<h2>Living with the chronic problem and associated conflicts</h2>Managing the supply chain of a distribution company (such as a consumer goods company, a retail chain or a spare parts distribution for automotive/industrial applications) is extremely challenging. The challenge lies in the frequent conflicts managers face when making day-to-day supply chain decisions. For example:<ul><li>The chronic conflict between sales and logistics centres on  inventory: while the sales team always want higher inventory to protect sales, logistics and finance want to limit inventory to control costs.</li><li>Sales are more than willing to start a discount scheme and push out inventories or counter competition, while finance is wary of such drops in product margins.</li><li>Production wants schedules (based on forecasts) to remain stable, while marketing would want production to be more flexible to the changes in market requirements.</li><li>Sales would want more budget allocated for marketing and advertising, while others may insist on sales growth to fund the extra allocation for marketing and advertising expenses.</li></ul> These conflicts manifest in seemingly contradictory supply chain issues, such as having significant stockouts despite having high overall inventory (inventory turns of around three or four) OR price pressure from the supply chain intermediaries, while the price to the end consumer is not affected OR new products introduced, when the old ones still clog the pipeline. The problem is further aggravated for high-tech products, where the total inventory in the distribution chain for most companies is usually much more than the product's life cycle. This leads to significant price discounts and the subsequent negative impact on companies’ profitability. Fashion products face a similar challenge – too many SKUs, and all of them need to be available much before the fashion season. During the season, there are stockouts in the distribution chain for about 20%-30% of the items (those that sell well) after a few initial weeks of the long fashion season. Towards the close of the season, there are many slow movers that have to be moved to the ‘factory outlet’ for discount sales.  The ramifications of dealing with such supply chains are significant for the end retail shop. A retail shop is always constrained on cash and/or space. Most of the shop's inventory is skewed towards the slow movers. Since the slow movers block cash and space, the sales efforts and space are allocated more towards them. This, in turn, removes the opportunity for retail shops to clock more profitable sales from the fast movers. At the same time, shortages occur, usually of the fast runners. With too much cash tied up in inventory, the ROI for retail shops is lower than desired. With such chronic conflicts in the distribution chain, significant sales growth (~30% over the previous year) is never targeted in the annual business plan because many believe that such rapid sales growth will invariably come at the cost of very high expenses, lower margins, or higher inventory levels. Targets are grudgingly set at <10% growth over the previous year in many distribution organisations.  Is there a way out of this problem? In response to this query, a manager of a large distribution company once remarked, “We can set and meet any ambitious target if we can be 100% accurate on the sales forecasts. An accurate forecast will ensure we make all the right SKUs, distribute them to the right location of demand, and do not feel the pressure to drop prices as inventory matches the forecast".  <h2>Direction of Solution which isn’t!</h2>An accurate forecast! This looks like a good direction of solution. No doubt, many companies are struggling to improve the accuracy of their forecasts. Many have invested in expensive software tools to improve forecasts. Despite all the investments, the problem remains with forecasts – they are not as accurate as one would want them to be. However, the fundamental question is, “Can forecasts ever be accurate?” We may be able to provide a 'reasonably' good forecast for a product at the national level, but forecasting at SKU and location levels for a long horizon is as reliable as weather forecasting. Chaos theory shows that it is almost impossible to accurately predict the outcome of a chaotic system accurately, such as market demand. A small change in any demand variable can lead to a disproportionate outcome, making it difficult to predict the outcome. For example, a competitor’s supply gets affected in one location, leading to a sudden surge in demand, further aggravated by scarcity-driven purchases or a sudden surge in demand for a fashion SKU after a local hero is seen wearing it at a public event. The sales at SKU and location levels are highly fluctuating and unpredictable, and it is impossible to predict the impact of all variables on demand. Any attempt to bring sanity to this level is a futile exercise.  