven by various factors to be at the prime of their performance, be it the market share or the share market. The rat race to be at the top forces companies to take desperate measures of starting various initiatives simultaneously– with the shimmering hope that the organization would come out on top in no time. Then they are frustrated when the company goes nowhere! Try taking one step at a time and see how soon you get to the top – after all, as the proverbial saying goes- “Rome was not built in a day”.&nbsp;

Organizations launch multiple initiatives to continously improve performance. However, key staff get overwhelmed with these and end up spending more time in meetings than working. The involvement of many mentors and experts, though meant to help, just increases complexity. Here is a way out.

Too many cooks spoil the... growth!

Running too many initiatives simultaneously may be counter-productive.A reality check:It is not uncommon for companies to have multiple improvement initiatives/transformation programs underway at the same time. In fact, this is considered a good sign. It shows that these companies are forward-looking and raring to grow. However, these progressive initiatives might not be telling the whole story of whether the companies are truly poised for growth.&nbsp;Scratch the surface, and you might find that key people in the company spend their days running from meeting to meeting, catching up on the progress of each of these initiatives. When not in meetings, they are preparing for yet another initiative. Their bandwidth is used up by reviews, leaving little time for ‘actual work’.&nbsp;Each initiative brings along an entourage of mentors – consultants, academicians, IT experts, motivational speakers and so on. Each, daily, takes up precious time of the key people in the company. Much data is generated for their discussions. This data is circulated in many formats and presented on multiple platforms. As the men and women juggle the many appointments, emails, presentations, and numerous meetings, they might wonder, “Which part of all this really matters to me or would help me?” And that might, eventually, lead to the question, “Why so many transformation programs?”&nbsp;The top management’s answer could be one of the following.<ol><li>The company is going through a lean phase and is not as busy as it tends to be, and it wants to make the most of it before the next wave of growth comes.&nbsp;</li><li>Competitors are implementing an initiative. Company has ‘Fear Of Missing Out’.&nbsp;</li><li>The transformation program would be a stepping-stone for ‘Future Owners’ to get started in the company.&nbsp;</li><li>They are recommended by a mentor/strategist with whom the company has a long-term engagement.&nbsp;</li></ol>The untold storySadly, these initiatives running in companies could be cannibalising each other unbeknownst to the people running them. Take what happened in one such company, for example.&nbsp;A company-wide cost reduction program was initiated when it was found that the sales team’s travel allowance was 20% higher than that of their competitor. The allowance was slashed by 30% even as inflation was at an all-time high. In a few months, sales took a sudden dip. In response, the company hired a motivational speaker to boost the sales force’s morale. As may be guessed, the speaker’s visits did little about the flagging sales. That’s because the sales team’s significant needs were not being met. No amount of motivation could help. It goes to show that efforts made in multiple directions may not lead to any gain.&nbsp;In fact, often, the top management is left wondering why not even a single initiative is yielding the desired results! That makes them start planning another initiative to pull them out of this mess. The loop continues. Interestingly, since most companies can’t see this vicious loop, the question of why initiatives don’t deliver expected results remains a puzzle.&nbsp;<img src="https://images.vectorconsulting.in/storage/editor_uploads//image_1700393953.png">The missing pieces of the puzzle:An organisation, in its simplest form, is an interconnected system of functions. When a problem arises, we seek a local solution by seeing the problem in isolation. Traditional management methods try to break a problem into smaller, simpler parts. And then deploy multiple initiatives to solve each of those parts. Simplicity, is not about seeing the problem in parts but as a whole– a system approach to solving problems.