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.

Illusively Complex – Effective Approach to Mixing Judgment and Statistics in Forecasting

In 1994, the IBM Micro-electronics Division, itself a fortune 100 size firm, put in place a major effort to create best in class supply chain planning process and software including demand planning(DM), central planning, available to promise, et al. I was fortunate to be an original member and had the opportunity to work extensively on all key components including DM (1996-97) – created an estimate of demand – a forecast.  With 20 years of experience in computational methods at that time, I thought DM would be a walk in the park. I quickly learned it was illusively complex.

By | October 13th, 2015|Demand Planning|0 Comments

Eliminating Some Of The Safety Stock Mystery

In the simplest inventory situation, the only variability is in the quantity of demand for a single day.  There is no trend up or down or seasonal effect. The demand today is independent of the demand for tomorrow.  Additionally, we will assume replenishment time is zero.  That is when we place an order for additional material it arrives immediately – sort of like we have a Star Trek transporter. However, we can only reorder every N days, where N might be 3, 4, 20, etc.

Generate, Test, Next: Computational Principles That Support Important Decision Technologies

Most folks involved in Sales and Operations Planning (S&OP) for supply chain management have heard the terms “optimization” or “linear programming” with regards to supply planning and have cringed at the sound. Over the past few years a new “cringe” worthy term has emerged – “machine learning” which is sometimes used with the term predictive analytics or data mining. The purpose of the material below is not to explain optimization or machine learning, but to provide an easy to follow example from numerical methods applied to high school algebra to illustrate the key computational principle that supports important decision technologies.  We will see it as Generate,Test,Next.

By | July 7th, 2015|General Topics, S&OP|0 Comments

7 Simple Steps to Optimize the Scheduler’s World

The term ‘optimization’ can and does have different meanings to different groups. For the folks who build and develop scheduling algorithms, creating the best schedule is defined in terms of cost criteria – perfectly logical. For business settings (from manufacturing to hospitals) optimization refers to the entire process. Let’s look at the scheduler’s world to identify key steps to optimize the process.

By | June 25th, 2015|General Topics|0 Comments

Planning versus Scheduling – Helpful Hints

Each year I work with new bright-eyed future experts in planning and scheduling as they make the transition from their academic studies to the murky world of applied planning and scheduling. One of the first rules of thumb I suggest is to ensure everyone has the same view of the problem.  Is this a planning problem or a scheduling problem or both? If both are being done simultaneously, the following example quickly illustrates the value in separating them.

By | June 18th, 2015|General Topics|0 Comments

Illusion of Capacity and Rabbit in the Hat

One value of supply chain modeling is the ability to explicitly model capacity or constrained resources. Which products get what capacity in what amount at what time? How does this impact on-time delivery? Where do I add capacity? These types of questions are custom made for a “little bit of math” and difficult to do in a simple spreadsheet model.

By | May 21st, 2015|Supply Planning|0 Comments

Rough Cut Capacity Planning – A Place to Start

Your demand planning process is complete and demand statement is created; excellent, but the sales and operation planning journey is far from complete – the next critical step is matching or balancing assets (capacity, materials, people and projected supply of finished goods) with demand to answer three questions that are part of being a responsive organization:

By | May 12th, 2015|Supply Planning|0 Comments

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