In my role as the Director of Analytics, I enjoy working with the Arkieva team and our clients, in building optimization models which help organizations make intelligent decisions with regards to meeting demand, capacity allocation, inventory levels, factory schedules, forecasting, and cancer research. These models are built using a variety of mathematical methods including Boolean
While reading a wonderful article titled “Linear Thinking in a Non-Linear World – the obvious choice is often wrong” in the May-June 2017 issue of Harvard Business Review, I was immediately transferred back to the early 1980’s and theme of the IBM Advanced Industrial Engineering department – “only the model knows.”
The director of Supply Chain (or inventory, manufacturing, analytics, customer order fulfillment, etc.) has pulled together a cross-discipline team to identify potential enhancements in managing the demand-supply network (DSN) that will result in improved business performance. Typically, these enhancements focus on better coordination and more intelligent decision with respect to matching assets with demand across
What is the Supply Chain “Maturity Model” Buzz? Aside from the terms big data and analytics, the most common buzz word in supply chain solutions is “Maturity Model”. As a 40+ year veteran of campaigns to bring better analytics to bear on key organizational decisions, my immediate reaction was: another buzz word with more hype than substance glossing over or ignoring a rich set of outstanding prior and current work that can be an inconvenient truth. Words of wisdom such as – how can we move forward, if we don’t know where we are going – seem obvious and a dangerous simplification!
During my apprenticeship, one of the critical lessons I learned to work successfully with managers and planners is consistently make clear which planning / scheduling problem is the current focus and how it relates to other planning and scheduling decisions. I have organized these decision points into a tier hierarchy for better understanding of what can make business planning successful.
Sales and Operations Planning is a continuous business process that enables firms from hospitals to chemicals to respond to emerging situations intelligently. Today we will discuss the relevance of buzz words such as Analytics, Predictive Analytics, Data Science, and Machine Learning, for S&OP.
Organizations, from health care facilities to manufacturing giants to small restaurants, can be viewed as an ongoing sequence of loosely coupled activities where current and future assets are matched with current and future demand across the supply chain or demand supply network. These planning and scheduling decisions occur across a complex playing field. Read to learn more about these planning activities.
Arkieva’s Dr. Ken Fordyce recently participated in the Dagstuhl Seminar, "Modeling and Analysis of Semiconductor Supply Chains" in Wardern Germany. While attending he was able to participate in great discussions about “end to end” planning – aka master scheduling, and has written about his observations in regards to Advanced Planning and Schedule (APS), supply planning, and Central Planning Engine (CPE).
In a place and time far-far away, before spreadsheets, laptops, even color display units – where typewriters were common and “clouding computing and virtual machines” were the norm (called mainframes and time share with MVS and VM) – I was apprenticed to learn the ways of the force – for agents of change.
“Complexity exists, whether you ignore or not – better not to ignore it” Peter Lyon IBM retired, former director IBM Strategic Systems In 1995 the IBM Microelectronics Division made a decision to invest in a “smart” central or supply chain planning engine (CPE) to intelligently match assets with demand to improve its performance and responsiveness. CPE was part of a wider effort called OPS (operational framework for supply chain planning) which covered the three pillars of success: data, process, and models.