&nbsp;When work in one department can impact work in another department, then it is possible that there aren’t multiple problems but a single problem at play. The multitude of problems could be nothing but symptoms of the underlying problem – the root cause! Until this root cause is caught and arrested, the problems (or symptoms) will continue to exist. That is why despite companies deploying multiple initiatives to address these symptoms, they find that either these initiatives don’t yield results, or that the problems resurface after some time.&nbsp;Any organisation can run only so many improvement initiatives at a time. Like ‘Risk Appetite’ organisations have ‘Transformation Appetite’ which is the quantum of transformational initiatives that a company can not only manage but also sustain. Sustained transformations are those wherein the new way of working becomes a habit, becomes second nature and gets ingrained in the DNA of the company.&nbsp;Considering these two facets of systems thinking and transformation appetite, it would make sense to have as few initiatives as possible and to focus on the root cause.&nbsp;“A chain is only as strong as its weakest link,” goes a popular saying. An initiative can be strongly followed in true spirit and be sustainable – provided that every individual in the company believes and follows the SOPs in true spirit. But this is not always the case. As these initiatives are often driven by the top management, they may not be followed with the right rigour by the people lower in the hierarchy. This is because companies do not invest in educating and obtaining the buy-in of all their employees. That is why few companies enjoy success even after adopting Lean, Six Sigma or TOC.&nbsp;<img src="https://images.vectorconsulting.in/storage/editor_uploads//image_1700394012.png">A structured approach:So, until we connect the pieces of this puzzle, the problem of poor initiatives results can never be solved. What would help is a structured, step-by-step approach to ’inducting’ a transformative process into the organisation.&nbsp;Step: 1 – PrioritiseIt is very important to prioritise transformational programs- not only in terms of which ones are more important but also in the sequence in which implementation should be done. As a company it makes sense to ‘focus’ on one initiative at any point of time to bring that initiative to fruition. By ‘focus’, we mean that the company may have to let go of various other potential initiatives under consideration or do them later.&nbsp;To decide on which one is the right initiative to focus on, it makes sense to check each initiative and answer the question, “What problem are we trying to solve with this?”. Once that is clear, it is also important to assess whether the problem we are trying to solve addresses a root cause or a symptom. If it is not addressing the root cause, we will be wasting our efforts by merely trying to address the situation at a superficial level. Always prioritise initiatives that address the root problems first. Other initiatives may be needed to ensure continuous improvements, but those will follow.&nbsp;Step: 2 – SynchroniseWhen an organisation decides to pursue multiple initiatives, it is necessary to consider whether any action taken for one initiative is a ‘Repetitive Action’ or whether it is conflicting with another. Sometimes, a single initiative can requiretwo conflicting actions. In such cases, the situation may deteriorate rather than improve.&nbsp;Step: 3 – ChanneliseOnce the initiatives are finalised, and the issues of synchronisation are resolved, the organisation must channel its entire attention towards implementing the Transformation Program with full vigour. While doing so, Top Management must look out for lead indicators to ensure that the actions are giving the desired results.&nbsp;A Word of CautionOrganisations are driven by various factors to be at the prime of their performance, be it the market share or the share market. The rat race to be at the top forces companies to take desperate measures of starting various initiatives simultaneously– with the shimmering hope that the organization would come out on top in no time. Then they are frustrated when the company goes nowhere! Try taking one step at a time and see how soon you get to the top – after all, as the proverbial saying goes- “Rome was not built in a day”.&nbsp;

