Implementing pull based supply chain solutions of Theory of Constraints involves significant paradigm shift from the conventional push based supply chain. When most companies, even the big names in many industries, are having push-based systems, the common experiential knowledge of most managers and even consultants are from such systems. As a result there is continual threat of a pull-based system being dismantled as most managers and consultants rely on their past experiences to “improve” systems.
This article was written for stake holders at corporate office of a large fashion company (referred as “The Fashion Company”), who despite getting fantastic results from pilot roll out of TOC solutions, in one of the divisions, were being “tempted” by consulting companies to go back to the push based system of forecasting.
Understanding results of pilot implementation
If we compare the actual off take of MTAM1 SKUs (end product entity) for pilot wholesalers with the booking2 quantities – the results provides us with an interesting insight. The actual off take varies widely from the initial booking quantities that they had given. Some qualities and shades sold twice compared to the booking quantity, while for others it is as low as 50% of the booking quantity. This kind of wide variation is seen in almost all wholesalers who have switched on to the MTA system, regardless of the difference in managerial competency, years of experience in business or IT systems available with them.
Remember, we are dealing here with perennial SKUs. If the forecasting error is so high for qualities and shades that are sold around the year, can we even imagine the errors that would happen with new products?
The people who have the deepest knowledge of their own local market (the wholesalers) are actually unable to forecast with any reasonable accuracy, even for products, which sell regularly. (In the seasons before the rollout of the MTA system, they have been suffering from high inventory and stock outs due to the gross errors in forecasting in perennials).
Possible Hypothesis We are faced with either of the below two hypotheses to explain the phenomenon (of gross errors of forecasting by the wholesalers)
- All wholesalers do not use good “scientific” forecasting tools and hence suffer from using “gut” based forecasting decisions.
- It is futile to expect any significant improvement in accuracy. Forecasting demand of a fashion related SKU within a wholesaler’s region for a booking season is, theoretically speaking, highly error prone.
The consulting community and software vendors have made us believe that the reason for poor forecasting is lack of use of “scientific tools”.
While the Theory of Constraints assumes hypothesis two is correct.
We have a problem!
How do we know which of the two is a valid hypothesis? If something is being promoted as a science, how we do invalidate that?
We will not go into philosophical debate of what is “science” and “non-science”. But we should be careful; the use of mathematical models does not necessarily mean we are being scientific! Math is not always Science!
Unless we get into a theoretical explanation of why hypothesis two is valid, the perceived sophistication of the tools will overwhelm us into accepting them under pressure of being “scientific”. (Rather, the fear of being labeled “unscientific” if we oppose those tools!).
Is there a theoretical fallacy behind these tools?
One of the latest branches of science called the Chaos Theory states that it is almost impossible to predict (with any reasonable accuracy) an output of a complex system because of non-linear feedback loops in reality. The relationship of variables in this system is not predetermined and a small change can have a disproportionate impact and vice-versa. (Remember the butterfly effect? – “ a butterfly flapping its wings in China can lead to a tornado in Tokyo”!)