The Modern Supply Chain Must Be Flexible – How Does Yours Compare?
Globalization, economic volatility and the rise of e-commerce, along with constantly changing product mix and customer preferences, have made the traditional supply chain a thing of the past. Singularly-focused facilities and rules designed around traditional markets and consistent relationships, are no longer sufficient.
The modern supply chain must quickly transform to become more scalable and more flexible in order to adapt to continuing market shifts toward faster and more profitable delivery.
And that means making high-quality, real-time decisions across numerous constraints using advanced mathematical models and machine learning. Applying mathematical research and knowledge to real-world operations can generate significant cost reduction and service level advantages.
In today’s complex supply chain environment, companies cannot afford to make multi-million-dollar decisions with limited historically approaches to analyses and rules. Mathematical models — whose sophistication increases with the intricacy of the supply chain configuration — is now required to support a holistic, end-to-end approach.
Transportation challenges, for example, are among the most complex that supply chain planners and executers will face, each with its own cost and service level outcome. While planners don’t need to master the math (mixed integer programs, meta-heuristics, simulated annealing, tabu-search and more), they should understand that any shipment generation process requires solving a variant of the VRP (vehicle routing problem).
VRP is one of the most-studied problems in the academic and professional world, by applied mathematicians, industrial engineers and operations research specialists. Numerous variables such as costs, resource availability, common carrier rates, time windows, service-level requirements make finding an optimal solution via trial and error nearly impossible. Instead we must depend upon automated and intelligent computation-based decision making.
The modern supply chain is defined less by assets than by the decisions that dictate the relationships between assets. Decisions such as how many distribution centers (DCs) to operate, which vendor to supply which DC, which stores to service from which DC, and how to blend e-commerce, service schedules and contracted rates, are some of the critical connections that define the overall supply chain.
Today, we must look to mathematical models and intelligent algorithms to streamline these relationships in a hierarchical manner, including facility location, source determination, pickup/delivery day and time windows, and territory planning and routing policies. Intelligent modeling and optimization simultaneously blend all of these options to provide the single most beneficial solution.
Users often treat decision-support systems as wizards, or oracles, forgetting that the output quality is directly proportional to the input quality. Better, more-comprehensive data leads to higher quality and more reliable output recommendations.
Of course, some level of uncertainty is inevitable. Ambiguity and unpredictable events always compromise input quality, and therefore, decision making under uncertain circumstances is understandably, in high demand. Intelligence that can detect and fix data inconsistencies, and provide multiple solutions for different expected situations, can be a powerful and profitable differentiator for shippers.
But it is easier said than done. In addition to algorithms that can detect and fix data inconsistencies, the development of tools that work in stochastic environments (settings characterized by probability and random variables, blended with simulation modules) is required for transportation modeling within the modern TMS (transportation management systems). That modeling enables strategic transportation planners to test various “what if” scenarios to determine the effects of different options and select the most-optimal decision.
With the help of intelligent optimization and modeling, risks in the modern supply chain can be minimized. Manhattan TMS uses machine learning and intelligent algorithms within its transportation modeling engine to ensure the right decision is made the first time, in real time.
For more information about Manhattan Transportation Management System and transportation modeling, please visit manh.com/products/modeling.