How to structure an effective change management process in organizations. Read to know more.

Too many cooks spoil the ..growth! - Vector Consulting Group

Organization culture is mostly shaped by the way its leaders interact with the people in that organization. At times, we come across leaders, who believe that the company’s most chronic problems – stagnant sales, delayed projects or inefficiency in production or quality issues – exist only because people aren’t good enough. They blame managers reporting to them, see them as incompetent, lacking motivation and commitment. They have very little trust in their subordinates, and they are authoritative. They maintain that managing an organization is all about managing the people in it. Hence, they deploy “people-driven” tactics to control them. Here are a few examples.<ul><li>Frequently shifting roles and responsibilities of people</li><li>Firing key senior managers and hiring new ones regularly</li><li>Personally controlling every aspect of decision-making; very minimal delegation</li><li>If the organization is very big, they use a coterie of personal favorites to keep watch over others and to overrule decisions of others.</li></ul>Resultant impact is an environment of “find-the-culprit” way of analyzing issues. In such an environment, everyone tries to be a survivor. All actions that people take are only with the purpose of securing their place in the organization. When key managers operate based on principles of self-preservation, additional issues crop up. This perpetuates a toxic work culture.Information OpacityIn such environments, feedback of managers is usually filtered to “save” or “promote” someone, and as a result, leaders don’t get to know the real issue at hand for a long period, despite having many interactions. Data is tailored to suit the hypothesis of individual managers. When leaders eventually discover the real issue, they feel cheated, which in turn further reinforces the belief that managers are bad and cannot be trusted.Low management bandwidthManagers then become focused on avoiding blame. Despite a formal structure, they tend to pass on decision making to the leaders. Consequently, the leaders’ bandwidth is stretched. And Company’s decision making process slows down.Instinctive yet bad solutionsIn such environments, leaders become delusional about their own intelligence, especially when they are surrounded by managers who feed them unrealistic amounts of positive feedback. Leaders become instinctive in decision making. Instinctive decisions without critical feedback, tend to be half-baked, and eventually cause damage over a period of time. Reversal of wrong decisions becomes difficult as critical feedback does not reach the leaders.There may be other decision biases in play as well. Filtered/limited information can create availability bias which prompts leaders to draw a conclusion based on available information. . Also, once they have some initial information, they tend to filter all subsequent information through that early data. This is called anchoring bias which can also hamper the decision-making process.Low on InnovationAn environment of blame is unfavorable for innovation. Fear of failure encourages people to maintain status-quo and avoid experimentation. The only exception is made for ideas originating from the leaders themselves!Organizations have various levels of this toxic work culture depending on their leadership styles – some are on the extreme, while others suffer from it fleetingly.The question before us is ‘why do such leadership styles thrive in organizations?’. This cannot be answered by putting the blame on specific leaders and their styles. We will not gain any knowledge by doing so.Cognitive bias and Leadership styleLeaders, like normal human beings, suffer from cognitive biases. One such bias, is Attribution Bias, which refers to the systematic errors made when people evaluate or try to find reasons for their own and others’ behavior/performance.When it comes to undesirable behaviour &nbsp;from others, we tend to attribute it to personality traits of the person. &nbsp;In contrast, when it comes to explaining our own behaviour, &nbsp;we attribute it to systemic conditions, our skills or intelligence, depending on outcomes. When desired results come in, we credit our actions and intelligence. Undesirable results are blamed on unavoidable circumstances. (That reckless driver who skipped the signal is a jerk! But when I did it, it was because I had to rush for an appointment with the doctor. The improved returns in the stock portfolio are because of my decisions while the dip was because of harsh market).&nbsp;Leaders, occupying a position of power over others, receive minimal critical feedback. Hence, they unknowingly suffer severely from attribution cognitive bias.&nbsp;Acknowledgement of BiasUnfortunately, unlike computer programs, the bugs in the form of biases in human cognition cannot be removed. One can only be consciously aware and willing to live with good enough workarounds to prevent its damaging effect.&nbsp;Critical feedback can help leaders become aware of atttribution bias. This is hard to extract from one’s subordinate. So, the only option is to do an experiment on your own, a simple thought experiment.&nbsp;Make two lists of people by asking the following questions.“Who were your favorites in the past? Who are they in the present?”If the lists are different, most likely you are suffering from attribution error. Your own judgement about people cannot be subjected to any repeatable test. Hence, by definition, it cannot be taken seriously.A template to overcome Attribution Bias, once one is aware of the bias and then willing to self-impose a workaround, is possible.&nbsp;The way to overcome &nbsp;attribution bias is by putting in four transparent rules, which will force one to circumvent the bias.&nbsp;<ul><li>Do not blame: &nbsp;When analysing &nbsp;any issue for causality, no one is allowed to blame any manager. If there is a temptation to attribute the undesired behaviour &nbsp;of an individual manager as the cause, the analysis must go deeper to understand why the manager behaved the way he/she &nbsp;did. This way of thinking may force one to look into policies or procedures or formal responsibilities that lead to the behaviour .&nbsp;</li><li>Look for underlying conflict: If issues are chronic and repetitive, assume that there is an underlying deeper conflict in the organisation. . A top-of-the-mind solution for the issue at hand will usually have a severe negative impact on some other aspect of business. Unless the competing needs are understood, no decision should be taken. Sales growth can conflict with inventory reduction needs or margin improvement goals. Quality goals can come in the way of production efficiency improvement. Speeding up a project can come in the way of completing the entire scope.&nbsp;</li><li>Find ways to meet competing needs: The pressure of competing needs is a big reason for a standstill in decision-making. One needs a breakthrough approach to develop solutions which meet both competing needs. Without that, delays in decisions are inevitable. For example, sales growth and inventory reduction can be achieved by finding a way to reduce supply lead time. Quality goals can improve while enhancing efficiency by ensuring quality parameters at source.&nbsp;</li><li>Do not assume people are ‘lazy’: If a manager is not completing tasks assigned to him, most likely he is busy with something else that he/she thinks is more urgent.</li></ul>Leaders should formalise &nbsp;and widely announce these four rules in the organisation. &nbsp;This will exert collective pressure on them (and others) to adhere to them.&nbsp;Good usage of the above “rules” will lead to the following changes<ul><li>Leaders will talk less and listen</li><li>They will master the art of asking critical questions to uncover real issues.</li><li>Soon, they will facilitate decision-making rather than making all the decisions.</li></ul>Once leaders begin following the above practices, the culture of the organisation &nbsp;will undergo a transformation. Transparency will improve as bad news moves up faster. Decisions will be speedier as managers will no longer take refuge in defensive behaviour. &nbsp;More importantly, managers will not worry about failing – a critical requirement for an innovative organisation. &nbsp;

Leadership styles significantly influence organizational culture, often leading to a toxic blame game. This situation is often rooted in leaders' cognitive biases. The article suggests four clear rules to combat these biases and enhance workplace culture.

