Aug 29 2021
The 2012 paper in Science about the CRISPR/Cas9 system has been hailed as the greatest breakthrough in biology since Crick and Watson’s discovery of the DNA double helix in 1953. It has earned its two Principal Investigators (PI), Emmanuelle Charpentier and Jennifer Doudna, the 2020 Nobel prize in chemistry. Laypersons cannot really follow this paper but what we can better understand is how the research team worked. And it is remarkable.
Aug 27 2021
My last two long posts were about evaluating sales forecasts. They begged the question of how you generate these forecasts. This is a partial answer, about what you can tell from a history of sales through both classical methods and recent developments, particularly probability forecasting.
Jul 31 2021
Almost 100 years ago, Mary Parker Follett wrote: “And our aim in the […] organization of industry should be […] so to organize the plant that the workmen’s experience can be added to that of the expert; we must see just where their experience will be a plus matter, and we must plan to have the workmen learn more and more of the industry as a whole.”
Source: Follett, M. P. (2013 reprint). Creative Experience. United States: Martino Fine Books. (p. 20)
Michel Baudin‘s comments: Today, we would say “manufacturing” rather than “industry,” and “operator” rather than “workmen.” This is the earliest text I have seen that recommends engaging shop floor operators in improvement activities and training them to understand the broader picture.
Taylor wanted to control operators so that they couldn’t collude to curtail output. The Gilbreths wanted to make their work easier. Follett wants to tap into their knowledge and combine it with the experts’ in order to achieve better outcomes. And she also believes in their ability to learn.
In these few words, she showed more respect for the humanity of the operators than I recall seeing from her immediate precursors and contemporaries.
#maryparkerfollett, #respectforhumanity, #traceyrichardson
Jul 30 2021
The lively response to last week’s post on this topic prompted me to dig deeper. First, I take a shot at clarifying the distinction between point forecasts and probability forecasts. Second, I present the idea behind the accuracy metric for probability forecasts that Stefan de Kok recommends as an alternative to the WSPL. Finally, I summarize a few points raised in discussions on LinkedIn and in this blog.
All of this is about evaluating forecasts. We still need methods to generate them. There are many well-known, published methods for point forecasts but not for probability forecasts, particularly for sales. This is a topic for another post.
Jul 16 2021
When sizing a new factory or production line, or when setting work hours for the next three months, most manufacturers have no choice but to rely on sales forecasts as a basis for decisions.
But how far can you trust sales forecasts? You use a training set of data to fit a particular model and a testing set of actual data observed over a time horizon of interest following the end of the training set period. The training set may, for example, cover 5 years of data about product sales up to June 30, 2021, and the testing set the actual sales in July, 2021.
The forecasters’ first concern is to establish how well a method works on the testing set so that the decision makers can rely on it for the future. For this, they need metrics that reflect end results and that end-users of forecasts can understand. You cannot assume that they are up to speed or interested in forecasting technology.
Forecasters also need to compare the performance of different algorithms and to monitor the progress of an algorithm as it “learns,” and only they need to understand the metrics they use for this purpose.
Oct 8 2021
Sales Forecasts – Part 4. Generating Point Forecasts with Trends and Seasonality
This fourth post about sales forecasts addresses what you actually start with — that is, visualizing the time series of historical sales and generating point estimates for the future. Theyou analyze the residuals to determine the probability forecasts.
What prompted me to review this field is the realization based on news of the M5 forecasting competition that this field has been the object of intense developments in recent years. Some techniques from earlier decades are now accessible through open-source software that can crunch tens of thousands of data points on an ordinary office laptop.
Others are new developments. Thanks to Stefan de Kok, John Darlington, Nicolas Vandeput, and Bill Waddell for comments and questions on the previous posts, that made me dig deeper:
Continue reading…
By Michel Baudin • Tools • • Tags: Exponential Smoothing, Holt-Winters, Point Forecast, Probability Forecast, Sales Forecasting, Time Series