SCM Technology Investment – INFORMS Provides Critical Information for Success

Summary

Before the current set of analytics and SCM experts, it was the OR/MS profession that drove the development and successful application of analytic and data methods to improve supply chain performance. In fact, the entire area of supply chain management evolves directly from their work on areas such as production planning. This group (INFORMS) still continues to lead in the ongoing challenge of more intelligent and responsive supply chains as demonstrated in the upcoming conference. In this blog we will point out some keynote addresses from the current meeting of relevance to successful SCM technology investments: evolution of retail supply chains, Intel realizes $25 billion by applying advanced analytics from product architecture design through supply chain planning, Forecasting Models for the COVID-19 Pandemic, Mathematical Optimization for Social, and Optimization for Machine Learning: Insights and Challenges.

Introduction

In a previous blog on hints for success, we mentioned well before the current analytics and SCM technology landscape was occupied by data scientists, machine learning and artificial intelligence gurus, and SCM high priests; the analytics and SCM world was pioneered by a group of Jedi knights referred to as OR/MS professionals where OR refers to operations research and MS – management science. This group remains the premier SCM and analytics professional organization across the globe called INFORMS. Each year INFORMS has its national meeting in early November. In this blog, we will point out some keynote addresses from the current meeting of relevance to successful SCM technology investments.

Evolution of Retail Supply Chains – A Practitioner’s Perspective

Abstract: Retail supply chains a few decades ago was only concerned with getting products from a local manufacturer to a local seller. The focus was on physical flow. It then evolved to a global scale where the winners extracted value by focusing on scale and efficiency. This is where the physical flow combined with a financial angle gained significance. Then the focus shifted to the efficiency with real-time visibility and control. Information flow took its place along with the financial and physical flow. Still, in the retail world, supply chain teams played a secondary role; merchants and store operations organizations ran the show. With the evolution of eCommerce, supply chain is taking its place in the boardroom. Now supply chain is defining the flow of the retailer’s strategy; on how to balance cost with service, how to provide innovative options to the shoppers, and how to get the right product to the right customer at the right time. What does that mean for the retail industry and what does it mean for operations research and data science practitioners?

Presenter: Dr. Guru Pundoor, VP: Supply Chain Strategy, Planning, and Execution, American Eagle Outfitters

Observation: One of my favorite presentations. It makes clear the need for a complete end to end solution that requires demand and central planning running across the different decision tiers for all industries. This approach was originally pioneered in major industrial firms such as wafer, chemicals, and fuels – it is now critical for survival in all organizations.

Edelman Reprise: Intel Realizes $25 Billion by Applying Advanced Analytics from Product Architecture Design through Supply Chain Planning

Abstract: The myriad of products in Intel Corporation’s portfolio is among the most complex offered in the international marketplace. We have leveraged advanced analytics to orchestrate corporate-wide, rapid, high-quality decision-making spanning product feature designs through supply chain planning. The extraordinary results have benefitted Intel (in excess of $25 billion over the last 10 years), our customers, the industry, and our planet.

Presenter: Intel Edelman Team

Observation: The Edelman competition for best use of “analytic” methods each spring is one of my favorite INFORMS events. The Edelman finalist papers are published each January in IJAA (previously named Interfaces). For over 25 years Intel has been a leader in the application of “analytics” to improve organizational performance under the leadership of INFORMS fellow Dr. Karl Kempf. INTEL CFO stated, “The system has been vital to improving the process of corporate decision-making, and the results have been in the billions since the system began.”

Panel Discussion: Forecasting Models for the COVID-19 Pandemic

Abstract: Since the early days of the outbreak of the COVID-19 virus, advanced analytics have been applied to forecast the outbreak of infections, deaths, and hospitalizations. This distinguished panel includes four leading researchers in the areas of modeling the disease, and they will discuss different modeling approaches, how these approaches quickly evolved the advantages and disadvantages of these approaches and a retrospective of how the models and their results have been communicated outside the profession. The panel will also touch on opportunities for future modeling to support decision making.

Moderator: Dr. Anne Robinson, Chief Strategy Officer Kinaxis

Observation: As the system complexity of COVID-19 became apparent in the spring of 2020, it was clear simpler traditional epidemiological methods would be insufficient to support the successful navigation of a multidimensional playing field. Early on Arkieva argued success with COVID-19 required an operations management approach and six months later this is clear. This panel discussion demonstrates the ability of OR/MS to help the nation handle critical challenges dating back to WWII.

Mathematical Optimization for Social Distancing

Abstract: The spread of viruses such as SARS-CoV-2 brought new challenges to our society, including a stronger focus on safety across all businesses. In particular, many countries have imposed a minimum social distance between people in order to ensure their safety. This brings new challenges to many customer-related businesses, such as restaurants, offices, etc., on how to locate their facilities under distancing constraints. In this talk, we propose parallelism between this problem and locating wind turbines in an offshore area. Even if the two problems may seem very different, there are many analogies between them. Both problems require fitting facilities (turbines or customers) in each area while ensuring a minimum distance between them. Similar to nearby customers who can infect each other, also nearby turbines “infect” each other by casting wind shadows (the so-called “wake effect”) that cause production losses. In both problems we want to minimize the overall interference/infection, hence optimal solutions will favor layouts where facilities are as spread as possible. The discovery of this parallelism between the two applications allowed us to apply Mathematical Optimization algorithms originally designed for wind farms, to produce optimized facility layouts subject to social distancing constraints as those arising in the time of the COVID-19 pandemic. These methods allow us to challenge the current (manual) layouts and provide new insights on how to improve them. We show that optimized layouts are far from trivial to design and that Mathematical Optimization can make an impact, helping businesses while ensuring safety.

Presenter: Dr. Matteo Fischetti, Professor of Operation Research at the Department of Information Engineering, University of Padua

Observation: COVID-19 has altered the entire concept of space optimization. This presentation is an example of the value of optimization for innovative solutions.

Optimization for Machine Learning: Insights and Challenges

Abstract: What is the mathematical optimization viewpoint on machine learning, and how does it scale to modern applications? The origins of linear programming stem from military resource allocation over tens of variables and constraints. Recently, techniques from mathematical optimization were used to optimize 175 billion parameters of a highly non-linear language model. In this talk, we will survey the algorithms arising from early developments in optimization theory, to giga-scale modern problems that lie at the heart of artificial intelligence research. We will describe some recent developments, insights, and challenges facing researchers in our field.

Presenter: Elad Hazan, Professor of Computer Science, Princeton University

Observation: From a technical perspective, this is my favorite presentation. Arkieva has made clear the best results come from mixing and matching machine learning and optimization.