Leadership styles and organization culture

Organization culture is mostly shaped by the way its leaders interact with the people in that organization. At times, we come across leaders, who believe that the company’s most chronic problems – stagnant sales, delayed projects or inefficiency in production or quality issues – exist only because people aren’t good enough. They blame managers reporting to them, see them as incompetent, lacking motivation and commitment. They have very little trust in their subordinates, and they are authoritative. They maintain that managing an organization is all about managing the people in it. Hence, they deploy “people-driven” tactics to control them. Here are a few examples.<ul><li>Frequently shifting roles and responsibilities of people</li><li>Firing key senior managers and hiring new ones regularly</li><li>Personally controlling every aspect of decision-making; very minimal delegation</li><li>If the organization is very big, they use a coterie of personal favorites to keep watch over others and to overrule decisions of others.</li></ul>Resultant impact is an environment of “find-the-culprit” way of analyzing issues. In such an environment, everyone tries to be a survivor. All actions that people take are only with the purpose of securing their place in the organization. When key managers operate based on principles of self-preservation, additional issues crop up. This perpetuates a toxic work culture.Information OpacityIn such environments, feedback of managers is usually filtered to “save” or “promote” someone, and as a result, leaders don’t get to know the real issue at hand for a long period, despite having many interactions. Data is tailored to suit the hypothesis of individual managers. When leaders eventually discover the real issue, they feel cheated, which in turn further reinforces the belief that managers are bad and cannot be trusted.Low management bandwidthManagers then become focused on avoiding blame. Despite a formal structure, they tend to pass on decision making to the leaders. Consequently, the leaders’ bandwidth is stretched. And Company’s decision making process slows down.Instinctive yet bad solutionsIn such environments, leaders become delusional about their own intelligence, especially when they are surrounded by managers who feed them unrealistic amounts of positive feedback. Leaders become instinctive in decision making. Instinctive decisions without critical feedback, tend to be half-baked, and eventually cause damage over a period of time. Reversal of wrong decisions becomes difficult as critical feedback does not reach the leaders.There may be other decision biases in play as well. Filtered/limited information can create availability bias which prompts leaders to draw a conclusion based on available information. . Also, once they have some initial information, they tend to filter all subsequent information through that early data. This is called anchoring bias which can also hamper the decision-making process.Low on InnovationAn environment of blame is unfavorable for innovation. Fear of failure encourages people to maintain status-quo and avoid experimentation. The only exception is made for ideas originating from the leaders themselves!Organizations have various levels of this toxic work culture depending on their leadership styles – some are on the extreme, while others suffer from it fleetingly.The question before us is ‘why do such leadership styles thrive in organizations?’. This cannot be answered by putting the blame on specific leaders and their styles. We will not gain any knowledge by doing so.Cognitive bias and Leadership styleLeaders, like normal human beings, suffer from cognitive biases. One such bias, is Attribution Bias, which refers to the systematic errors made when people evaluate or try to find reasons for their own and others’ behavior/performance.When it comes to undesirable behaviour &nbsp;from others, we tend to attribute it to personality traits of the person. &nbsp;In contrast, when it comes to explaining our own behaviour, &nbsp;we attribute it to systemic conditions, our skills or intelligence, depending on outcomes. When desired results come in, we credit our actions and intelligence. Undesirable results are blamed on unavoidable circumstances. (That reckless driver who skipped the signal is a jerk! But when I did it, it was because I had to rush for an appointment with the doctor. The improved returns in the stock portfolio are because of my decisions while the dip was because of harsh market).&nbsp;Leaders, occupying a position of power over others, receive minimal critical feedback. Hence, they unknowingly suffer severely from attribution cognitive bias.&nbsp;Acknowledgement of BiasUnfortunately, unlike computer programs, the bugs in the form of biases in human cognition cannot be removed. One can only be consciously aware and willing to live with good enough workarounds to prevent its damaging effect.&nbsp;Critical feedback can help leaders become aware of atttribution bias. This is hard to extract from one’s subordinate. So, the only option is to do an experiment on your own, a simple thought experiment.&nbsp;Make two lists of people by asking the following questions.“Who were your favorites in the past? Who are they in the present?”If the lists are different, most likely you are suffering from attribution error. Your own judgement about people cannot be subjected to any repeatable test. Hence, by definition, it cannot be taken seriously.A template to overcome Attribution Bias, once one is aware of the bias and then willing to self-impose a workaround, is possible.&nbsp;The way to overcome &nbsp;attribution bias is by putting in four transparent rules, which will force one to circumvent the bias.&nbsp;<ul><li>Do not blame: &nbsp;When analysing &nbsp;any issue for causality, no one is allowed to blame any manager. If there is a temptation to attribute the undesired behaviour &nbsp;of an individual manager as the cause, the analysis must go deeper to understand why the manager behaved the way he/she &nbsp;did. This way of thinking may force one to look into policies or procedures or formal responsibilities that lead to the behaviour .&nbsp;</li><li>Look for underlying conflict: If issues are chronic and repetitive, assume that there is an underlying deeper conflict in the organisation. . A top-of-the-mind solution for the issue at hand will usually have a severe negative impact on some other aspect of business. Unless the competing needs are understood, no decision should be taken. Sales growth can conflict with inventory reduction needs or margin improvement goals. Quality goals can come in the way of production efficiency improvement. Speeding up a project can come in the way of completing the entire scope.&nbsp;</li><li>Find ways to meet competing needs: The pressure of competing needs is a big reason for a standstill in decision-making. One needs a breakthrough approach to develop solutions which meet both competing needs. Without that, delays in decisions are inevitable. For example, sales growth and inventory reduction can be achieved by finding a way to reduce supply lead time. Quality goals can improve while enhancing efficiency by ensuring quality parameters at source.&nbsp;</li><li>Do not assume people are ‘lazy’: If a manager is not completing tasks assigned to him, most likely he is busy with something else that he/she thinks is more urgent.</li></ul>Leaders should formalise &nbsp;and widely announce these four rules in the organisation. &nbsp;This will exert collective pressure on them (and others) to adhere to them.&nbsp;Good usage of the above “rules” will lead to the following changes<ul><li>Leaders will talk less and listen</li><li>They will master the art of asking critical questions to uncover real issues.</li><li>Soon, they will facilitate decision-making rather than making all the decisions.</li></ul>Once leaders begin following the above practices, the culture of the organisation &nbsp;will undergo a transformation. Transparency will improve as bad news moves up faster. Decisions will be speedier as managers will no longer take refuge in defensive behaviour. &nbsp;More importantly, managers will not worry about failing – a critical requirement for an innovative organisation. &nbsp;&nbsp;

