Improving Transportation Planning Using Machine Learning

Introduction

Companies can no longer rely on lack of dependencies and integrations with suppliers or customers. Companies that are successful are the ones that have a focus on supply chain (Anderson, Britt, & Favre, 2007). Will (2021) published an article in which supply chain is described as follows: “A supply chain is a network between a company and its suppliers to produce and distribute a specific product to the final buyer. This network includes different activities, people, entities, information, and resources. The supply chain also represents the steps it takes to get the product or service from its original state to the customer”. Lummus and Vokurka (2001) provide definitions from different article and finally provide a summarized definition of supply chain as; “all the activities involved in delivering a product from raw material through to the customer including sourcing raw materials and parts, manufacturing and assembly, warehousing and inventory tracking, order entry and order management, distribution across all channels, delivery to the customer, and the information systems necessary to monitor all of these activities”. Another term supply chain management (SCM) is key in understanding supply chains and their management. The Council of Supply Chain Management Professionals defines SCM as “encompasses the planning and management of all activities involved in sourcing and procurement, conversion, and all logistics management activities. Importantly, it also includes coordination and collaboration with channel partners, which can be suppliers, intermediaries, third party service providers, and customers. In essence, supply chain management integrates supply and demand management within and across”.

As per the definition of supply chain, product moving from raw material stage to final customer, is physical movement, but there is information/ data being transferred from systems at every stage of the supply chain. A study by Forrester Research suggests that U.S. manufacturers are benefiting from using information technology (IT) to improve supply chain agility, reduce cycle time, achieve higher efficiency, and deliver products to customers in a timely manner (Radjou, 2003). Wu, Yeniyurt, Kim, & Cavusgil (2006) discuss the importance and competitive advantage IT can create for supply chains. The authors also discuss IT related resources, IT advancement, IT alignment and the role of these in a supply chain or a company. With the use of IT systems to improve efficiencies in the supply chain, comes a very compelling biproduct, i.e., data.

Transportation is a very important part of the supply chain. Because of the globalization of supply chains, the transportation networks must connect more effectively across different regions to meet increase in customers’ demands, such as ensuring on-time delivery. The global nature of transportation networks and competition among companies to serve customers better leads to increase in demands of service and faster delivery times with cost efficiency. This adds greater complexity in transportation networks which results in vulnerability.

With massive amounts data being generated in supply chains (Schoenherr & Speier‐Pero, 2015) it is very crucial that supply chain management professionals are using predictive analytics to improve supply chain performance and competitive advantage (Waller & Fawcett, 2013a). McAfee and Brynjolfsson (2012) note that use of predictive analytics has a potential for significant above-average returns. Predictive analytics is a quantitative and qualitative approach of using historical data to answer questions of the future. Predictive analytics is a positioned within the domain of data science (Schoenherr & Speier‐Pero, 2015). Data Science (DS) is an art of using science to tell a story about the data that allows for better decision making (Van Der Aalst, 2016) and (Provost & Fawcett,).