Any business that 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 level. 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 between keeping more inventory to protect sales and keeping fewer to control costs (and other costs associated with high inventory, i.e. obsolescence). The conventional approach to solving 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 company’s working capital requirements. So, what we need is a breakthrough that provides adequate protection with lower inventory. We know the level of protection is highly dependent on a combination of two important factors: the sales rate and the supply lead time. So, the only way to reduce inventory while providing protection is to shorten the supply lead time. Many customers feel that reducing suppliers’ lead time is not within their control, but the fact remains that the bigger part of the supply lead time has nothing to do with the supplier’s manufacturing lead time. It concerns with the customer’s ordering behaviour.  Over the years, the min-max and reorder point (ROP)-based ordering mechanism has become well entrenched in the mental models of most managers. The problem of erroneous ordering behaviour is institutionalised in the ROP model.  To analyse the problem with the ROP systems, we need to understand their 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 optimises between the need to lower purchasing (or production) costs and the need to lower inventory carrying costs. 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 demand will deplete the stock at the same rate as in the original calculations while the order is being processed may be grossly flawed. It can so happen that the actual demand has slowed significantly, and the time to deplete the safety stock during the supply lead time has increased many times over the actual supply lead time. So, there is no need to even place an order at the ROP, since stock is moving very slowly.  On the other hand, waiting for the ROP may be too late when demand is moving at a much faster rate (than initially calculated). The chances of a stockout are very high as stocks are likely to be wiped out much faster during the supply lead time (the calculated minimum will not provide the required protection). So, when demand is highly variable, ROP-based ordering behaviour neither provides protection against stockouts nor prevents unwanted inventory.  In many cases, the EOQ does not lead to a significant decrease in costs, either in ordering or in the goods ordered. The damage it causes is quite significant; it delays ordering and limits the system’s ability to respond to a sudden upsurge in demand or exposes it to a temporal deterioration in supply lead time, thus leading to a possible loss of sales. The only way out is to have a system of ODRF (Order Daily and Replenish Frequently) as a norm, set to the maximum possible consumption during the supply lead time. Or, in other words, we have a system where the minimum equals the maximum. 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>Ordering 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 longer lead time is fine, but if there is a stockout, a much shorter lead time than the standard is preferred. </li></ul> So, what the customer wants from the supplier is not delivery at a fixed lead time but a commitment to prevent stockouts for the customer. These are two different commitments with differing paradigms of behaviour expected from both supplier and customer. For example, the supply lead time can be longer when stock levels are high, and vice versa. At the same time, when the customer is translating daily consumption information to the supplier, the batching decisions are not set by the customer; they are best left to the supplier. In most supply chains, the actual batching considerations involve combinations across SKUs in both production and transportation, so there is no need for the customer to upfront batch 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 at a fixed time and the supplier is given the flexibility to respond, he can use consumption information to determine his batching requirements. For example, many SKUs can be sent in a single dispatch container rather than in big batches of a few to fill it up. Or a supplier can batch small requirements of one customer with another customer to create his production batch requirements.  As customers, we need to provide complete transparency into the stock situation on a daily basis, which, in turn, will reveal the relative urgency of each SKU’s requirement.  When SKUs are too many, we need colour-tagging systems to quickly assess the adequacy of stock levels across SKUs. For example, if the stocks are too low (zero to 1/3rd of the norm), they can be tagged as Red; and if they are reasonably high (between 2/3rd of the norm and the top of the norm), they can be tagged as Green; and the middle band can be in Yellow. A supplier receiving multiple orders across SKUs can prioritise the orders and make the best decisions to optimise production and transportation while preventing stockouts 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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Problem: Businesses struggle with stock management, often facing either stockouts or excess inventory. Traditional methods like reorder points (ROPs) assume steady demand, leading to errors in inventory levels and unnecessary costs or missed sales. Solution: Implementing an Order Daily and Replenish Frequently (ODRF) based on real-time consumption data helps businesses align stock levels with actual demand, eliminating the need for fixed reorder points and reducing unnecessary inventory. Benefits: The ODRF system reduces inventory costs by eliminating excess stock, prevents stockouts by aligning inventory with demand, and enhances supplier flexibility. It also fosters stronger, long-term partnerships by shifting from transactional to collaborative relationships, benefiting both businesses and suppliers.

Key Takeaways

Effective inventory management is vital for businesses. Traditional reorder point (ROP) systems often fail to adapt to demand changes, causing shortages or surpluses. An innovative daily ordering system is proposed that can align seamlessly with market demand while reducing overall inventory across the organisation.