Leaders can transform the culture of an organisation by overcoming their various biases. Read to know more.

Leadership styles and organization culture - Vector Consulting Group

“you are smarter than your data. Data do not understand causes and effects; humans do.”&nbsp;― Judea Pearl, The Book of Why: The New Science of Cause and EffectUnderstanding the ‘why’ to navigate ambiguities in decision-makingIn the complex world of business, the decision-making process can often feel like trying to find your way through a fog. It's hard to be sure you're choosing the right path when everything seems unclear. This article emphasizes a powerful tool to cut through that fog: understanding the ‘why’ behind things, which is a bit like using a map in unknown territory. By looking closely at the reasons behind business challenges, just like scientists do when they're solving mysteries, we can make better decisions. Through real-life examples and stories, we'll see how figuring out the ‘why’ can light up the way forward in business.A case study: Beyond sales team ‘right-sizing’Consider a case of one of our clients (operating in a B2B environment). They faced with the dilemma of a ‘bloated’ sales team that was trained for years to handle client servicing (everything from order delays to payment follow ups). The market was projected to grow slowly at 7% p.a. However, the client wanted to grow faster and profitably. A simple approach would be to cut down on excess salespeople and improve profitability. Right?Wrong! Analyzing this problem with existing data risked taking actions detrimental to the existence of the company! The sales team, in this case, played a crucial role in ensuring that the client’s customers were served on time, every time in a highly competitive market.The understanding we built here was ‘if the market is highly competitive, then order servicing is a business-critical action and cannot be diluted’; having the sales team involved in order servicing end-to-end was a feature, not a bug!Hence, what needed to be done was a rethink of the market the client was operating in, and not ‘right-sizing’ the sales team. We worked out a strategy to retain the customer servicing skills of the existing sales team and reskilling top performers for a high growth market segment that was untapped. We then used data from the untapped market to figure out how to build a moat for the client, thus solving for profitable growth.It worked! The client is in the process of re-designing their entire sales strategy. Taking a step back allowed us to prevent an action that could have jeopardized a business-critical feature of the sales operations!The quest for ‘why’This way of working on business problems (taking a step back and building a deep understanding of the causes at play) is inspired by science. We call it Causal Thinking (you can call it – the quest for ‘Why’). There are many pitfalls to not following this approach. Here’s one example of the many disasters in the business world where decision making has been purely based on data without understanding the ‘Why’.The 2008 Financial CrisiA devastating blow to the global economy was exacerbated by an overreliance on sophisticated algorithms used by rating agencies and trading desks at major banks. These algorithms, intended to assess the risk associated with various financial instruments, were critically flawed. They mistakenly rated complex mortgage-backed securities, many of which were essentially junk bonds, as AAA—the highest possible credit rating. This misrepresentation of risk encouraged a widespread investment in these toxic assets, leading to a massive bubble. When the bubble burst, it triggered a cascade of financial failures, as the true value of these securities became apparent. This crisis highlighted the dangers of relying too heavily on algorithmic judgments in financial markets, underscoring the need for more robust oversight and a deeper understanding of the risks involved in complex financial instruments. The Movie The Big Short illustrates how some predicted the crash by digging deep into story behind mortgage-backed securities, unlike the major banks that missed the signs (or chose to ignore).David Spiegelhalter, in his book, The Art of Statistics: Learning from Data writes, ‘Senior managers simply did not realize the frail basis on which these models were built, losing track of the fact that models are simplifications of the real world–they are the maps, not the territory. The result was one of the worst global economic crises in history.’Contrast this with the lifesaving work done by Dr. John Snow amidst the Cholera outbreak of the 1850s in London.Dr. John Snow, an English physician, is often considered one of the fathers of modern epidemiology thanks to his pioneering work during the Cholera outbreaks in London in the mid-19th century. His most famous research is associated with the 1854 Cholera outbreak in the Broad Street area of Soho, London.&nbsp;Snow did not believe in the then-dominant miasma theory, which suggested that diseases such as Cholera spread through the air as a form of ‘bad air.’ Instead, he proposed that Cholera was spreading through contaminated water – a new theory.Dr. Snow had evidence contrary to the miasma theory which allowed him to propose a new one. For instance, if the ‘miasma’ spread through air, why were there pockets of no disease within a cluster of high disease prevalence? How could air be causing the disease if everyone was breathing the same contaminated air in the same cluster and prominent pockets were completely healthy?Thus began Dr. Snow’s meticulous case tracking and mapping of Cholera cases. He identified a public water pump on Broad Street as the outbreak's epicentre.By gathering data on where the victims of Cholera lived and where they got their water, Dr. Snow was able to convincingly argue that those who drank water from the contaminated Broad Street pump were much more likely to develop and die from Cholera and those who were drinking from safe supplies in the same vicinity as the Broad Street pump, were safe, even though they were in the vicinity. &nbsp;He famously had the handle of the pump removed, and the outbreak quickly subsided.Dr. Snow's work didn't immediately change public health policies, but over time, his theory, backed by a strong evidence-based approach, helped shift the scientific community's perspective towards understanding the importance of sanitation, clean water, and the germ theory of disease. His mapping and data analysis method remains foundational in Epidemiology today, illustrating the importance of building a sound theory, gathering empirical evidence and the application of scientific methods to public health.The key point is Dr. Snow started with a causal framework, a theory of ‘Why’ and then worked his way to meticulously investigated with data to determine whether it was correct - NOT the other way around!<figure class="image"><img style="aspect-ratio:1692/1030;" src="https://new-stage-be.vectorconsulting.in/storage/editor_uploads//screenshot-2024-05-13-at-44453-pm_1715598908.png" width="1692" height="1030"></figure>Data as a Compass, Not the MapJust like Dr. John Snow used data to trace the cause of Cholera back to a single water pump, data can illuminate the actual reasons behind what we observe, helping us validate or invalidate the causal story. A striking illustration from science is how observations during a solar eclipse validated one of Einstein's revolutionary predictions—that gravity can bend light. This concept reshaped our understanding of the universe, but it wasn't universally accepted until data from a solar eclipse showed stars' light curving around the sun, precisely as Einstein had predicted. This historical moment underscores a critical lesson: data is most powerful when used to test our theories and hypotheses, not when viewed in isolation. Just gathering data without a clear idea of what you're looking for can be like finding pieces of different puzzles mixed—you might have a lot of pieces, but they don't help you see the whole picture.In business, this means, before diving into data, we should start with a theory or a hypothesis about why things are happening. Then, use data to see if our theory holds up, guiding us toward the right solutions and decisions. This approach ensures that we're not just collecting data for data's sake but using it as a tool to challenge and refine our understanding, leading to clearer insights and better outcomes.A simple guide to building a causal viewpoint:Let’s circle back to our case on Sales Team ‘Right Sizing’ and analyze it using simple causal analysis steps:1. Clarify the problem you’re trying to fix (an exact definition of the problem) –<ul><li>Good precision: “Growth has slowed down to the market rate of ~7% while the cost of sales (including sales team opex) has gone up 1% point”</li><li>Poor Precision: ”We have a large sales force”</li></ul>2. Do NOT explain the problem – let the problem stand on its own<ul><li>‘Cost of sales has gone up because we’ve hired people faster that market growth’</li></ul>3. Start with a Hypothesis - a simple explanation of ‘why this might be happening’<ul><li>&nbsp;‘Share of business at regular customers is growing at market rate while the share of business at irregular customers is stagnant or declining as these customers are switching to other types of input raw materials (this is where the bulk of new sales recruits have been deployed)’</li></ul>&nbsp;4. Try to invalidate the hypothesis by counter-examples or any other variable impacting the outcome<ul><li>Switching to other raw materials is irrelevant, as this is true only for a few customers</li><li>Import of input raw material has increased impacting the client’s customer base</li><li>Overall consumption of the input raw material still remains steady at 7% for ‘irregular’ customers as well</li></ul>5. If the hypothesis still stands, check for effects of the hypothesis being true, if not build a new hypothesis.The hypothesis does not stand the test of invalidation in this case. A new hypothesis is needed!<ul><li>New Hypothesis: Imported raw material is cheaper and of similar quality, allowing ‘irregular’ customers to switch</li><li>Check for effects if the new hypothesis is true:<ul><li>‘Irregular’ customers are still consuming the same raw material at a steady pace</li><li>Switched customers are fine with increased lead time of supply from imports</li><li>Sales team’s efforts are rejected primarily on the price differential</li><li>If the sales teamis able to match price with imports, some of the irregular customers switch back</li></ul></li></ul>6. Build a direction of solutionThe new hypothesis stands validated. It is now time to brainstorm solutions.a. Since competing on price alone is recipe for continuous margin pressure, this direction is ruled out.b. There will always be imported raw materials flooding the domestic market for regular SKUs; there is no way of stopping thisc. Customers already prefer our raw material for non-regular SKUsd. The ONLY way to sustain existing demand and grow FASTER than the market is to DEVELOP more consumption occasions – i.e. convert users of other types of raw materials! A tall ask, but its possible. Our client, therefore, started re-deploying the sales team, and the ‘bloated’ team no longer needed to be ‘right-sized’. In fact, we might need to hire more!These simple steps need rigor of analysis and can be quite challenging to implement without practice. It’s easy to find quick fixes using existing data. However, building a habit of thinking causally can have immense bottom line benefits for companies – by avoiding erroneous decision-making and by ignoring irrelevant variables. This approach allows focussing on the right problem and the right explanation for the problem. So, happy thinking!

