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Lean is largely about satisfying customer requirements. That’s nearly impossible if the lean practitioner doesn’t understand demand. In fact, misunderstand average daily demand, and the impact can be significant, including inaccurate takt times, improper demand segmentation, poorly sized kanban, and incorrect reorder points. Calculating average daily demand can be deceptively complex. Here are a handful of things to consider.
SKU and part number vs. product family.Kanban is applied at the SKU (stock-keeping unit) and part number level, so average daily demand must be calculated at that level as well. When calculating takt time, average daily demand is often, but not always, determined at the product family level, or at least the group of products or services that are produced or delivered within a given line, cell, or team.
True demand. Do not blindly accept what was sold, produced, processed, purchased, or issued as true historical demand. Often this demand can be:
• Capped by internal constraints, whether capacity- or execution-related, leaving unmet demand, which may or may not be fulfilled by competitors or may become back-ordered
• Artificially inflated due to overproduction, or purchasing of excess stock
If the barriers to constrained demand will be addressed in the near future, then include both historical met and unmet demand. In the area of overproduction or overpurchasing, identify the real demand and use it.
Historical vs. forecasted demand. If forecast demand is different than historical and the lean practitioner has faith in the forecast accuracy, then the forecast should be used to determine average daily demand (with historical most likely used to determine demand variation). Otherwise, use historical demand.
Abnormal historical demand. Historical demand, whether considered for the purpose of determining average daily demand or demand variation, may very well contain abnormal data. If they are significant and there is a reasonable probability that something of that nature and magnitude will not occur in the future (i.e., one-time order or marketing promotion), then it may be prudent to exclude those data from the analysis.
Demand horizon. Demand is rarely constant over extended periods of time. Narrowing the demand horizon will increase the risk of missing seasonality, cyclicality, and other significant variation. This is important for calculating both average daily demand and demand variation. The historical horizon often should be as much as 12 to 36 months, with forecast future horizon three to 18+ months. Statistically speaking, the practitioner needs ±25 data points to make valid calculations.
Demand time buckets. Clearly, the size of demand time buckets does not impact the purely mathematical calculation of average daily demand. However, the use of daily or weekly demand time buckets, as opposed to monthly or quarterly, does provide the necessary insight to visually identify abnormal demand and inflection points for seasonal demand changes. Furthermore, smaller buckets are required for calculating statistically valid demand variation (really, the coefficient of variation).
Number of operating days. “Average daily” presumes a denominator in days. The number of days must correspond to the number of operating days for the resource that is satisfying the demand. For kanban we have to remember that the resource is the “owner” of the supermarket.
Operating days without activity. Demand analysis will sometime reveal SKUs or parts that have days (or even weeks) that do not have any demand. This, by its nature, typically is indicative of relatively high demand variation. Depending upon the situation, the lean practitioner, when sizing kanban, may consciously want to include the zeros within the calculations or not (or not use kanban at all). For example, excluding zeros will drive a higher average daily demand and a lower coefficient of variation. The excluded zero approach will more likely ensure that the kanban can meet the spiked demand, but at a price: more inventory.
Any thoughts or war stories?