While decision-making support tools and predictive models have been widely implemented in supply chain management, the implementation of integrated solutions for complex chains with multiple types of warehouses and multiple distribution networks and service level agreements is always a challenge.
FREMONT, CA: Statistical modeling and mathematical optimization play a critical role in the management of the supply chain, considering the vast number of micro-decisions that need to be taken and the need to schedule activities ahead of time in the face of uncertainty. Here are a range of developments that should be on the radar of companies that build in-house supply chain optimization models or develop industrial supply chain management software.
Fresh Data and Methods for COVID-19 and Post-COVID-19
The Coronavirus Disease (COVID-19) pandemic has been the greatest disruptor of supply chain operations since the 1940s. In 2020, supply chain and inventory managers had to deal with a variety of shocks related to lockdowns, reopenings, changes in customer behaviour and economic downturns. These efforts have been widely supported by analytical software and data science teams that have adapted forecasting and optimization models to account for unprecedented shocks. Many of these changes were made using new indications and data sources not normally used before the pandemic, such as seasonal influenza figures, foreign data, macro-economic data for the global crises of 2000 and 2008, and so on. These advances will begin in 2021, and many of the strategies and innovations implemented during the pandemic are likely to be around for a long time to come.
Integrated Decision Support Tools with Predictive Components
While decision-making support tools and predictive models have been widely implemented in supply chain management, the implementation of integrated solutions for complex chains with multiple types of warehouse management and multiple distribution networks and service level agreements is always a challenge. Currently, several businesses are in the process of developing detailed service lines that will allow them to search efficiently for optimal supplier and logistics options while taking account of these complexities, and we expect this trend to continue in 2021 and beyond.
Adoption of the Prescriptive Approach
Demand forecasting has been widely adopted in the industry and is the basis for a variety of supply chain planning and optimization activities, but several organizations have not yet been able to develop robust decision-making automation capabilities in addition to such forecasting. Manual analysis of forecasts and other predictive outputs will be replaced by systems that have a higher degree of automation and incorporate mathematical, econometric and risk score models to make decisions more autonomously. More precisely, the simulation and reinforcement of learning will be a significant technological enabler of this process.