ASK THE ‘WHY’  How causality can play a crucial role in solving business problems

“you are smarter than your data. Data do not understand causes and effects; humans do.”&nbsp;― Judea Pearl, The Book of Why: The New Science of Cause and EffectUnderstanding the ‘why’ to navigate ambiguities in decision-makingIn the complex world of business, the decision-making process can often feel like trying to find your way through a fog. It's hard to be sure you're choosing the right path when everything seems unclear. This article emphasizes a powerful tool to cut through that fog: understanding the ‘why’ behind things, which is a bit like using a map in unknown territory. By looking closely at the reasons behind business challenges, just like scientists do when they're solving mysteries, we can make better decisions. Through real-life examples and stories, we'll see how figuring out the ‘why’ can light up the way forward in business.A case study: Beyond sales team ‘right-sizing’Consider a case of one of our clients (operating in a B2B environment). They faced with the dilemma of a ‘bloated’ sales team that was trained for years to handle client servicing (everything from order delays to payment follow ups). The market was projected to grow slowly at 7% p.a. However, the client wanted to grow faster and profitably. A simple approach would be to cut down on excess salespeople and improve profitability. Right?Wrong! Analyzing this problem with existing data risked taking actions detrimental to the existence of the company! The sales team, in this case, played a crucial role in ensuring that the client’s customers were served on time, every time in a highly competitive market.The understanding we built here was ‘if the market is highly competitive, then order servicing is a business-critical action and cannot be diluted’; having the sales team involved in order servicing end-to-end was a feature, not a bug!Hence, what needed to be done was a rethink of the market the client was operating in, and not ‘right-sizing’ the sales team. We worked out a strategy to retain the customer servicing skills of the existing sales team and reskilling top performers for a high growth market segment that was untapped. We then used data from the untapped market to figure out how to build a moat for the client, thus solving for profitable growth.It worked! The client is in the process of re-designing their entire sales strategy. Taking a step back allowed us to prevent an action that could have jeopardized a business-critical feature of the sales operations!The quest for ‘why’This way of working on business problems (taking a step back and building a deep understanding of the causes at play) is inspired by science. We call it Causal Thinking (you can call it – the quest for ‘Why’). There are many pitfalls to not following this approach. Here’s one example of the many disasters in the business world where decision making has been purely based on data without understanding the ‘Why’.The 2008 Financial CrisiA devastating blow to the global economy was exacerbated by an overreliance on sophisticated algorithms used by rating agencies and trading desks at major banks. These algorithms, intended to assess the risk associated with various financial instruments, were critically flawed. They mistakenly rated complex mortgage-backed securities, many of which were essentially junk bonds, as AAA—the highest possible credit rating. This misrepresentation of risk encouraged a widespread investment in these toxic assets, leading to a massive bubble. When the bubble burst, it triggered a cascade of financial failures, as the true value of these securities became apparent. This crisis highlighted the dangers of relying too heavily on algorithmic judgments in financial markets, underscoring the need for more robust oversight and a deeper understanding of the risks involved in complex financial instruments. The Movie The Big Short illustrates how some predicted the crash by digging deep into story behind mortgage-backed securities, unlike the major banks that missed the signs (or chose to ignore).David Spiegelhalter, in his book, The Art of Statistics: Learning from Data writes, ‘Senior managers simply did not realize the frail basis on which these models were built, losing track of the fact that models are simplifications of the real world–they are the maps, not the territory. The result was one of the worst global economic crises in history.’Contrast this with the lifesaving work done by Dr. John Snow amidst the Cholera outbreak of the 1850s in London.Dr. John Snow, an English physician, is often considered one of the fathers of modern epidemiology thanks to his pioneering work during the Cholera outbreaks in London in the mid-19th century. His most famous research is associated with the 1854 Cholera outbreak in the Broad Street area of Soho, London.&nbsp;Snow did not believe in the then-dominant miasma theory, which suggested that diseases such as Cholera spread through the air as a form of ‘bad air.’ Instead, he proposed that Cholera was spreading through contaminated water – a new theory.Dr. Snow had evidence contrary to the miasma theory which allowed him to propose a new one. For instance, if the ‘miasma’ spread through air, why were there pockets of no disease within a cluster of high disease prevalence? How could air be causing the disease if everyone was breathing the same contaminated air in the same cluster and prominent pockets were completely healthy?Thus began Dr. Snow’s meticulous case tracking and mapping of Cholera cases. He identified a public water pump on Broad Street as the outbreak's epicentre.By gathering data on where the victims of Cholera lived and where they got their water, Dr. Snow was able to convincingly argue that those who drank water from the contaminated Broad Street pump were much more likely to develop and die from Cholera and those who were drinking