How to Best Understand Demand Sensing and Demand Shaping

Executive Summary

  • Demand sensing and demand shaping are two methods that are often promoted to improve forecasting. However, one of these methods is barely ever applied. The other is not actually a way of improving the forecast but is presented as one by vendors.

Introduction

Demand planning always has new terms in supply chain forecasting that are necessary to keep up with. Two of the present ones are demand shaping and demand sensing. I decided to include both in an article because, for some time, I would get the two confused for one another, and it is important not to do as they are unrelated, except for being two terms which are currently popular.

Demand Shaping

Demand shaping is the process of creating incentives through with customers that smooth demand, or in eliminating pre-existing incentives such as promotions or end of quarter pushes which distort the demand history-making forecasting more challenging to perform.

Demand Sensing

Demand sensing is the use of a procedure to analyze the demand history to gain new insight as to how to develop a better forecast and to make changes in the short-term to the forecast.

This entry into Wikipedia on the topic is patently ridiculous.

“The typical performance of demand sensing systems reduces near-term forecast error by 30% or more compared to traditional time-series forecasting techniques. The jump in forecast accuracy helps companies manage the effects of market volatility and gain the benefits of a demand-driven supply chain, including more efficient operations, increased service levels, and a range of financial benefits including higher revenue, better profit margins, less inventory, better perfect order performance and a shorter cash-to-cash cycle time. Gartner, Inc. insight on demand sensing can be found in its report,” – “Supply Chain Strategy for Manufacturing Leaders: The Handbook for Becoming Demand Driven.”

This entry was apparently made by a software vendor selling demand sensing software. No forecasting methodology produces a reduction in forecast error by 30%. If the forecast error were 30%, this would mean demand sensing improved the forecast by 30 % x 30 % or 9 percentage points. I would very much like to see the hard data on those numbers. Secondly, what is near-term forecasting? Forecasting is produced long-term, and the sales orders exceed or are short of the forecast. I am not sure that “near-term” forecasting is a legitimate term. If the lead time is longer than the duration of the short-term forecast to sale, how to adjustments to the forecast help?

And this gets to the next topic.

Who Provides Information on Demand Sensing?

The information on the definition of demand sensing is currently and primarily controlled by supply chain software vendors. Demand sensing did not come out of the academic community, so there have been few unbiased descriptions of what demand sensing is. That is unbiased in that the definition is not promoted by someone trying to sell software. This is why I have developed the definition below:

“Demand sensing is the adjustment of forecasting inside of the lead time of the product, and therefore when the supply plan cannot respond. If our lead time is 2 weeks, then demand sensing means changing the forecast less than 14 days out. Demand sensing is the adjustment of forecasting inside of the lead time of the particular product, and therefore when the supply plan cannot respond.”

Because demand sensing changes the forecast within the lead time, demand sensing cannot be considered a forecasting approach.

To understand this, it is important to comprehend that while broadly speaking a forecast is a prediction of a future event, in practical terms a forecast is a prediction of a future event which is given with sufficient advanced notification to be worthwhile. For instance, a forecast could be improved for a football game by waiting until 1/2 the game is over. However, when half the game is over, it is too late to place a bet on the match.

What is and What is Not a Forecast

Therefore the forecast is not particularly useful because it occurs within the lead time of when some benefit from being received from it. The forecast for the game could be further improved by waiting until the minute before the match ends, but again it is hard to see how anyone would accept this as a forecast. One could not want to compare the prediction accuracy of a person who forecasts games while in progress versus those that forecast the game before the game begins. This, of course, brings up the topic of demand sensing and forecast accuracy “improvement.” Demand sensing does not help supply planning and does not affect inventory management. Furthermore, it reduces the data quality of the forecasting history.

I have a better definition of demand shaping.

“Demand sensing is the adjustment of forecasting inside of the lead time of the product, and therefore when the supply plan cannot respond.”

