COVID-19 taught us the importance of supply chains when everything from raw materials to finished goods became delayed or simply unavailable to manufacturers and retailers. It also accelerated a dramatic shift in the logistics and delivery side of the supply chain equation as consumers moved from brick-and-mortar purchases to online shopping. The dynamic nature of the full supply chain is now a given, demanding significant shifts in the way we view optimization.
The goal of a supply chain organization is to meet customer requirements while minimizing total supply chain costs. Businesses must be flexible enough to respond quickly when disruptions occur.
Unfortunately, most of us aren’t as agile as we could be, as this research from Ventana points out:
Additionally, the last mile grows even more complex. The last mile has always been the most expensive, long-bemoaned challenge of the supply chain. With the “new normal” of changing consumption habits and channels creating unpredictable demand, forecasts have become meaningless. This makes agility and speed to optimization that much more important to meet customers’ growing expectations for instant availability and near-immediate delivery.
A fixed logistics model is not designed to be flexible or fast. Capgemini Research Institute, Supply Chain Survey 2020 found that 70% of companies are prioritizing inbound and outbound logistics as part of their supply chain sustainability efforts post Covid. Yet less than half of organizations asked by Accenture agree they’re currently meeting customer expectations for order fulfillment.
What happens when the industry becomes even more dynamic and customer expectations require that time cycles compress?
The Missing Link
Constrained optimization helps manufacturing supply chains by identifying the best path forward as dynamic conditions impact sourcing and logistics options. In simple terms, constrained optimization guides you to decide how to do more with less, or how to use less to do more.
Most economic business decisions require applying constraints, such as cost, volume, or time to a set of variables, such as trucks, SKUs, or people with an objective to minimize (cost) or maximize (profit) outcomes. Every organization has a multitude of such optimization problems to solve.
This sounds like something we should be using, right? But there are a few reasons why we don’t:
Classical vs. Complex
Many of us have heard of the traveling salesman problem, which can be compared to truck routing and how to optimize the routes, as well as the trucks. The challenge is that traveling salesman problems like this grow in complexity by n! (n factorial). Routing problems are more constrained and complex for every variable (truck, route, driver, etc.) that you add. For example, a traveling salesman problem that has 10 stops results in 3,628,800 route options, 40 stops will result in approximately 40! = 815,915,283,2 00,000,000,000,000,000,000,000,000,000,000,000,000 options. Routing multiple trucks and packages is even more complex.
A classical computer would struggle under the weight and scale of a vast set of possibilities. This is where quantum computers promise to take on the task to quickly produce options to choose from to make the best decision based on your goals.
Complicated scenarios meant to solve for multiple variables are not achievable by a classical computing algorithm in a short span of time. However, algorithms using quantum computing techniques can quickly achieve this simulation using a classical system applying quantum techniques, or a hybrid solution that employs both quantum and classical, today.
Accenture concurs, stating, “Route-optimization algorithms are helping reduce mileage and improve on-time delivery rates. In logistics, quantum routing uses cloud-based, quantum computing to calculate the fastest route for all vehicles, taking into account millions of real-time data points about traffic congestion.”
Here are a few additional ways constrained optimization benefits manufacturing supply chains, from inbound raw materials to outbound distribution:
IDC research concludes: “The ability to ingest broad and deep data sets to inform better decision making will be the single largest differentiator of supply chain performance in the future.” Quantum computing techniques empower constrained optimization to a new level of accuracy and performance.
Quantum computers process complex computations to return a diversity of answers, not just one. Every answer that meets the optimized state you need is delivered to you. You get exposure to more viable options than with classical processors and can select the one that best matches your specific situation right now. This is a much better way to make decisions vs the classical software approaches that provide a single answer as your only option.
Quantum computing is one of the most promising technological innovations likely to shape, streamline and optimize the future of the supply chain. It offers better insights to make better decisions. That’s why there’s so much excitement about it.
Robert Liscouski is president and CEO of Quantum Computing Inc.
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