from safe supplies in the same vicinity as the Broad Street pump, were safe, even though they were in the vicinity. &nbsp;He famously had the handle of the pump removed, and the outbreak quickly subsided.Dr. Snow's work didn't immediately change public health policies, but over time, his theory, backed by a strong evidence-based approach, helped shift the scientific community's perspective towards understanding the importance of sanitation, clean water, and the germ theory of disease. His mapping and data analysis method remains foundational in Epidemiology today, illustrating the importance of building a sound theory, gathering empirical evidence and the application of scientific methods to public health.The key point is Dr. Snow started with a causal framework, a theory of ‘Why’ and then worked his way to meticulously investigated with data to determine whether it was correct - NOT the other way around!<figure class="image"><img style="aspect-ratio:1692/1030;" src="https://new-stage-be.vectorconsulting.in/storage/editor_uploads//screenshot-2024-05-13-at-44453-pm_1715598908.png" width="1692" height="1030"></figure>Data as a Compass, Not the MapJust like Dr. John Snow used data to trace the cause of Cholera back to a single water pump, data can illuminate the actual reasons behind what we observe, helping us validate or invalidate the causal story. A striking illustration from science is how observations during a solar eclipse validated one of Einstein's revolutionary predictions—that gravity can bend light. This concept reshaped our understanding of the universe, but it wasn't universally accepted until data from a solar eclipse showed stars' light curving around the sun, precisely as Einstein had predicted. This historical moment underscores a critical lesson: data is most powerful when used to test our theories and hypotheses, not when viewed in isolation. Just gathering data without a clear idea of what you're looking for can be like finding pieces of different puzzles mixed—you might have a lot of pieces, but they don't help you see the whole picture.In business, this means, before diving into data, we should start with a theory or a hypothesis about why things are happening. Then, use data to see if our theory holds up, guiding us toward the right solutions and decisions. This approach ensures that we're not just collecting data for data's sake but using it as a tool to challenge and refine our understanding, leading to clearer insights and better outcomes.A simple guide to building a causal viewpoint:Let’s circle back to our case on Sales Team ‘Right Sizing’ and analyze it using simple causal analysis steps:1. Clarify the problem you’re trying to fix (an exact definition of the problem) –<ul><li>Good precision: “Growth has slowed down to the market rate of ~7% while the cost of sales (including sales team opex) has gone up 1% point”</li><li>Poor Precision: ”We have a large sales force”</li></ul>2. Do NOT explain the problem – let the problem stand on its own<ul><li>‘Cost of sales has gone up because we’ve hired people faster that market growth’</li></ul>3. Start with a Hypothesis - a simple explanation of ‘why this might be happening’<ul><li>&nbsp;‘Share of business at regular customers is growing at market rate while the share of business at irregular customers is stagnant or declining as these customers are switching to other types of input raw materials (this is where the bulk of new sales recruits have been deployed)’</li></ul>&nbsp;4. Try to invalidate the hypothesis by counter-examples or any other variable impacting the outcome<ul><li>Switching to other raw materials is irrelevant, as this is true only for a few customers</li><li>Import of input raw material has increased impacting the client’s customer base</li><li>Overall consumption of the input raw material still remains steady at 7% for ‘irregular’ customers as well</li></ul>5. If the hypothesis still stands, check for effects of the hypothesis being true, if not build a new hypothesis.The hypothesis does not stand the test of invalidation in this case. A new hypothesis is needed!<ul><li>New Hypothesis: Imported raw material is cheaper and of similar quality, allowing ‘irregular’ customers to switch</li><li>Check for effects if the new hypothesis is true:<ul><li>‘Irregular’ customers are still consuming the same raw material at a steady pace</li><li>Switched customers are fine with increased lead time of supply from imports</li><li>Sales team’s efforts are rejected primarily on the price differential</li><li>If the sales teamis able to match price with imports, some of the irregular customers switch back</li></ul></li></ul>6. Build a direction of solutionThe new hypothesis stands validated. It is now time to brainstorm solutions.a. Since competing on price alone is recipe for continuous margin pressure, this direction is ruled out.b. There will always be imported raw materials flooding the domestic market for regular SKUs; there is no way of stopping thisc. Customers already prefer our raw material for non-regular SKUsd. The ONLY way to sustain existing demand and grow FASTER than the market is to DEVELOP more consumption occasions – i.e. convert users of other types of raw materials! A tall ask, but its possible. Our client, therefore, started re-deploying the sales team, and the ‘bloated’ team no longer needed to be ‘right-sized’. In fact, we might need to hire more!These simple steps need rigor of analysis and can be quite challenging to implement without practice. It’s easy to find quick fixes using existing data. However, building a habit of thinking causally can have immense bottom line benefits for companies – by avoiding erroneous decision-making and by ignoring irrelevant variables. This approach allows focussing on the right problem and the right explanation for the problem. So, happy thinking!&nbsp;&nbsp;

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