Better ways to address trade-offs with supply chain optimisation

Malcolm McLean, a trucker from North Carolina, was tired of waiting. The story goes that he had a load full of cotton bales, and, while idling away for hours at a shipyard watching stevedores load other cargo onto ships, he dreamed up the idea for the containers we use today.

 

Those simple boxes have transformed global supply chains, and the world itself. Before Containerisation (BC), loading and unloading trucks was inefficient, and McLean’s frustration led to the commercialisation of containers that could be moved on and off trucks easily. Containerisation eventually reduced shipping and loading costs by at least 75 per cent. The trade with Asia we take for granted today was only possible by mitigating a significant supply chain trade-off – reducing costs without appreciable impacts to quality and service. And supply chain optimisation has improved further in significant ways that can address these trade-offs better than before.

 

Trade-offs are inherent in supply chains, so companies that effectively address them gain a significant competitive advantage. Operational innovations such as the invention of containers led to the huge growth in global value chains, and today 95 per cent of manufactured goods move on ships. Technical innovations can also impact supply chains, which is why many turn to supply chain planning solutions, which are designed to be trade-off machines. Work once done manually by planners can now be calculated by software. But advances in this area have two limitations – the software itself, and our ever-changing world.

 

The myth of the “perfect plan”

The promise of supply chain planning software has led leaders to chase the mirage of the perfect plan. Analytical techniques such as linear programming can create a mathematically “optimal” plan, but these methods must be implemented well to avoid creating other challenges. A plan is a point in time, so any solution must be paired with agility to adjust when the inevitable changes occur. Optimisation has often been applied too narrowly, because, as AI and supply chain expert Professor Nada Sanders articulates, analytical techniques applied to just one node only create highly efficient silos. Optimising one link doesn’t optimise the entire supply chain – you must consider the impact of any decision on the entire network.

 

Poor implementation of optimisation has also created biased perceptions of mathematical optimisation algorithms as slow or rigid. Optimisation is a very powerful approach to addressing supply chain trade-offs with complex interdependencies, but improper model formulation (how the maths is set up to address the problem) can add hours to finding a solution. And if the tools don’t allow flexibility for the planner using them, the resulting rigidity can limit their utility and planners resort to manual estimates. The irony is that software designed to address trade-offs falls prey to a trade-off itself – the belief that a planner must choose between a perfect (but rigid) plan that requires a long wait, or a suboptimal but flexible plan they can generate quickly.

 

Software written to solve yesterday’s problems also doesn’t account for the manifold ways the global value chains shipping containers helped enable have also increased in complexity. Combined with Covid-19 unleashing a series of impacts that are still working their way through the chains, as well as growing concern about climate change, the result is that trade-off considerations must account for cost, quality, service and now sustainability. The problem space has become harder, not easier. 

 

Supply chain optimisation for today’s realities

Thankfully, technical innovations have emerged that can help narrow this problem space. Algebraic gymnastics cleverly cut down the computation time needed by optimisation solvers, which run on much faster modern machines. Better problem formulation targets the maths more precisely, speeding up runtime. And fusing mathematical techniques together allows each approach to bring its best to problems. Fast heuristics can narrow the problem space with a near-optimal but feasible solution that is also adaptable in real-time. We can think of optimisation to parameterise the heuristics, or in lay terms, optimisation guides the forest and heuristics guides the trees. And then we can layer in AI and machine learning to speed up the quality and speed of the solution by intelligently selecting the best algorithmic approaches. Optimisation is then positioned to run faster in a narrowed decision space but with flexible objectives and granularity across any horizon.

 

Fusing analytical approaches improves the maths behind optimisation, but to avoid highly efficient silos it should be paired with concurrent planning. Common approaches to optimisation involve using a spreadsheet solver or third-party optimisation tool and then passing it to the planning solution, a workflow that increases the risks of errors in handoffs. When the master data and results are all in the planner’s regular workflow, not only are these risks mitigated but visualisation of the results is in context of the problems they are trying to solve, which greatly increases understanding and likelihood of adoption by making what might otherwise be viewed as “black box” output more explainable.

 

There are many forms of supply chain complexity. One is managing trade-offs with multiple interdependencies, a situation well-suited to optimisation. In this use case, optimisation helps a planner identify the best plan based on what is known. But planning for what might happen in the future, especially when there are the inevitable disruptions, is another dimension of complexity for which creating scenarios can be critical. Scenarios allow planners to ask all kinds of what-if questions to help determine the best course of action under a variety of future outcomes.

 

Why supply chain optimisation matters

If you’re a business leader, why do you care about better maths? Imagine you are a high-tech manufacturer with more inventory in stock than your forecasted production volume, which puts you in good company, since US manufacturers’ inventories continue to rise.

 

Not only are these pre-purchased components and assemblies gobbling up cash, they raise the risk of obsolescence, which these days represents not only wasted resources but also a potential hit to sustainability metrics. You have a limited procurement budget but want to maximise revenue with a supply-driven production plan that minimises waste. What additional components would allow you achieve your business objectives? Fusing fast heuristics, optimisation, and machine learning can answer that question, and scenarios can help identify the budget worth allocating. One company using this approach was able to invest only $3,000 to purchase additional inventory but generate an incremental revenue gain of $110,000. Now that’s an ROI! 

 

Other problems that benefit from this fusion of fast heuristics, optimisation and machine learning include the common blend challenge prevalent in life sciences – how do you make the “best” use of the available ingredients and select the optimal processing techniques to maximise total demand satisfied? Or optimal distribution – what’s the best way to maintain balance through disruption and shortages while eliminating delivery bias across the network? These are critical and expensive problems in need of better solutions.

 

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Polly Mitchell-Gutherie, VP of Industry Outreach and Thought Leadership, Kinaxis