Many organisations have adopted a Min-Max replenishment system in their supply chains to address the forecasting problem. An order is placed to the ‘Max’ level when inventory reaches the ‘Min’ level or the reorder point. The ‘Min’ inventory is supposed to take care of demand fluctuations in  during the replenishment lead time.  However, in a growing-demand market, the ‘Min’ inventory across the entire distribution chain can vanish before the next supply comes in. Alternatively, during reduced market demand, one need not procure when the inventory reaches the 'Min' level because the company can afford to wait further. In case of high demand fluctuations, such as at the end of the supply chain, the ‘Min-Max’ system does not seem to provide adequate protection, but it can also lead to higher inventory from time to time. In many organisations, the ‘Min’ inventory is set based on a sales target, which, in any case, is also a forecast.  Some other companies have defined global inventory policies for the distribution chain. For example, a dealer has to carry inventory for so many weeks. Such policies are even more damaging for the supply chain, as the demand for one SKU, in most cases, has nothing to do with the demand for the other. The total cap on the overall inventory policy leads to a decision in which the inventory in the distribution chain is inadequate for some SKUs, while for others it is more than adequate. Such a policy also leads to situations in which a distributor is a few hours away from the company warehouse but has only a few weeks or even a few months’ stock while still suffering from stockouts. In most organizations, the inventory is set, thinking only about the demand without considering the supply lead times. Theoretically, inventory norms should be set based on both demand fluctuations and supply times. However, many ignore the supply lead times and set inventory norms only based on demand fluctuations.  <h2>The Solution: Simple yet powerful!  </h2>The starting point of building a good solution is to go back to the basics of inventory management. One needs to understand that inventory is maintained for products where the customer tolerance time is much less than the time to produce. Hence, there is a need to keep inventory so as not to lose the customer. The inventory at any location has to account for the following variables: <ul><li>Replenishment lead time</li><li>Demand during lead time</li><li>Variations in supply time  </li><li>Variation in demand </li></ul> Of all the above variables, replenishment lead time is the most important as it affects all the other variables. For example, the supply lead time is highly variable when it is longer. The demand is also highly variable for a longer lead time.So, if one wants to improve the forecasting, it is essential to focus on one variable – the replenishment lead time. If can reduce the replenishment lead time significantly, we can manage with much less inventory, and forecasting accuracy will improve. To reduce supply lead time in a distribution chain, one needs to understand the components of the replenishment lead time. The components are: <ul><li>Order lead time (time till an order is placed for an SKU)</li><li>Production lead time</li><li>Transportation lead time </li></ul><figure class="image"><img style="aspect-ratio:16668/10415;" src="https://web-assets.vectorconsulting.in/storage/editor_uploads//cg-article-17-img-1-01-1733794746_1777273751.jpg" width="16668" height="10415"></figure> <h2>Reducing the Order Lead Time</h2>The above diagram shows that the order lead time is a significant part of the total lead time. The ‘Min-Max’ ordering system increases the ordering lead time, as one has to wait till the level reaches the reorder point before placing orders. Many organisations take orders from distributors once or twice a month per SKU, even though they might be taking many orders throughout the month. For an SKU, the lead time goes up. Can we reduce the order lead time? Yes, in an era of interlinked computers and EDI (Electronic Data Interchange) systems, we can go with daily ordering per SKU – we do not need optimise by clubbing orders per SKU. Does it mean that the supplier has to ship out more frequently with partial loads? No. With frequent shipments, each shipment will now have a larger assortment of SKUs rather than a single SKU. The change is as depicted in the diagram below:<figure class="image"><img style="aspect-ratio:16668/8960;" src="https://web-assets.vectorconsulting.in/storage/editor_uploads//cg-article-17-img-2-01-1733794756_1777274042.jpg" width="16668" height="8960"></figure><h2>Managing with a lower inventory</h2>Now that the supply lead time has been reduced significantly, one needs to stock enough to cover any demand during the supply replenishment time. The next step is to focus on fast replenishment of the actual consumption from the stocks. The need for forecasting goes away – the entire supply chain just reacts to a very objective data point – the consumption. For every item at every location in the distribution chain, a target inventory is set based on 'paranoid' consumption during replenishment time (the maximum forecasted demand during the replenishment time factored by the fluctuations in the replenishment time). The stock is replenished at the pace of the sales.  <h2>Managing exceptions with buffer management</h2>Even though supply is based on consumption, there is a chance that between two supplies the inventory might fall to dangerous levels, which, in turn, can lead to stock-outs. The target inventory is a planning decision – we need a system to manage exceptions during execution. Buffer Management is an execution control method that provides priorities based on the actual consumption of the buffers.<ul><li>The target level of every item at any location is a Buffer.</li><li>Buffer status measures how much stock does NOT reside at a location compared to the target level.</li><li>It is defined as the percentage of (Target Level – On hand) to the Target Level.</li><li>What is missing from the on-hand stock should be somewhere in the distribution chain from the source to the target.  </li></ul>When the stock at the target is more than 2/3rds of the target level, the buffer is considered Green, indicating too much stock. When the stock at the target is between 1/3rd and 2/3rd of the target level, the buffer is considered Yellow, implying that the stock level is OK. When the stock at the target is less than 1/3rd of the target level, the buffer is considered Red, which means that things are NOT OK. There is a real risk of running out of stock, and a quick replenishment is required. <figure class="image"><img style="aspect-ratio:16668/12415;" src="https://web-assets.vectorconsulting.in/storage/editor_uploads//cg-article-17-img-3-02-1733794787_1777274173.jpg" width="16668" height="12415"></figure><figure class="image"><img style="aspect-ratio:16668/12415;" src="https://web-assets.vectorconsulting.in/storage/editor_uploads//cg-article-17-img-3-01-1733794798_1777274220.jpg" width="16668" height="12415"></figure><h2>Changing the target levels</h2>If the stock stays continuously in Red throughout the replenishment period, there is a need to look at changing the target levels. Similarly, when the stock stays in Green for multiple replenishment periods, it is time to consider reducing the norms. This Dynamic Buffer Management (DBM) system helps align stock level based on the demand conditions for each per SKU and location. The decision is much better than the current practice of just arbitrarily cutting stock norms. When one arbitrarily cuts the stock norms, they usually reduce the fast runners, leading to more stockouts. The DBM system prevents such adhoc decisions.  <h2>Benefits of aggregation: The plant warehouse</h2>In most push-based supply chains, the bulk of the inventory is close to the demand point, where demand fluctuations are most erratic. Sales targets based on primary sales push inventory close to the demand point. This leads to stockouts of an SKU at one location in the distribution chain, while another location may have excess. Many organisations have tried to solve the problem by inter-warehouse transfers, but rising logistics costs and the temptation to hold on to inventory at local points have made this solution ineffective. We can solve the problem by introducing a skew in inventory at a point close to the supply, where demand is flat and stable. A plant warehouse with the bulk of the inventory feeding the regional warehouse should solve the problem to a large extent. The plant warehouse will decouple the regional warehouse from production fluctuations. The inventory at the plant warehouse will account for production lead time, while the regional warehouse needs to stock only to account for the transportation lead time. The plant warehouse will reduce overall inventory levels in the supply chain, as fluctuations are lower at the mother warehouse level than at the regional or retail levels. Demand points in the distribution chain are free from production fluctuations, so they can manage with much lower inventory than the high levels that they are  currently used to. The supply to each inventory location is based on consumption. For example, the plant warehouse will supply the regional warehouse based on consumption; similarly, the plant will produce based on consumption in the plant warehouse.  <h2>The Expected Benefits</h2>The stockouts should drastically reduce as replenishment is reacting to sales pace much faster than before. In most environments, we see a sales jump of around 30%-40% after the implementation of the replenishment solution. The inventory drops drastically while the dealers’ ROI goes up significantly as they manage business with a much wider spread of SKUs without any stockouts.

Key Takeaways

Despite significant investments in advanced forecasting tools, predicting SKU and location-level demands remains elusive due to market complexities. Minmax replenishment systems and global inventory policies often fall short, highlighting the need to revisit inventory management basics.

Managing distribution chain: Is there a better way than crystal ball gazing?

