A new inventory management paradigm for made to stock companies

Puneet Kulraj

new inventory

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.

  • 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
  • Every order need not be matched by a back-to-back delivery
  • 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

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 & 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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