On the left (in green) is the ratio of the standard deviation of the mean to the average of the mean. The higher that percentage, the more variable is the demand. The other indicator that you see is the number of days in the year that the item had activity. If you look at the very first item, it had 330 days in the year with activity, and the standard deviation of demand over the average demand is 100 percent which means it is one. Based on what you see in that graph, what is the relationship between the popularity of the item and the demand variability? It sounds like a good quiz question. All right, fill in the blank. Items that are very popular have a higher or lower demand variability than items that are hardly ever ordered? Lower demand variability and usually a higher forecast accuracy because you get to see the demand more often, so you have a better chance to predict it. Suppose you are a basketball team and you are in a conference. For example, in the ACC Georgia Tech may play Carolina three, sometimes four times in the year. They play them at home, they play them away, they may play them in the ACC tournament, and they may play them in the NCAA tournament. How well do you think Georgia Tech can forecast what Carolina is going to do in a game by the time they get to the NCAA tournament? They know exactly what they are going to do. They can call the play for the other team.
Demand Variability Profiling
We frequently find it helpful to identify and rank order root and systemic causes of excess inventory. An example completed for a large HVAC client is depicted below. In this case the root causes in
The RightStock™ model also distinguishes between value added inventory (VAI) and excess, non-value added inventory (NVAI). Value added inventory is the sum of safety stock, lot size, and pipeline inventory. Those three types of inventory
Hedge inventory (HI) mitigates risks of potential sharp price increases, shortages in critical commodities, and extreme price and availability volatility for those same items. Fuel is a classic example of a commodity whose inventory may
Contingency and disaster inventory (CDI) insures against unexpected situations outside the realm of those covered by traditional safety stock inventory. Those situations include natural disasters, labor strikes, and other abnormal supply chain disruptions. For example,
The shortage factor is the % of an item's unit selling price (USP) that is lost in the event of a stockout and subsequent lost sale. It is used to compute the lost sales cost. For example, if the
Setup cost (SUC) is the cost to setup (prepare or changeover) a machine or production line to make a production run for a particular item or change between items. It is sometimes referred to as changeover cost (COC).
The purchase order cost (POC) is the cost of placing a purchase order from a vendor. The majority of those costs are related to sourcing, purchasing, and procurement salaries and benefits (italics) and include: Purchase
As a part of the National Science Foundation’s Japan Technology Evaluation Center I had the unique privilege to lead a major study for the U.S. government comparing U.S. and Japanese logistics systems. During the study
There are two conceptual models for inventory management – push and pull. The push inventory model is so called because the emphasis is on "pushing" speculative inventory, made-to-forecast (MTF) in response to forecasted demand, out
Order status communication should be proactive when there is an exception to the order contents, timing, or terms agreed upon at order entry. Order status information should be updated in real-time and should be available