<h2>Living with the chronic problem and associated conflicts</h2>Managing the supply chain of a distribution company (such as a consumer goods company, a retail chain or a spare parts distribution for automotive/industrial applications) is extremely challenging. The challenge lies in the frequent conflicts managers face when making day-to-day supply chain decisions. For example:<ul><li>The chronic conflict between sales and logistics centres on  inventory: while the sales team always want higher inventory to protect sales, logistics and finance want to limit inventory to control costs.</li><li>Sales are more than willing to start a discount scheme and push out inventories or counter competition, while finance is wary of such drops in product margins.</li><li>Production wants schedules (based on forecasts) to remain stable, while marketing would want production to be more flexible to the changes in market requirements.</li><li>Sales would want more budget allocated for marketing and advertising, while others may insist on sales growth to fund the extra allocation for marketing and advertising expenses.</li></ul> These conflicts manifest in seemingly contradictory supply chain issues, such as having significant stockouts despite having high overall inventory (inventory turns of around three or four) OR price pressure from the supply chain intermediaries, while the price to the end consumer is not affected OR new products introduced, when the old ones still clog the pipeline. The problem is further aggravated for high-tech products, where the total inventory in the distribution chain for most companies is usually much more than the product's life cycle. This leads to significant price discounts and the subsequent negative impact on companies’ profitability. Fashion products face a similar challenge – too many SKUs, and all of them need to be available much before the fashion season. During the season, there are stockouts in the distribution chain for about 20%-30% of the items (those that sell well) after a few initial weeks of the long fashion season. Towards the close of the season, there are many slow movers that have to be moved to the ‘factory outlet’ for discount sales.  The ramifications of dealing with such supply chains are significant for the end retail shop. A retail shop is always constrained on cash and/or space. Most of the shop's inventory is skewed towards the slow movers. Since the slow movers block cash and space, the sales efforts and space are allocated more towards them. This, in turn, removes the opportunity for retail shops to clock more profitable sales from the fast movers. At the same time, shortages occur, usually of the fast runners. With too much cash tied up in inventory, the ROI for retail shops is lower than desired. With such chronic conflicts in the distribution chain, significant sales growth (~30% over the previous year) is never targeted in the annual business plan because many believe that such rapid sales growth will invariably come at the cost of very high expenses, lower margins, or higher inventory levels. Targets are grudgingly set at <10% growth over the previous year in many distribution organisations.  Is there a way out of this problem? In response to this query, a manager of a large distribution company once remarked, “We can set and meet any ambitious target if we can be 100% accurate on the sales forecasts. An accurate forecast will ensure we make all the right SKUs, distribute them to the right location of demand, and do not feel the pressure to drop prices as inventory matches the forecast".  <h2>Direction of Solution which isn’t!</h2>An accurate forecast! This looks like a good direction of solution. No doubt, many companies are struggling to improve the accuracy of their forecasts. Many have invested in expensive software tools to improve forecasts. Despite all the investments, the problem remains with forecasts – they are not as accurate as one would want them to be. However, the fundamental question is, “Can forecasts ever be accurate?” We may be able to provide a 'reasonably' good forecast for a product at the national level, but forecasting at SKU and location levels for a long horizon is as reliable as weather forecasting. Chaos theory shows that it is almost impossible to accurately predict the outcome of a chaotic system accurately, such as market demand. A small change in any demand variable can lead to a disproportionate outcome, making it difficult to predict the outcome. For example, a competitor’s supply gets affected in one location, leading to a sudden surge in demand, further aggravated by scarcity-driven purchases or a sudden surge in demand for a fashion SKU after a local hero is seen wearing it at a public event. The sales at SKU and location levels are highly fluctuating and unpredictable, and it is impossible to predict the impact of all variables on demand. Any attempt to bring sanity to this level is a futile exercise.  