Therefore, demand sensing is not a forecasting approach. It is a method of falsely improving the forecast accuracy by changing the forecast in the way that can never translate into an improvement in supply chain performance.
Why Demand Sensing is Not a Forecasting Approach

The reason that demand sensing is not a supply chain forecasting approach is that makes changes to the forecast within lead times, and therefore when supply planning cannot respond. 

This is for a variety of reasons:

  • Skills
  • Software selection (many have selected the wrong software)
  • Their management does not understand forecasting
  • Many companies do not hire people with forecasting knowledge or an education in forecasting, often instead choosing to hire people out of finance.
  • Marketing has proliferated the product database with difficult to forecast product location combinations.
  • etc..

The list of basic things that most companies cannot do in their supply chain forecasting systems is often fantastic. A typical list would be:

  1. Can’t-do attribute-based forecasting.
  2. Don’t know or track their forecasting bias.
  3. Don’t know what part of their forecasting process contributes or detracts from forecast accuracy.
  4. Cannot manage their forecast parameters correctly.
  5. Measure forecast error in the wrong location.
  6. Have weak lifecycle functionality in their present application.
  7. Can’t perform software selection because their executives do not understand forecasting themselves and because they rely on consulting companies that choose their software-based on billing hour maximization.
The list would go on, but supply chain forecasting is in a terrible state at even the biggest companies in the US.

How Common is Forecasting Accuracy Falsification?

Incredibly common. It starts off with measuring forecast error at too high a level of aggregation, and at a level that means nothing for supply planning. Supply planning also fakes its numbers, which is typically some service level. Faking “99%” supply chain service level is natural and quite common. For instance, many companies maintain their supply chain service level only by deleting orders that they cannot meet. Demand sensing is brazen in that it pretends it is an actual forecasting approach. Companies across the country don’t know how to forecast, yet have accuracy targets they must meet. This is where demand sensing can come to the rescue.

Improve the Forecast or Fake the Forecast Accuracy with Demand Sensing?

Demand sensing is a very convenient tool, or but it could be used for changing the forecast at the last-minute, far after it has any relevance. This is similar to changing your “forecast” for the winner of a football game after three-fourths of the game is over. It no longer meets the definition of a prediction.

This brings up the critical topic of when forecasting changes can be made. As explained in the graphic above, after a certain point, changes are no longer part of forecasting/demand planning, and they do not improve the forecast for obvious reasons. 

The demand planning department will use this term to in effect fake out other departments that rely on the forecast into telling them that they are using a legitimate technique to improve “forecast accuracy.” In the short-term, this may work in departments that don’t understand forecasting. In the long-term, it won’t work.

Demand sensing is not a part of forecasting because it occurs after the forecasting period has passed.

Searching for Logical Consistency

The more I read on demand sensing, the less it makes any sense. Firstly, many of the things that are often touted as part of demand sensing should be performed by the regular demand planning system. Secondly, making short-term changes to the forecast introduces a significant amount of noise into the forecast. Forecasts need to be set and left unchanged. Sales orders may be higher or lower than the forecast, but the forecast is a value which is to be frozen.

Why Forecasts are Created in the First Place

The entire purpose of setting up an estimate is that there is a lead time, if there were no lead time, there would be no need to predict. Therefore the concept of demand sensing is on fragile ground. At this point, it seems to be simply a popular term which is used to sell software rather than any solid addition to the realm of demand planning. The article definition I have on demand sensing explains that the fact that no academic literature supports demand sensing.

Using Either of Demand Sensing or Demand Shaping

Demand shaping is a valuable function. However, extremely few companies perform demand shaping. In fact, the vast majority of enterprises achieve the opposite or demand distortion. This is because the supply chain department does not control the conditions and terms or pricing that their product is offered to customers under. This is determined by sales or marketing, which generally could not care less about how difficult this makes it for operations to fulfill the demand, or what the cost of sales ends up being. Therefore it is strange to hear so much about demand shaping when demand distortion rules the show.

