APICS is the premier professional association for supply chain management.

The Seven Deadly Sins of Sales Forecasting

By Fred Tolbert, CPIM, CSCP | N/A 2012 | 7 | 3

Lust, pride, greed, gluttony, envy, anger, and sloth. Collectively, they are known as the seven deadly sins. If you are guilty of one or more of them, it is said that a guy with a pitchfork and pointed tail has a spot reserved for you surrounded by fire and brimstone. Sound bad? You bet.

Perhaps not as bad__but certainly something worth considering for any supply chain and operations management professional__are the sales forecasting seven deadly sins. These activities contribute to increased stockkeeping unit (SKU)-level sales forecast error, and the consequences are the supply chain equivalent to fire and brimstone: increased sales forecast error, excess inventory, and missed customer due dates.

Deadly sin #1: Using shipment history. Sales forecasting systems use sales history data to generate the statistical forecast for future periods. The key issue is the type of sales history used to run the statistical forecast__shipment history or demand history.

Let's say your customer placed an order for 1,000 units of Item 12345 for delivery in July. Item 12345 is on backorder, and you are unable to ship the product until September. Does your enterprise resources planning system post the sales history as July demand (when the customer wanted the product) or September demand (when you were able to deliver the product)?  

If the answer is that your system posts the history as September history, your forecasting system will use 0 units in July and 1,000 units in September to drive the statistical forecast. Using shipment history will perpetuate your backorder situation. The appropriate procedure is to post the 1,000 units as July history for sales forecasting purposes.

Deadly sin #2: Relying on bad data. Congratulations if you avoid the first deadly sin and use customer demand data as the basis for generating the statistical forecast. However, the demand data can still be polluted with the effects of one-time or non-recurring orders that can lead to inaccurate statistical sales forecasts. Examples of the bad demand data that get posted to the demand history file include

  • sales due to promotions that will not be repeated in the same period next year
  • spikes in demand due to special, one-time customer orders
  • pipeline fill orders by big-box retailers
  • special orders due in advance of quarter-end price increases
  • using customer-specific demand data that are too granular to be statistically significant
  • unit of measure conversion issues, such as when items are sold as both individual units and three-packs.

It is necessary to periodically scrub the demand history file in order to eliminate the effect of the special orders. Some systems automatically filter demand history values that are outside of a statistical confidence interval. Others identify the exceptional demand and rely on the user to determine how to adjust the sales history and eliminate the bad data. The key is to include data scrubbing as part of your regular demand planning process.   

Deadly sin #3: Excessive "gut feel" overrides. Many companies commit hours or days of effort each month to review and adjust the system's statistical forecast. The sales forecast sometimes travels from the forecast planner to the sales team to the product management team, with each level making their adjustments to the sales forecast. Too often, planners base forecast adjustments on a feeling and not specific knowledge of customer activity. Raise a red flag if you feel compelled to override more than 10 to 20 percent of the system's statistical forecasts. 

Use the system's statistical forecast as the starting point for making forecast adjustments. The rule of thumb should be to adjust the statistical forecast only if you know something about the future that is not reflected in the demand history. Otherwise, resist the urge to adjust the forecast just to make it look pretty.

Deadly sin #4: Poor event planning. Some companies do the opposite of the third deadly sin; instead, they don't make enough forecast overrides when special events are scheduled to occur. Poor event planning often is the source of missed customer due dates, product expedites, and excess inventory.

The typical special events that occur that do not have the benefit of being reflected in past sales history include new item introductions, promotions, item substitutions, and item replacements. It is imperative to develop an internal collaboration process that brings together all the individuals responsible for planning for the impact of special events. There also needs to be frequent review of how actual sales are performing versus the forecast to ensure that the right level of inventory is available to meet the special event needs.   

Deadly sin #5: Senior management meddling. If your weekly or monthly demand planning meeting involves the company president, CFO, vice president of sales, or vice president of operations, it is almost certain the fifth deadly sin is going to occur. Senior executives have no business exerting their bias or influence on the SKU forecasts.

The sales forecast should be the company's best estimate of customer demand. It is inappropriate for executives to adjust the forecast as a means of manipulating inventory levels and fill rates. Inventory and service levels are best managed as part of the supply planning and inventory replenishment processes. Unfortunately, this sin is the easiest to recognize and the toughest to do anything about.

Deadly sin #6: Failing to measure sales forecast accuracy. It's crucial to measure forecast error and understand the root causes of high forecast errors. Some basics of sales forecast accuracy reporting include the following:

  • Measure forecast accuracy at the SKU and product family levels.
  • If your products have long lead times, account for the lead time lag in the sales forecast accuracy reporting.
  • Measure which is more accurate__the system's statistical forecast or the planners' override forecast.
  • Determine if forecasts are consistently too high or too low, indicating a bias in the statistical forecast or in forecast adjustment.
  • Perform root cause analysis on items with high forecast errors to learn the real reasons for the forecast error.

Deadly sin #7: Safety stock based on forecast error. Safety stock inventory exists to cover periods when actual demand is greater than the forecast. It is a common system feature to compute SKU safety stock quantities based on forecast error and a desired customer service rate. Just plug in a 98 percent service rate, and let the system compute the safety stock quantity. Sounds too good to be true, right? Yes__until you look at the math of the traditional safety stock calculation.

The traditional safety stock calculation based on forecast error does not distinguish between periods when the forecast is too high and too low. But if we have forecasting processes that contribute to forecast error and the technique that uses forecast error results in excess inventory, why would we use it? Consider using new statistical modeling techniques that eliminate the bias of periods when the forecast error is the result of the forecast greater than actual. Or try an inventory planning strategy based on safety time rather than the fixed safety stock calculation that uses forecast error.

The seven deadly sins of sales forecasting contribute to increased forecast error, increased inventory, and lower customer service levels. Avoid them, and may find yourself in sales forecasting seventh heaven.

Fred Tolbert, CPIM, CSCP, has 25 years supply chain management experience. He is principal of Southeast Demand Solutions, a reseller of the Demand Solutions suite of demand planning software. He is an active APICS volunteer, serving two terms as president of the Atlanta Chapter and recently completing his term as the APICS Southeast District Director. Tolbert may be contacted at +1-770-565-8498.

All comments will be published pending approval. Read the APICS Comment Policy.


  1. Abdelghany Eladib October 06, 2014, 12:15 AM
    Great article.  I am about to lead a project on regional forecasting in my company, any advice where and how to start
  2. Brian July 12, 2016, 05:08 PM
    In reference to "sin" 1, how do you handle advance orders and cancelled orders? You would have to continuously revise historical forecast error, right? Not to mention you introduce human error of dating an order consistently and correctly on when the item is needed. Recommendations?


  1. RadEditor - HTML WYSIWYG Editor. MS Word-like content editing experience thanks to a rich set of formatting tools, dropdowns, dialogs, system modules and built-in spell-check.
    RadEditor's components - toolbar, content area, modes and modules
    Toolbar's wrapper 
    Content area wrapper
    RadEditor's bottom area: Design, Html and Preview modes, Statistics module and resize handle.
    It contains RadEditor's Modes/views (HTML, Design and Preview), Statistics and Resizer
    Editor Mode buttonsStatistics moduleEditor resizer
    RadEditor's Modules - special tools used to provide extra information such as Tag Inspector, Real Time HTML Viewer, Tag Properties and other.

Please log in to see content on this page

This page article is available to APICS members and APICS magazine subscribers only. APICS members and APICS magazine subscribers: Please log in at the top right of this page to view this content.

Not an APICS member? Join today to receive instant access to valuable APICS member benefits like this.