The management practices in modern inventory management solutions began to develop during the industrial revolution in the late 1800s. Soon after inventory management training consultants developed programs to educate distribution managers. In the software era Gordon Graham paved the road for today's inventory management consultants. Graham's videos and books remain popular primers: Distribution Inventory Management for the 1990s (1987) and a companion volume, Distributor Survival in the 21st Century (1992).
Charles Bodenstab views order history as an inventory management database and brings to it the power of statistical analysis. Many of his methods are coded into wholesale distribution systems and all of them into his own comprehensive Mars-95 inventory management software that can be integrated to any ERP package. Bodenstab explains high-end practices in his book, A New Era in Inventory Management for the Distribution Industry (1993).
Jon Schreibfeder explains multiple techniques for each inventory management topic in a well-organized reference manual, Achieving Effective Inventory Management (third edition 2005), which Schreibfeder continues to improve in revised editions. The goal here is to introduce the techniques managers will find in their inventory management software and these three resources for further learning.
The 80 / 20 rule -- 20 percent produces 80 percent of the results -- applies to inventory and to customers. Develop an authorized stock list for each warehouse and procedures for adding and removing items. In general, top selling inventory items are stocked, slow movers special ordered. Sales people should confer with the best customers to ensure their needs for in-stock items are stocked. Lesser customers can special order or even look elsewhere once in awhile. Look for opportunities to drop ship.
To reduce damaged and pilfered inventory, run a clean, well-organized warehouse. Establish a cycle-count schedule. No forecasting and replenishment system will work well if the quantities in the computer are not accurate. Accuracy is improved over manual procedures by using scanners for shipping and receiving.
When branch locations do enough business to be able to buy at the same price as the main location, then the branches should do so. For items that can only be purchased at a competitive price by consolidating orders across the company, like slow moving items, they should be purchased by the main location and transferred to the branches.
Use sales history to measure demand for forecasting, not item requests or demand data. Adjust sales history to exclude unusual orders. Bodenstab recommends an automatic filter on sales history of four times the mean average deviation between the forecast and actual usage (explained below).
Graham offered two forecast methods, one for non-seasonal items and one for season items. For non-seasonal items, use a simple average of the usage for the last six months. For seasonal items, use a simple average of the next six months from a year ago and adjust the number with an estimate about how this year will vary from last year. Bodenstab and Schreibfeder expand simple averages with weighted averages (or exponential averages) to give greater weight to more recent or to more seasonally-relevant demand. Bodenstab continually refines the forecast by capturing the forecast error each month to calculate the actual mean average deviation. The result is also used to filter sales history automatically.
Without consideration of other factors, the optimal inventory level is achieved by ordering as often and as little as possible. An order is placed every day to cover demand the day the inventory arrives. Even for non-seasonal items, however, there are other factors to consider. Demand is not constant. There are costs associated with processing orders. Vendors sell only in case or pallet quantities or have minimum order quantities for a total order. Then there are tiered price breaks or the best price or freight rate for combined orders that fill a truck or container. As a result of these and other factors, producing a good demand forecast is only the beginning of calculating an optimal order quantity.
Since the EOQ formula was developed in 1904 a number of variations and abbreviations have evolved. The idea takes into account the costs associated with placing, processing and paying for an order. The goal is to find the sweet spot between the reordering cost (R cost) and the carrying cost (K cost), a point referred to as the lowest total cost of inventory. Carrying costs refer to all the costs associated with stocking the inventory: the costs of warehouse space, of moving the inventory within the warehouse, of the opportunity cost of the money used to purchase the inventory, of insurance and taxes, of taking inventory and of inventory shrinkage. For inventory held for one year carrying costs are usually twenty-five to thirty-five percent. On the one hand, the more stock ordered per line item, the lower the reordering costs per item. On the other hand, the less stock ordered per line item, the lower the carrying cost per item. In use EOQ calculations insure inventory making up a small portion of sales will not get too much attention but be ordered infrequently in large quantities. Inventory making up a large portion of sales will be managed tightly and ordered frequently in small quantities.
The order point is the daily usage rate times the number of days lead time plus safety stock to accommodate unevenness in demand. Graham recommends putting the safety stock at an additional fifty percent. Bodenstab recommends multiplying the mean average deviation by a safety factor to produce a desired order fill rate. For example, for a 90 percent order fill rate the mean average deviation is multiplied by 1.6, for 95 percent by 2.0, for 97 percent by 2.4, and for 98 percent by 2.6. Key inventory items are set at higher target fill rates than less-important inventory items. Bodenstab's calculation involves not just lead time but also an estimate of order frequency. Schreibfeder explains a number of methods to determine safety stock.
Line point is the order point plus the amount of inventory expected to be sold during the order lead time. When an order is being placed, any item whose stock level is below its line point should be ordered. Bodenstab's reorder target, a similar concept to the line point, again incorporates an estimate of order frequency into the equation.
Minimums and maximums are used in many ways and, in some software systems, not at all. In some software systems the minimum is the same as -- or one more than -- the line point or reorder target. For items with sporadic sales, the minimum might be the size of a typical order. Maximum represents the most that should ever be in stock.
Buying more of an item to reduce the purchase price or freight cost also has the negative effect of increasing the carrying costs. If an item stocked for a year has a carrying cost of twenty-four percent, then the carrying cost of an item stocked for a month has a carrying cost of two percent. The optimal level to buy is where the sum of the purchase price and freight cost plus the carrying costs result in the lowest total cost of inventory. For example, a price discount of three percent that results in an extra month of inventory being ordered at a two percent carrying cost is a good value.
Many software systems forecast demand, balance EOQ and line point equations, then modify the results to fit a price-level goal or a full truck while taking into account the case or pallet quantities required by the vendor.
By bringing inventory under good management the result should be overall inventory levels decrease while order fill rates increase. However, in some cases, like an economic downturn, it might be smart to reduce both inventory and order fill rates. Graham recommends reducing the overall inventory by using a higher number for carrying cost, perhaps ten or even twenty percent higher. Bodenstab would reduce the target order fill rates. If the goal is improved order fill rates, then the opposite measures are called for.
What to stock should be determined by market forces as described above. There is a bean-counter method of validating these market-based decisions. To learn more visit the GMROI definition at the Multiport dictionary.
There are many other specialized topics of interest to inventory managers. Becoming familiar with these three inventory management gurus will provide a ready reserve of tools for special situations.