So while it’s certainly a useful concept, there is very little chance of this occurring, so it is necessarily a waste of time to continue to discuss. The topic should be passed to sales and marketing, who will promptly put the idea in the trash bin.

Conclusion

Neither of these will amount too much in the longer term.

  • One is a splendid idea, but companies are positively opposed to its implementation, and actively engage in demand distortion, thinking this maximized profits and helps them meet quarterly numbers in a pinch.
  • The other has little foundation in any logic and seems to distract companies from improving their forecasting capability by adding a post-forecasting processor that jiggers the forecast around.

Somewhat unaddressed is that not everything departments do is designed to benefit the company. Some things are just designed to help get people promoted. Another example of this phenomena is IT outsourcing of stuff like SAP support. Outsourced support of SAP is a major problem which degrades SAP implementations as is described in this article. However, it continues to be popular because it allows some VPs and CIOs to talk about cost savings. The company gets “cost savings, ” but the support substantially degrades.

I have now seen major companies truly disabled because of outsourced IT. This includes companies with extremely high-profit margins based upon a brand who do little more than marketing to create value and how are in a position of monopoly for which money simply comes very quickly, unwilling to fund essential IT support. There is a strong emphasis on companies to get out of areas which are real work and give that to someone else. For instance, Boeing is losing interest in bearing the costs of R&D, and is outsourcing much of its R&D for the ever-late “Dreamliner.

Remote Forecasting Consulting

  • Questions About This Area?

    The software space is controlled by vendors, consulting firms and IT analysts who often provide self-serving and incorrect advice at the top rates.

    • We have a better track record of being correct than any of the well-known brands.
    • If this type of accuracy interests you, tell us your question below.

Brightwork Forecast Explorer

Improving Your Forecast Management

Brightwork Research & Analysis offers the following free software for tuning forecasting systems. See by clicking the image below:

 

References

Promotions can be used for demand shaping as is described in the following book.

Forecast Promotions Book

PROMOTE2

Promotions Forecasting: Forecast Adjustment Techniques in Software

The Constant Issues with Promotions Planning

Promotions keep increasing in companies, but the ability to manage promotions is simply nowhere near to keeping up.

Accurately accounting for promotions is the only way to guarantee the availability of the promoted product during the promotion period. Not only do in-house promotions change demand, but so do competitor promotions; being able to forecast the impact on your demand is vital to maintaining service levels.

Promotions Forecasting in Software

All promotions can be measured for their effect on sales, and the history of any promotion can be used to adjust the forecast to account for future promotions. In this book, through the use of graphics and screenshots, you will receive a tutorial on how software applications can be used to maintain this history and adjust forecasts.

By reading this book you will:

  • Gain an overview of the different categories and types of promotions.
  • Understand how forecast accuracy is impacted by promotions.
  • Learn how to create a database of historical changes, and then use this information to predict future changes in demand when similar promotions are run.
  • Appreciate promotions from the perspective of both Sales and Marketing, and Supply Chain.
  • Recognize common issues with forecasting for promotions.
  • Create strategies for communicating with Sales and Marketing about future promotions, and for translating this information to Supply Chain and Forecasting.

Chapters

  • Chapter 1: Introduction
  • Chapter 2 Where Demand Planning Fits within the Supply Chain Planning Footprint
  • Chapter 3: The Common Problems with Statistical Forecasting
  • Chapter 4: Introduction to Best Fit Forecasting
  • Chapter 5: Comparing Best Fit to Home Grown Statistical Forecasting Methods
  • Chapter 6: Sales Forecasting
  • Chapter 7: Sales Forecasting and CRM
  • Chapter 8: Conclusion

Software Ratings: Demand Planning

Software Ratings

Brightwork Research & Analysis offers the following free demand planning software analysis and ratings. See by clicking the image below:

software_ratings