Calculating Average Daily Demand, Not a No-Brainer

Lean is largely about satisfying customer requirements. That’s near impossible if the lean practitioner does not understand demand. In fact, misunderstand average daily demand (ADD) and the impact can be significant – inaccurate takt times, improper demand segmentation, poorly sized kanban, incorrect reorder points, etc.

Calculating average daily demand can be deceptively complex. There are a handful of things to consider.

  • SKU and part number versus product family. Kanban is applied at the SKU and part number level, so ADD must be calculated at that level as well. When calculating takt time, ADD 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 is: 1) capped by internal constraints, whether capacity or execution related, leaving unmet demand (that may or may not be fulfilled by competitors or may become backordered), or 2) artificially inflated due to overproduction, purchasing of excess stock, etc. 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 over-purchasing, identify the real demand and use it.
  • Historical versus forecasted demand. If forecasted demand is different than historical and the lean practitioner has faith in the forecast accuracy, then forecast should be used to determine ADD (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 ADD or/and demand variation may very well contain abnormal data. If it is 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 that 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/or other significant variation. This is important for the calculation of both ADD and demand variation. The historical horizon often should be as much as 12 to 36 months, with forecasted future horizon 3 to 18 plus 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 ADD. 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, inflection points for seasonal demand changes, etc. Furthermore, smaller buckets are required for calculating statistically valid demand variation (really, the coefficient of variation (CV)).
  • 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 ADD and a lower CV versus including zeros and calculating a lower ADD and a higher CV. The excluded zero approach will more likely ensure that the kanban can meet the spikey demand, but at a price…more inventory.

Any thoughts or war stories?

Related posts: Does Your Cycle Time Have a Weight Problem?, Musings About FIFO Lane Sizing “Math”

There are 3 Comments

markrhamel's picture

Hi Larry,

Thanks for the comment. Excellent point! Customer returns can definitely distort demand history.

Best regards,

lmloucka's picture

Another data issue is that of customer returns which may show up as negative numbers or not show up at all in the transaction history.