Inventory management is perhaps the defining element of supply chain management – where supply and demand truly meet. Getting that right not only reduces operating costs, but has a dramatic impact on cash flow and shareholder value. As such we have published numerous articles on the subject and will continue to do so.

The following will be of interest to SMBs in need of an up-to-date inventory management method that is relative easy to adapt and use on a frequent basis.

Is the Wilson Formula Still Viable?

Economic order quantity is the level of inventory that minimizes the total inventory holding costs and ordering costs. It is one of the oldest classical production scheduling models. The framework used to determine this order quantity is also known as Wilson EOQ Model or Wilson Formula. The model was developed by F. W. Harris in 1913, but R. H. Wilson, a consultant who applied it extensively, is given credit for his early in-depth analysis of it.

The Wilson formula remains a popular and traditional method for determining the buy order or production quantity if you know the total consumption during a period of time. The formula assumes that the only costs entailed are warehousing cost per SKU and a one-time cost every time an order is placed, known as administrative re-ordering costs. The formula tries to find an optimal balance between the two costs to minimize the total cost, which is known as the economic order quantity (EOQ).

In order for the Wilson Formula to work properly, a number of conditions have to be met:

• Demand is constant and continuous
• The lead time for receiving ordered goods is constant
• Administrative re-ordering costs and warehousing costs are constant
• The order quantity does not need to be expressed as an integer
• The entire order quantity is delivered to the warehouse on the same occasion
• No shortages allowed
• The price/cost is independent of time requirements and ordered quantity

Also, the formula doesn't take into account the one thing that really costs - handling.

Many attempts have been made to minimize the effect of the unreasonable conditions of the original formula. For instance, there are amendments that handle shortages, differences in lead time and differences in demand. The problem is that these new formulas require even more data that is hard to collect, and the results are often the same as with the original formula, while the amount of work spent on administration and gathering information is significantly increased.

Using the Wilson Formula is an example of sub-optimization, where the desire to reach an optimal solution to a local problem steals resources from the whole. As such, it is not always the best tool for inventory optimization for buyers who need a reliable and easily adaptable method for daily use.

Searching For a Better Inventory Management Tool

The following article on New Innovations in Inventory Optimization was recently submitted to Supply Chain Digest by Mr. Jyrki Salmivuori of Salmivuori Consulting (www.salmivuori.fi) located in Helsinki, Finland. Salmivuori is an independent consultant whose primary focus is on Inventory Management and ERP systems. Published in 2009, you can purchase a copy of Mr. Salmivuori book “Inventory Management for Small and Medium-sized Businesses” from the above web site.

New Innovations in Inventory Optimization by Jyrki Salmivuori

Stocks and purchases play a significant part in the results of companies. A loss in sales caused by shortages in storage not only has an impact on net sales and operating profit, but also on customer satisfaction and image. However, the capital tied to inventory produces costs and, particularly in the current economic situation, companies need to release capital to more efficient operations. In addition, there is always risk of products turning obsolete. The impact of the undervaluation of slow-moving products can be seen directly in the bottom line of the income statement.

Challenges for the Buyer

The ungrateful task of the buyer is to be able to purchase a sufficient quantity of the correct products so that the sales unit has items to sell but, at the same time, not too many products so that the total inventory value will not rise too high. Therefore, the essential question is “How much?”

According to textbooks, the calculation of the optimal order quantity (EOQ – Economic order quantity) is based on the optimization of holding costs and ordering costs. This calculation method, also known as the Wilson Formula, was developed in the 1910s. The formula works in theory and, with specific restrictions, also in practice.

An inherent requirement is that the demand for products remains fairly stable, ordering costs are independent of the batch size and the lead time remains unchanged. In addition, ordering and holding costs should be allocated to each product with sufficient accuracy. However, this presents challenges, because even in a small company there may be several thousand different products. Some products are easy to order and store, whereas others require a more complicated ordering process.

The calculation of an optimal order quantity (and, as a result, the allocation of costs to products) must be revised as often as possible because the company’s operating environment is constantly changing (i.e. new products are launched, old ones are removed, demand for products varies and there are changes in the company’s cost structure).

Even though the Wilson Formula is not a complex one mathematically speaking, it is too difficult for basic buyers, and produces somewhat unreliable results. Buyers, working in the cross-fire of sales and financial administration, must have full confidence in the purchase proposals produced by a system or calculation model. Otherwise, they will rely on a traditional four-function calculator and calculate the order volume manually.

Generally, a company’s purchase process is based on purchase proposals generated by an ERP system. The calculation of purchase proposals uses item-specific control parameters (e.g. order point). The order point can be calculated by adding consumption over the delivery time to the safety stock. The Wilson Formula pays no attention to the safety stock. It can be calculated through the mean deviation in deliveries using statistical methods. However, confidence presents yet again a problem. Does the buyer understand the principle for calculating the safety stock and does the principle produce sufficiently correct results?

Life Cycle-Based ABC+ Categorization

What could be a better method than the Wilson Formula? The recent trend has been to shorten the life cycle of products. This applies not only to mobile phones and digital cameras, but also to nearly all traditional business fields. When considering inventory optimization, the safety stock of “rising trend products” (that might be at the introduction stage or growth stage of their life cycle) can be maximized, whereas the safety stock of products that are approaching the end of their life cycle can be minimized.

How can this be carried out in practice? The answer is product categorization. In a traditional ABC categorization, products are divided into A, B or C categories on the basis of sales. The product category should indicate whether a specific product is at the initial or final stages of its life cycle. For this purpose, a product life cycle-based ABC+ categorization method has been developed for products. New and EOS (end of sales) products require separate categories. A rising trend, stable demand and decreasing trend can be represented using symbols '+', '=' and '-'. As a result, the ABC category of a volume product with a rising trend is ‘A+’. The category of a volume product with decreasing sales is ‘A-’ and that of a volume product with stable demand is ‘A=’.

A Min–Max range is specified for each ABC category within which the product’s stock balance must be kept. For example, the minimum level (i.e. safety stock) of an A+ product can be a quantity corresponding to the consumption of one month and the maximum level can be a quantity corresponding to the consumption of three months. Thus, the traditional ABC categorization has been replaced by the new, product life-cycle based ABC categorization.

It must be pointed out that this method is not aimed at optimising costs related to the ordering process. Still, it can significantly reduce costs arising from the undervaluation of obsolete products because the stock levels can be reduced in a controlled manner following the product’s life cycle. At the same time, the capital tied to the inventory can be reduced and product availability can be improved.

Final Thoughts

Optimizing inventory level is a high priority and critical to maintaining a healthy balance sheet. Companies should probably do ABC SKU activity profiling at least annually or even semi-annually in normal times. However, coming out of this recession and given the unprecedented changes in consumer demand, it is especially critical right now and perhaps should be maintained on a more frequent basis. The product life-cycle based analysis method described above should work well for companies (especially SMBs) looking for more clarity as to SKU velocity and appropriate min-max inventory levels.

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