Author: Z. Caner Taşkın
There’s a popular saying that goes: “If the only tool you have is a hammer, you tend to see every problem as a nail”. This has essentially been the attitude of supply chain companies over the past several decades when it comes to optimization software technologies.
Having invested extensively in algorithm-based optimization software products, these companies tend to see optimization as the “magic bullet” that can provide a perfect solution to all their supply chain planning and decision-making dilemmas – but this is simply not the case.
Indeed, more and more companies are starting to realize that optimization tools cannot fix all their supply chain woes, and that these solutions are not always able to provide the operational and financial benefits they are expecting.
This is because – frankly speaking – optimization algorithms cannot necessarily find “truly optimal” solutions to every supply chain planning and decision-making challenge.
Many real-world supply chain processes and abstract business concepts simply cannot be defined and modeled satisfactorily using mathematical optimization – and in these instances it is necessary to deploy other algorithmic tools (oftentimes in conjunction with an optimization solver).
Selecting the right tool
There are dozens of supply chain planning and decision processes – from order promising to demand forecasting, network design, real-time dispatching and inventory, capacity, and supply management. Each one of these processes presents a unique planning and decision-making challenge and requires a unique solution.
There are many problem-solving tools and techniques that may be more suitable for a specific supply chain planning and decision-making challenge. Here are some:
- Constructive heuristics
- Machine learning
- Artificial intelligence
For example, there may be cases where the optimization approach (which views every problem as a mathematical problem and typically takes a fair amount of time to solve it) is not the best way to deal with a particular supply chain planning or operational issue, while a heuristics-based approach (which employs a practical process commonly referred to as “rule of thumb” or “best practice”) is able to produce a fast, feasible, and more effective solution.
Or perhaps a hybrid solution approach that incorporates both heuristics and optimization algorithms (possibly along with other algorithms) would be best.
It all boils down to selecting which is the right algorithmic tool or set of tools to use to solve a given supply chain planning or operational problem and optimize a particular planning and decision-making process.
Having a full algorithmic toolbox
Optimization is without a doubt an essential “hammer” for a supply chain company to have in their algorithmic toolbox, but to truly reap the rewards of automated, algorithmic supply chain planning and operations, they need to invest in a platform that possesses multiple problem-solving techniques and enables them to holistically address each separate decision process in their organization and find the best tools to support that particular process.
It is important to remember that the ultimate goal of implementing an algorithmic supply chain software solution is not optimization itself, but rather to enable optimized strategic, tactical, and operational decisions that drive greater productivity and profitability.
Indeed, to unleash the full power of optimization it needs to be integrated and utilized on a robust algorithmic platform that empowers your planners and other key stakeholders to make optimized decisions.
Does your company have the algorithmic tools that you need to solve your supply chain planning and operational problems and achieve decision-centric optimization?
ICRON is equipped with multiple problem-solving techniques. To achieve the state of greater productivity and profitability on top of optimization - what we call at ICRON “ decision-centric optimization ”, you need a full and complete algorithmic toolbox at your disposal!