A business engaged in managing stock to meet customer needs faces key challenge in stock management – how much to
hold, in other words, what is the right inventory. It seems 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 is to have a compromise between holding low inventory at month end (to
ensure good reporting) and high inventory in between. However, this approach has no impact on managing the working
capital requirements of a company. So, what we need is a breakthrough that provides adequate protection with lower
inventory levels. It is well known that the level of protection is very much dependent on the combination of two important
factors – the rate of sales and the supply lead-time. Therefore, the only way to reduce inventory while protecting sales, is to
reduce the supply lead-time. It is common notion that reduction in supplier’s lead time is not within the locus of control for
a customer, whereas the supply lead-time has nothing to do with the supplier manufacturing lead-time. Actually, the supply
lead-time is related to the ordering behavior of a customer.
Over the years, Min-Max and Reorder Point (ROP) based ordering mechanism have fascinated majority of the managers, giving rise to a problem of erroneous ordering behavior, which has institutionalized the ROP model. To analyze the problem related to the ROP systems, one needs to understand the basic Principles of the ROP system. In the ROP system, 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, which is the optimum level between the
requirement to lower purchasing cost (or production costs) and the requirement to lower the carrying cost of inventory. If
the EOQ ordered arrives instantly, then “maximum” inventory for that item is reached.
A significant drawback of this model is it assumes that the demand remains constant. During an order processing, the
demand depletes the stock at same rate as per the original calculations is a flawed assumption. It is likely to happen that the
actual demand can go down significantly while, the time taken to deplete the safety stock during the supply lead-time may
go up several times the actual supply lead time. In this scenario, there is no need to place an order at the reorder point level,
as inventory moves at a very slow rate.
On the other hand, waiting for re-order point may be too late when demand is moving at much faster rate (than initially calculated) and chances of 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 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. 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 a ROP system.
1. Order of small lots as per daily consumption does not inflate costs. It is just transmission of consumption information on a daily basis. Any existing IT system can help transmit this information.
2. Every order need not be matched by a back-to-back delivery.
3. Every order need not be delivered in a fixed lead time – when stock are adequate a higher lead time is fine but if there is stock out, much lower than standard lead time is preferred.
So, it obvious 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 of an SKU for a supplier. A customer cannot and need not try to create batching for the supplier.
When a SKU is not required to be delivered at 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 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 stock situation on a daily basis, which in turn will provide the relative urgency of requirement for every SKU.
When SKUs are too many, we need color tagging systems to quickly conclude on adequacy of stocks across SKUs. For
example, if the stocks are too low (about zero to 1/3rd of the norm) it can be tagged as Red and if it is reasonably high
(between 2/3rd of the norm till top of norm) it 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 best decisions which helps him to optimise his production and transportation while ensuring the stock outs are prevented at the customer's end. But , in many environments ,the rate of sales changes over a period of time, so there is a need to frequently adjust the norms (or the buffer) as per the Process of Dynamic Buffer Management (DBM). When sales rate is higher than supply rate, we expect stocks to be consistently remain at low level (red level). A continuos red level stock is an indicator to increase the norms. If items stay in Red for about one replenishment time, we can increase norms by 1/3rd. Similarly, when the sales rate is lower and the items stay in Green for long( about 1 or 2 replenishment periods) , the norms can be reduced by 1/3rd. The daily DBM process will help aligning inventory with sales rate and prevent stockouts and surpluses.
The system of replenishment 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 treats 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.
Puneet Kulraj is the Founding Director of Vector Consulting Group.
Vector Consulting Group (www.vectorconsulting.in ) is the leader of ‘Theory of Constraints’ consulting in India. Vector has been working closely with some of the well-known retail chains, FMCG, fashion products, custom manufacturing industry and auto after market companies to improve their overall profitability through supply chain effectiveness.