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GREAT SOFTWARE DOES NOT MANAGE INVENTORY EFFECTIVELY

            The most expensive, sophisticated software package will not automatically result in an optimal level of inventory – a high level of customer service and inventory turns, but with low inventory investment. To achieve an optimal level, and maintain it, employees educated in the principles of effective inventory management must understand how to set certain “parameters”, then set them and keep them set right. Here are some tips about principles of inventory management and setting key parameters.

            The Basics. Too often I work with users who have been well-trained in the rote mechanics of using the ERP software package that literally runs the distributorship, but were not educated in modern inventory management principles. Important principles include the relationship between customer service level and inventory level, and the meaning of the “normal statistical” distribution (bell curve) that plays a role in the calculation of safety stock and the adjustment of data on odd sales events. A principle related to purchasing, and so indirectly to inventory management, is the “Line Point” (LP), which is not another term for Order Point (OP). The LP is the OP plus sales forecasted for the buying cycle (time between buys). Items that are above their OP but below their LP should be purchased only when those items are needed to make a purchase minimum or would result in a purchase discount that would be larger than the cost of  inventorying those items for longer than usual.

Another basic that is sometimes skipped is the setting of parameters. When the system was first installed, the users were too busy to determine what values to set parameters to, so the system went live with “default” values (on average good for all distributors, but not good for any specific distributor). And, of course, these users are still too busy to investigate the values and change those that are not right for the company – if they could even do so without first learning the principles of effective inventory management.

Qualifying Historical Data. Although most systems adjust historical data to remove oddities before using the data to forecast future sales, the scope and amount of an adjustment depends on the values of certain parameters. In addition to the common oddities of unplanned sales “spikes” and “dips”, there can be periods of no sales (perhaps due to stock outs, perhaps not), sales spikes caused by promotions (perhaps followed by decreased sales), and sales dips that reflect a large quantity of returns; and more. The values of parameters determine whether an oddity will be adjusted, and the extent of that adjustment (and that extent may increase or decrease with the size of the oddity). Users should not set these parameter values until they understand the specific oddities of the distributorship’s sales and how those specifics relate to the available parameters.

            Forecasting. Many systems come with several different formulas for forecasting future sales (by using history). One of those formulas is the “default” – the one that will be used unless someone selects another formula. Life would be easy if one formula, the default or otherwise, could be used for all items, but that is very rarely possible. Even the use of one formula for all items in a particular product line would save time, but that too is seldom possible – because every line has its slow moving items, and they cannot accurately be forecast with the same formula that works well for fast moving items. As with the setting of most parameters, it is necessary to select different formulas for different items – unless the system can automatically select the “best” formula (based, of course, on parameters that define “best”). Formulas that are easy to understand but not accurate include averaging and weighted averaging (where users set the weights – emphasis factors.) Wherever possible, use the more sophisticated formulas, even though someone still needs to set related parameters.

            And if a system measures the accuracy of an item’s forecast (sometimes called the Mean Average Deviation, or MAD), accuracy reports should be reviewed quarterly – to determine if parameters should be changed or a different formula used.

                        EOQ is Dead, Long Live EOQ. For some items, EOQ (Economical Order Quantity) is inaccurate; items with a very low unit value relative to the cost of procurement, and items that sell infrequently. For these kinds of items, EOQ would calculate a multi-year supply or a quantity of zero, respectively. A better way to handle both kinds of EOQ-inappropriate items is to use the dynamic Min/Max feature of the system, whereby the system uses history to determine the values of Min and Max. But before doing so, research the system’s Min/Max formula, and determine what the Min/Max parameters should be and if Min/Max would produce realistic results. Avoid using manual Min/Max because it is not dynamic, and so takes a lot of effort to keep up to date as sales patterns change.

            Safety Stock. Safety stock can account for a large portion of an item’s quantity on hand, and for too many items, the quantity on hand is seldom less than the level of safety stock – which means that the safety stock is not being sold, and is dead inventory. One reason that related parameters are sometimes set wrong is that some people do not understand principles for calculating safety stock: 1) safety stock is kept in case sales exceed forecasted sales; 2) the level of safety stock does not depend on an item’s velocity; 3) the level of safety stock for an item should be mainly in proportion to the volatility of its activities; 4) the level of safety stock should be based on the item’s target service level.

            Lead Time. Lead time may be the most difficult value to determine, because it is basically beyond a distributor’s control; and because some lead times are seasonal, even though sales of the items are not. But that is no excuse for not examining the default values of related parameters – which are often set with the assumption of constant lead time. Where a system contains optional sophisticated formulas for calculating lead times, those formulas should be investigated, compared to vendor performance, and used wherever possible. Even if there are no sophisticated formulas, related parameters should still be set in the context of vendor performance.