Dr. Ken Fordyce

About Dr. Ken Fordyce

Before joining Arkieva, he had a very successful 36-year career with IBM, much of it in all aspects of supply chain (to use Intel’s Karl Kempf’s preferred term – demand supply networks) for IBM Microelectronics Division (MD). During this period, MD was a Fortune 100-size firm by itself. Fordyce was part of the teams that altered the landscape of best-practices – receiving three IBM Outstanding Technical Achievement Awards, AAAI Innovative Application Award, and INFORMS Edelman Finalist (twice) and Wagner (winner). He writes and often speaks about the “ongoing challenge,” both to practitioners and academics. In his free time, Dr. Fordyce enjoys writing programs in APL2 while running sprints.

Tools of the Trade: How to Compare / Combine Diverse Time Series – “Normalizing”

A reoccurring challenge in comparing and combining diverse time series in demand forecasting is the “scale” – as it is in combining metrics. Rescaling is a powerful but simple method to help with this issue enabling demand planners to focus on similarities of shape. This blog provides an example of one method called normalization.

AI/ML, SCM, The Holy Grail, and Helpful Information from Foresight

In this blog, we point the reader to a recent article “Humachine”, which identifies the general challenge of implementing decision technology to improve SCM decision making resulting in improved organizational performance and the importance of experience in the trenches.

Time Series Forecasting Basics

In this blog we briefly cover some key insights for successful time series forecasting: (a) Profiling the Shape of the Curve is the first stage, and the first step is assessing if the time series is stationary. (b) The forecast method identified must capture the shape and be able to project the shape across time. (c) There are limits in historical and no amount of “fancy math” can overcome them.

Sudoku and SCM: The Binary Connection – A Path To Smarter Solutions

An earlier Sudoku blog recognizing Puzzle Day, provided an overview of solving Sudoku using MILP optimization and mentions these methods are helpful to find solutions in supply or central planning. This blog elaborates on “binary variables” which is the connecting technology between Sudoku and Supply Chain Management (SCM).

Data Science Without Modeling Impact is a Path to Disaster – Simulation to Explore the Impact of Group Size on COVID-19 Spread

In this blog post, we will briefly review some examples of being “COVID-19 adrift” with just data and then focus on the primary task – demonstrating how modeling can be used to understand the impact of group size on COVID-19 spread.

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