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Sales Forecast Example

Jul 16 2021

Sales Forecasts – Part 1. Evaluation

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. 

 

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By Michel Baudin • Tools 11 • Tags: Kanban, Production planning, Sales forecast

VietnameseSatellitePassesFinalTestInJapan.png

Apr 16 2021

Measuring QC Efficacy: A Proposal

[The featured image is of a Vietnamese satellite undergoing final test in Japan]

As Jay Bitsack pointed out in his comments on LinkedIn about my previous post, the portability of a method from epidemiology to manufacturing quality is not a foregone conclusion. Formally, the logic of validating a vaccine seems applicable to the solution of a quality problem. They look similar when you consider only outcomes in terms of infection rates or the proportion of defectives. 

There are differences between data sets from a clinical trial and tests run before and after a process change in production that may affect the applicability of a method. We examine the conditions for the approach developed by Carlo Graziani for vaccine efficacy to cross over to quality control. Then we work out the math of Graziani’s method and the means to apply it. 

 

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By Michel Baudin • Laws of nature 0 • Tags: Bayesian Statistics, Efficacy, Manufacturing, Quality, Quality Assurance, Quality Control

NBC News story

Apr 2 2021

COVID-19 Pfizer Vaccine Study On Teens

Pfizer-BioNTech just announced the results of a COVID-19 vaccine clinical trial of 12- to 15-year olds. Because the vaccinated group had 0 infections, the news media jumped to the conclusion that the vaccine has “100% efficacy” on 12- to 15-year olds. That is what the chyron said on NBC news. A look at the published trial results and quick analysis with current methods confirms that the vaccine works for that population but not that it eliminates 100% of the infections.

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By Michel Baudin • Data science 0 • Tags: Bayesian Statitics, COVID-19, Efficacy, Quality

GreatExpectations1946-3

Mar 12 2021

Process capability

The literature on quality defines process capability as a metric that compares the variability of its output with tolerances. There are, in fact, two different concepts:

  1. The ability of a process under nominal conditions to consistently produce output that meets expectations.
  2. The means of assessing it.
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By Michel Baudin • Tools 6 • Tags: Cpk, First-pass Yield, Quality, Rolled Throughput Yield, Run-by-Run, SPC, Yield

VillageDistanceChartMarieNeurath

Jan 30 2021

Of Bubbles and Arrows

[The featured image is an ISOTYPE from Marie Neurath (1936)]

Maps of symbols connected by lines are the most common form of graphic communication about operations, next to bar charts, pie charts, and time series. The symbols may be a variety of pictograms and there may be different types of lines, including arrows, double-headed arrows with a variety of arrowheads, with dashed lines of varying thicknesses…

This is about what you can do with such maps beyond communicating, and the challenges of mapping systems that don’t fit on one slide. It is also about improving current practices.

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By Michel Baudin • Information Technology 5 • Tags: Database, Graph, Map, Network, Operations, Operations Management, Operations Research, Simulation, Statechart, Visualization, VSM

Maureen-Mace-Tree-of-Knowledge.png

Jan 5 2021

Deep Learning And Profound Knowledge

[The featured image is Maureen Mace’s Tree of Knowledge]

In the news, Deep Learning is the currently emblematic technology of Machine-Learning (ML) and Artificial Intelligence (AI). In Management, the System of Profound Knowledge (SoPK) is a framework by W. Edwards Deming that specifies what individuals should know to be effective leaders of business organizations.

Your knowledge is what you have learned. You would not call a deep lake profound but a deep thought is also profound and vice versa.  When discussing abstractions, there is no daylight in meaning between deep and profound.

Consequently, we might expect Deep Learning to be the process by which you acquire Profound Knowledge but it is nothing of the kind. As technical terms, they are unrelated and neither one matches expectations based on common, everyday usage. 

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By Michel Baudin • Management 9 • Tags: Case-Based Reasoning, CBR, Deep Learning, Deming, Explaination, Machine Learning, Neural Nets, Profound Knowledge, SoPK

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