3 Reasons Why Mathematical Optimization Is Such A Potent Weapon In Combating Supply Chain Disruption

Supply Chain Disruption

 

Author: Edward Rothberg, PhD
Date: 5/7/2021

 

The COVID-19 pandemic has sent shockwaves through global supply chains over the past year, unleashing unprecedented disruption and upending supply and demand dynamics across numerous industries, including healthcare and pharmaceuticals, CPG, logistics, and agriculture and food production.

In fact, 94% of Fortune 1000 companies have experienced supply chain disruptions from COVID-19 and 75% of those companies have had negative or severely negative impacts on their businesses as a result of the pandemic, according to Accenture.

Disruption, of course, is not a new phenomenon in the supply chain world (in fact, it is a fundamental feature of each and every supply chain system), but the scale and scope of the upheaval caused by COVID-19 is something supply chain stakeholders have never seen before.

Fortunately, businesses today have an array of AI technologies – like mathematical optimization – at their disposal, which can help them combat and overcome supply chain disruptions.

Mathematical optimization has long been established as the go-to technology for supply chain planning and operations. Ever since the 1980s, companies across the business world have been leveraging mathematical optimization – in a wide array of off-the-shelf and bespoke planning applications – to not only drive greater supply chain efficiency and profitability, but also to manage and mitigate supply chain disruption.

Indeed, mathematical optimization technologies have been a pivotal tool over the years (and especially this past year) in fostering supply chain agility and resilience.

Mathematical optimization empowers businesses to deal with supply chain disruption by facilitating two types of decision making:

  • Reactive: Planners and other key stakeholders can sense disruptions in real-time and respond to them rapidly and effectively by identifying root causes and dynamically reallocating resources, thereby reducing time to recovery.
  • Proactive: Planners and other key stakeholders can analyze supply chain risks and anticipate potential disruptions.

But – you may be wondering – how exactly is mathematical optimization able to drive optimal reactive and proactive decision making? To answer that question, let’s take a look under the hood of a mathematical optimization application and examine the reasons why this AI technology is such a potent weapon in handling supply chain disruption.

 

Reason #1: Mathematical optimization relies on a model of your real-world supply chain

Every mathematical optimization application is essentially made up of two elements: A mathematical optimization solver (an algorithm-based problem-solving engine) and a mathematical optimization model (a representation or digital twin of your real-world operating environment, with all its complexity and challenges).

In mathematical optimization applications for supply chain, the model can encapsulate specific elements of your supply chain (such as your supplier or logistics network or your production or warehouse operations), or it can encompass your entire end-to-end network.

Each model has three parts:

  • Decision variables: The decisions that you have to make at various points across your supply chain.
  • Constraints: The business rules that you must follow.
  • Business objectives: Your numerous (and often conflicting) business goals, such as minimizing costs and inventory levels or maximizing resource utilization, on-time delivery performance, and customer satisfaction.

When a disruption occurs, mathematical optimization applications – because they are built on models that understand and embody how your actual supply chain behaves – enable you to achieve:

  • Visibility: Instantly identify the sources of the disruption such as capacity bottlenecks and sudden fluctuations in supply and demand.
  • Flexibility: Modify your model – by making adjustments and even adding new constraints, decision variables, and business objectives – to reflect current operating conditions across your supply chain.
  • Agility: Dynamically and automatically reoptimize plans and schedules and determine the best course of action to resolve the disruption as quickly and effectively as possible.

With a mathematical optimization application (which is constructed on a model of your real-world supply chain), you can maintain real-time visibility and control over your end-to-end network – so that when disruptions hit, you can easily pinpoint their root causes and swiftly take the necessary steps to remedy them and preserve business continuity.

 

Reason #2: Mathematical optimization runs on up-to-date data

Data is the fuel that makes AI technologies run.

In contrast to what is probably the best known AI technology, machine learning (which relies on  historical data), mathematical optimization leverages the latest available data to deliver real-time prescriptive analytics – or, as Gartner calls it, “continuous intelligence” – across your supply chain network.

When a severe supply chain disruption strikes (like it did during the COVID-19 pandemic) and the supply and demand landscape suddenly shifts in a way it never has before, you can’t depend on data from the past to help you navigate the unprecedented financial and operational challenges you are dealing with in the present.

Even in the face of the most massive disruptions, mathematical optimization applications – because they utilize the latest available data and models that capture current conditions across your operational network – are capable of automatically generating the best possible solutions to your present-day supply chain problems, and enabling continuous intelligence and optimal decision making.

 

Reason #3: Mathematical optimization allows you to conduct robust scenario analysis

An important part of handing supply chain disruption is assessing risk and proactively planning and preparing for the future – and mathematical optimization empowers your planners and key stakeholders to do this more quickly and easily.

With mathematical optimization’s robust scenario analysis capability, you can:

  • Explore various supply, demand, inventory, capacity, macroeconomic, geopolitical, and other what-if scenarios and evaluate their potential effect on your business.
  • Uncover hidden risks and gauge your risk exposure and time to recover in the event of a disruption such as a natural disaster or a production or transportation breakdown.
  • Unlock opportunities to mitigate risk and drive improved supply chain resilience by reallocating your resources or reconfiguring your supply chain.

By exploiting mathematical optimization’s robust scenario analysis functionality, you can insulate your supply chain against the impact of future disruptions by making proactive, strategic decisions in a number of areas, including capital investments, supplier selection, capacity and inventory planning, and production and warehouse facility location.

 

Building a Stronger and More Resilient Supply Chain

During the COVID-19 pandemic, we’ve experienced an unexpected, unprecedented wave of supply chain disruption, which has caused significant and enduring chaos in the global economy and huge challenges for supply chain professionals.

Hopefully we will never encounter a disruption of that magnitude ever again, but businesses will undoubtedly continue to experience disruptions on a daily basis – as disruption always has been and always will be a part of every supply chain.

As mathematical optimization has proven that it is a potent weapon in battling supply chain disruption (and also in boosting supply chain efficiency and profitability), this AI technology will continue to be an essential tool for supply chain leaders as they navigate the ever-changing business landscape.

 

A version of this article was originally published on SupplyChainBrain.com here.