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


Living with the Chronic Problem and Associated Conflicts

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 is inherent in the frequent conflicts that the managers face in handling the day-to-day decisions for the supply chain. For example:

  • The chronic conflict between sales and logistics is about inventory - while sales always want higher inventory for protecting sales, logistics and finance want to limit inventory to control costs.
  • Sales is 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.
  • Production wants schedules (based on forecasts) to remain stable, while marketing would want production to be more flexible to the changes in market requirements.
  • Sales would want more budgets allocated for marketing and advertising expenses, while others may insist on sales growth to fund the extra allocation for marketing and advertising expenses.

These conflicts manifest in seemingly contradictory supply chain issues, such as having significant stock-outs despite having high overall inventory (inventory turns of around 3 or 4) 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 life cycle of the product itself. This leads to significant price discounts, and the subsequent negative impact on profitability of companies. Fashion products face a similar challenge – too many SKUs and all the SKUs need to be available much before the fashion season. During the season, there are stock-outs in the distribution chain for about 20 to 30% of the items (those that sell well) after few initial weeks of the long fashion season. Towards the close of the season, there are many slow movers which have to move 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 inventory is skewed towards the slow movers. Since the slow movers block cash and space, the sales efforts and space are allocated more towards the slow movers. 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 shop is less than what is desired.

With such chronic conflicts in the distribution chain, significant growth in sales (about 30% over the previous year) is never targeted in the annual business plan, because many believe that such a rapid growth in sales will invariably come at the cost of very high growth in expenses, lower margins or high inventory. The targets are grudgingly set at less than 10% growth over the previous year in many distribution organizations.

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 are able to be 100% accurate on the sales forecasts. An accurate forecast will ensure we make all the right SKUs, and distribute it to the right location of demand, and do not feel the pressure to drop prices as inventory matches the forecast.”

Direction of Solution which Isn’t!

An Accurate Forecast! This looks like a good direction of solution. No doubt many of the companies are struggling hard 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 be ever 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 does validate that it is almost impossible to predict the outcome of a chaotic system accurately, like demand in a market. A small change in any demand variable can lead to disproportionate outcome, making it difficult to predict the outcome. For example, the supply of the competitor gets affected in one location, leading to a sudden surge in demand, further aggravated by scarcity driven purchases or a sudden surge in demand of a fashion SKU after a local hero is seen wearing the product in a public event. The sales at SKU and location level are highly fluctuating and unpredictable, and it is impossible to predict the impact of all variables on demand. Any attempt to bring sanity at this level is a futile exercise.

Many organizations have moved to minmax replenishment system in their supply chain to get over the forecasting problem. An order is placed to the max level when inventory reaches the min level or the re-order point. The min inventory is supposed to take care of fluctuations in demand during the lead time of replenishment.

However, in a growing demand market, the min inventory in the entire distribution chain can vanish before the next supply comes in, or during the scenario of reduced market demand, one need not procure when the inventory reaches the min because the company can afford to wait further. In case of high demand fluctuations, like at the end of the supply chain, the minmax does not seem to provide adequate protection while, it can also lead to higher inventory from time to time. In many organizations, 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 so many weeks of inventory. 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 overall inventory policy leads to a decision where 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 where a distributor is a few hours away from the company warehouse but has a few weeks or even a few months’ stocks while still suffering from stock-outs. In most organizations, the inventory is set, thinking only about the demand without considering 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.

The Solution: Simple yet Powerful!

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:

  • Replenishment lead time
  • Demand during lead time
  • Variation in supply time, and
  • Variation in demand

Of all the above variables, replenishment lead time is the most important as it impacts all 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 important to focus on one variable – the replenishment lead time. If we are able to reduce the replenishment lead time significantly, we can manage with much less inventory, and forecasting accuracy will improve significantly with lower lead times.

To reduce supply lead time in a distribution chain, one needs to understand the components of the replenishment lead time. The components are:

  • Order lead time (time till an order is placed for an SKU)
  • Production lead time
  • Transportation lead time


Reducing the Order Lead Time

The above diagram shows that the order lead time is a significant part of the total lead time. The min-max ordering systems increase the ordering lead time, as one has to wait till the level reaches the reorder point before placing orders. Many organizations 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 systems, we can go for daily ordering per SKU – we need not optimize by clubbing orders per SKU. Does it mean that the supplier has to ship out more frequently with partial loads? No. If there are 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:


Managing with a Lower Inventory

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.

Managing Exceptions with Buffer Management

Even though there is supply based on consumption, there is a chance that between two supplies, the inventory might fall down 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.

  • The target level of every item, at any location, is a buffer.
  • Buffer status measures how much stock does NOT reside at a location compared to the target level.
  • It is defined as the percentage of (Target Level – On hand) to the Target Level.
  • What is missing from the on-hand stock should be somewhere in the distribution chain from the source to the target.

When the stock at the target is more than two-third of the target-level, the buffer is considered to be Green, meaning it has too much stock. When the stock at the target is between one-third and two-third of the target level, the buffer is considered to be Yellow, implying that the stock level is OK. When the stock at the target is less than one-third of the target-level, the buffer is considered to be Red, which means that things are NOT OK. There is a real risk of running out of stock and a quick replenishment is required.


Changing the Target Levels

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, there is a time to look at reducing the norms. This dynamic buffer management system helps align stocks based on demand situation per SKU per location. The decision is much better than the current practice of just arbitrarily cutting stock norms. When one cuts arbitrarily the stock norms, they usually reduce the fast runners leading to more stock-outs. Dynamic Buffer Management prevents such ad-hoc decisions.

Benefits of Aggregation – The Plant Warehouse

In most push-based supply chains, bulk of the inventory is close to the demand point, where demand fluctuations are most erratic. Sales target based on primary sales pushes inventory close to the demand point. This leads to stock-outs of an SKU in one location in the distribution chain which may be in excess in another location.

Many organizations 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 having a skew of inventory at a point close to supply where demand is flat and stable. A plant warehouse with a bulk of the inventory feeding the regional warehouse should solve the problem to a large extent. The plant the warehouse will decouple the regional warehouse from production fluctuations. The inventory at 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 the overall inventory levels in the supply chain, as fluctuations are less at the mother warehouse level than regional or the retail level. The demand points in the distribution chain are free from production fluctuations, so they can manage with much lower inventory than they have currently. The supply to each inventory location is based on consumption. For example, plant warehouse will supply the regional warehouse based on consumption, similarly, the plant will produce based on consumption in plant warehouse.

The Expected Benefits

The stock-outs 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 to 40% after the implementation of the replenishment solution. The inventory drops drastically while the dealers’ ROI goes up significantly as they manage business with much wider spread of SKUs without any stock-outs.

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