Coronavirus: How Mathematical Optimization Can Help Mitigate Supply Chain Disruption

 

 

Authors: Professor Haitao Li, PhD and Pano Santos, PhD

Date: 3/12/2020

 

 

The novel coronavirus (COVID-19) outbreak is spreading rapidly around the world, infecting more than 120,000 people, killing over 4,500 people, and causing an unprecedented global health crisis. This growing epidemic has also already taken a huge toll on the global economy, and some experts are predicting that the outbreak could trigger a widespread economic recession – potentially leading to $2.7 trillion in lost output. Fear and uncertainty reign in the global economy today as stock markets are plummeting, oil prices are in free fall, and global supply chains have been severely disrupted.

 

The latter phenomenon – supply chain disruption – is particularly significant both from a health and humanitarian perspective (as many patients urgently need care and medicines) and from an economic perspective (as the global economy depends on the smooth flow and delivery of goods and services around the world). The negative impact of this supply chain disruption is so immense due to the fact that the coronavirus outbreak:

 

-Has spread to more than 100 countries – from China (the epicenter of the outbreak) to South Korea, Iran, Italy, the U.S. and more.  

 

-Has disrupted numerous industries – from electronics manufacturing to pharmaceuticals, logistics, agriculture and food production, tourism, and more.

 

-Has wreaked havoc on supply chain dynamics, causing major volatility and uncertainty in terms of both supply, demand, and human resources.

 

So the question is: How can governments and companies respond to this global supply chain disruption from the coronavirus outbreak in the most efficient and effective manner possible?

 

Given the unprecedented and unpredictable nature of the epidemic (specifically, the speed and scope of its spread), organizations can’t solely rely on forecasts and predictions. They need be able to quickly react to constantly changing conditions across their end-to-end supply chains, make optimal plans and decisions, and take the necessary actions – so that they can deliver vital goods and services (in the right places, at the right times, and at the lowest cost).  

 

Indeed, to mitigate and manage supply chain disruption in these tumultuous times, organizations require a full advanced analytics toolbox that contains not only predictive analytics tools like machine learning and time series forecasting, but also the powerful prescriptive analytics tool of mathematical optimization.

 

Mathematical optimization – an AI technique that empowers organizations to rapidly solve their complex, real-world problems and make optimal, data-driven decisions that maximize their efficiency – is an essential technology for organizations today.

 

Facing the supply chain chaos caused by the coronavirus, businesses and governments must have supply chain agility: The capability to sense supply and demand volatility across their end-to-end networks and swiftly decide on how best to react and respond. Mathematical optimization technologies provide this power.

 

A Proactive and Reactive Approach

 

With mathematical optimization technologies, governments and companies can more effectively cope with the sudden and severe supply chain disruption and manage potential supply chain risks caused by the coronavirus crisis.

 

Mathematical optimization – especially when utilized in combination with predictive analytics tools such as machine learning – gives decision-makers the ability to make both proactive and reactive decisions.

 

Let’s take a look at a real-world example of how mathematical optimization – by enabling proactive and reactive decision-making – can help manage supply chain volatility:

 

The supply chain network of the pharmaceutical industry – which relies on China to produce a significant portion of APIs (active pharmaceutical ingredients) – is in danger of being disrupted, and this could potentially lead to shortages of critical drugs in the U.S. and around the globe.

 

To help manage and minimize this supply side risk in the pharmaceutical industry, pharmaceutical producers must be able to take:

 

·      A proactive approach – This involves three steps:

 

1) Utilizing traditional statistics techniques (such as machine learning, logistic regression, and neural networks) to build models that assess and predict supply chain risk and raw material unavailability.

 

2) Feeding those predictions into the mathematical optimization models (deterministic or stochastic), which – taking the potential supply chain risks into account – can recommend optimal, strategic- and tactical-level decisions in terms of network design, supplier selection, inventory management, and in other areas.

 

3) Conducting “what-if” analysis or simulation to explore various possible scenarios and determine the best courses of action.  

           

With mathematical optimization, pharmaceutical companies can proactively make plans and decisions to ensure their end-to-end networks are efficient, agile, and robust enough to withstand the impact of supply chain disruption caused by the coronavirus crisis.  

 

·      A reactive approach – With mathematical optimization technologies, pharmaceutical companies can effectively respond in real-time to changing conditions, emergency situations, and unexpected disruptions across their end-to-end supply chain networks by:

 

1) Automatically and dynamically revising and reoptimizing their supplier selection, production, inventory, logistics, and workforce plans.

 

2) Using those reoptimized plans to make optimal decisions on how to utilize their resources and allocate their raw materials and inventory in the most efficient way possible – so that the right medicines can be delivered to the right places at the right times (and at the lowest cost).  

 

This is merely one example of how mathematical optimization can help companies minimize the impact of supply chain disruption and uncertainty during the coronavirus epidemic.

 

By fueling proactive and reactive planning and decision-making, mathematical optimization technologies are a vital tool for companies across industries and governments around the world to help manage the supply chain risk and volatility that we are experiencing today.  

 

An Immense Challenge and Opportunity

 

The coronavirus outbreak presents a huge crisis and challenge for the world – on a health and humanitarian level as well as on the economic front.

 

Containing and combating the virus will require a concerted and collective effort by governments, industries, and individuals. It will also require the most powerful and innovative technological tools. Mathematical optimization is definitely one of the tools that must be used – in order to help ensure the smooth flow and successful delivery of vital goods and services around the world.

 

Indeed, the coronavirus epidemic provides an opportunity for mathematical optimization practitioners and researchers to develop and deploy new models that help to minimize the negative impact of supply chain disruption by enabling companies and governments to make optimal proactive and reactive decisions that drive greater operational efficiency and agility.