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.

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).

By |2021-02-03T09:51:17-05:00February 2nd, 2021|Central Planning, Optimization, Sudoku, Supply Planning|

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.

By |2024-02-21T13:54:34-05:00December 1st, 2020|COVID-19, Data Science, Demand Management, Supply Planning|

Data Science Tools of the Trade: Monte Carlo Computer Simulation

Monte Carlo Discrete Event Computer Simulation is a particularly powerful and flexible “tool of the trade” for a wide range of challenges in supply chain and operations management. This blog provides some basics on MCDECS, the next blog will use MCDECS to explain why limiting the size of a gathering helps reduce the spread of COVID-19.

By |2024-02-21T13:56:05-05:00November 24th, 2020|COVID-19, Data Science, Machine Learning|

Investing in SCM Technology: Sense and Respond – A Vision from A Long Time Ago in A Galaxy Far, Far Away

Having a vision of the future is important to help coordinate SCM initiatives towards the “global good” – but having a vision is worthless without effective work in the trenches. In fact, a far better vision is “sense and respond” SAR which moves an organization from a big bang paradigm periodic driven approach that currently dominates SCM even in best in class organizations to an “as needed” basis.

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