When fidget spinners started flying off the shelves in 2017, retailers were caught off-guard. Some 50 million units of the toy were sold in the first six months of that year alone, as a result of the phenomenal demand.
It was a textbook example of how traditional business models can be overwhelmed by unpredictability in demand and supply.
But today’s fast-moving world means that companies must be able to quickly expand or shrink their capabilities to meet the demand within the supply chain at any point in time. This flexibility is known as elastic logistics.
To introduce elasticity to their operations, logistics companies are adopting transportation management systems (TMS) to manage and optimize their transportation fleets. This allows businesses to provide the fast and on-demand delivery services that customers have come to expect.
Part of this flexibility also encompasses the ability to forecast risks, costs and demand. With advancements in Artificial Intelligence (AI), most TMSs employ predictive technologies in their analytical functions.
Having such early warning systems helps companies monitor and avert fluctuations in demand, and deals with supply-side disruptions such as material shortages.