Many organisations have adopted a Min-Max replenishment system in their supply chains to address the forecasting problem. An order is placed to the ‘Max’ level when inventory reaches the ‘Min’ level or the reorder point. The ‘Min’ inventory is supposed to take care of demand fluctuations in  during the replenishment lead time.  However, in a growing-demand market, the ‘Min’ inventory across the entire distribution chain can vanish before the next supply comes in. Alternatively, during reduced market demand, one need not procure when the inventory reaches the 'Min' level because the company can afford to wait further. In case of high demand fluctuations, such as at the end of the supply chain, the ‘Min-Max’ system does not seem to provide adequate protection, but it can also lead to higher inventory from time to time. In many organisations, the ‘Min’ inventory is set based on a sales target, which, in any case, is also a forecast.  Some other companies have defined global inventory policies for the distribution chain. For example, a dealer has to carry inventory for so many weeks. Such policies are even more damaging for the supply chain, as the demand for one SKU, in most cases, has nothing to do with the demand for the other. The total cap on the overall inventory policy leads to a decision in which the inventory in the distribution chain is inadequate for some SKUs, while for others it is more than adequate. Such a policy also leads to situations in which a distributor is a few hours away from the company warehouse but has only a few weeks or even a few months’ stock while still suffering from stockouts. In most organizations, the inventory is set, thinking only about the demand without considering the supply lead times. Theoretically, inventory norms should be set based on both demand fluctuations and supply times. However, many ignore the supply lead times and set inventory norms only based on demand fluctuations.  <h2>The Solution: Simple yet powerful!  </h2>The starting point of building a good solution is to go back to the basics of inventory management. One needs to understand that inventory is maintained for products where the customer tolerance time is much less than the time to produce. Hence, there is a need to keep inventory so as not to lose the customer. The inventory at any location has to account for the following variables: <ul><li>Replenishment lead time</li><li>Demand during lead time</li><li>Variations in supply time  </li><li>Variation in demand </li></ul> Of all the above variables, replenishment lead time is the most important as it affects all the other variables. For example, the supply lead time is highly variable when it is longer. The demand is also highly variable for a longer lead time.So, if one wants to improve the forecasting, it is essential to focus on one variable – the replenishment lead time. If can reduce the replenishment lead time significantly, we can manage with much less inventory, and forecasting accuracy will improve. To reduce supply lead time in a distribution chain, one needs to understand the components of the replenishment lead time. The components are: <ul><li>Order lead time (time till an order is placed for an SKU)</li><li>Production lead time</li><li>Transportation lead time </li></ul><figure class="image"><img style="aspect-ratio:16668/10415;" src="https://web-assets.vectorconsulting.in/storage/editor_uploads//cg-article-17-img-1-01-1733794746_1777273721.jpg" width="16668" height="10415"></figure> <h2>Reducing the Order Lead Time</h2>The above diagram shows that the order lead time is a significant part of the total lead time. The ‘Min-Max’ ordering system increases the ordering lead time, as one has to wait till the level reaches the reorder point before placing orders. Many organisations take orders from distributors once or twice a month per SKU, even though they might be taking many orders throughout the month. For an SKU, the lead time goes up. Can we reduce the order lead time? Yes, in an era of interlinked computers and EDI (Electronic Data Interchange) systems, we can go with daily ordering per SKU – we do not need optimise by clubbing orders per SKU. Does it mean that the supplier has to ship out more frequently with partial loads? No. With frequent shipments, each shipment will now have a larger assortment of SKUs rather than a single SKU. The change is as depicted in the diagram below:<figure class="image"><img style="aspect-ratio:16668/8960;" src="https://web-assets.vectorconsulting.in/storage/editor_uploads//cg-article-17-img-2-01-1733794756_1777274019.jpg" width="16668" height="8960"></figure><h2>Managing with a lower inventory</h2>Now that the supply lead time has been reduced significantly, one needs to stock enough to cover any demand during the supply replenishment time. The next step is to focus on fast replenishment of the actual consumption from the stocks. The need for forecasting goes away – the entire supply chain just reacts to a very objective data point – the consumption. For every item at every location in the distribution chain, a target inventory is set based on 'paranoid' consumption during replenishment time (the maximum forecasted demand during the replenishment time factored by the fluctuations in the replenishment time). The stock is replenished at the pace of the sales.  <h2>Managing exceptions with buffer management</h2>Even though supply is based on consumption, there is a chance that between two supplies the inventory might fall to dangerous levels, which, in turn, can lead to stock-outs. The target inventory is a planning decision – we need a system to manage exceptions during execution. Buffer Management is an execution control method that provides priorities based on the actual consumption of the buffers.<ul><li>The target level of every item at any location is a Buffer.</li><li>Buffer status measures how much stock does NOT reside at a location compared to the target level.</li><li>It is defined as the percentage of (Target Level – On hand) to the Target Level.</li><li>What is missing from the on-hand stock should be somewhere in the distribution chain from the source to the target.  </li></ul>When the stock at the target is more than 2/3rds of the target level, the buffer is considered Green, indicating too much stock. When the stock at the target is between 1/3rd and 2/3rd of the target level, the buffer is considered Yellow, implying that the stock level is OK. When the stock at the target is less than 1/3rd of the target level, the buffer is considered Red, which means that things are NOT OK. There is a real risk of running out of stock, and a quick replenishment is required. <figure class="image"><img style="aspect-ratio:16668/12415;" src="https://web-assets.vectorconsulting.in/storage/editor_uploads//cg-article-17-img-3-02-1733794787_1777274114.jpg" width="16668" height="12415"></figure><figure class="image"><img style="aspect-ratio:16668/12415;" src="https://web-assets.vectorconsulting.in/storage/editor_uploads//cg-article-17-img-3-01-1733794798_1777273869.jpg" width="16668" height="12415"></figure> <h2>Changing the target levels</h2>If the stock stays continuously in Red throughout the replenishment period, there is a need to look at changing the target levels. Similarly, when the stock stays in Green for multiple replenishment periods, it is time to consider reducing the norms. This Dynamic Buffer Management (DBM) system helps align stock level based on the demand conditions for each per SKU and location. The decision is much better than the current practice of just arbitrarily cutting stock norms. When one arbitrarily cuts the stock norms, they usually reduce the fast runners, leading to more stockouts. The DBM system prevents such adhoc decisions.  <h2>Benefits of aggregation: The plant warehouse</h2>In most push-based supply chains, the bulk of the inventory is close to the demand point, where demand fluctuations are most erratic. Sales targets based on primary sales push inventory close to the demand point. This leads to stockouts of an SKU at one location in the distribution chain, while another location may have excess. Many organisations have tried to solve the problem by inter-warehouse transfers, but rising logistics costs and the temptation to hold on to inventory at local points have made this solution ineffective. We can solve the problem by introducing a skew in inventory at a point close to the supply, where demand is flat and stable. A plant warehouse with the bulk of the inventory feeding the regional warehouse should solve the problem to a large extent. The plant warehouse will decouple the regional warehouse from production fluctuations. The inventory at the plant warehouse will account for production lead time, while the regional warehouse needs to stock only to account for the transportation lead time. The plant warehouse will reduce overall inventory levels in the supply chain, as fluctuations are lower at the mother warehouse level than at the regional or retail levels. Demand points in the distribution chain are free from production fluctuations, so they can manage with much lower inventory than the high levels that they are  currently used to. The supply to each inventory location is based on consumption. For example, the plant warehouse will supply the regional warehouse based on consumption; similarly, the plant will produce based on consumption in the plant warehouse.  <h2>The Expected Benefits</h2>The stockouts should drastically reduce as replenishment is reacting to sales pace much faster than before. In most environments, we see a sales jump of around 30%-40% after the implementation of the replenishment solution. The inventory drops drastically while the dealers’ ROI goes up significantly as they manage business with a much wider spread of SKUs without any stockouts.

<h4>Managing distribution chain: Is there a better way than gazing the crystal ball?</h4>

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Vector Consulting Group - Research and Publications: Adopting replenishment based distribution instead of a forecast based inventory movement helps increase sales. Know more.