Thanks to the changing world under the coronavirus pandemic, we’ve realized how vulnerable supply chains are to dynamic market conditions. Whether it’s shifts in raw material availability or buyer behaviors and expectations, our ability to continually optimize often falls short.
That’s why competitive supply chains leverage constrained optimization (CO) techniques to assure their operations are optimally tuned. CO helps supply chains by identifying the best path forward as dynamic conditions change sourcing options and buyer requirements. These kinds of CO are ideally suited for quantum computers, which simulate real-world scenarios in ways that classical computers cannot.
How Supply Chains Benefit
Among the measures CO can identify are:
- The probable combinations that will result in the minimum supply chain cost for a specific product or a set of products, where the cost is constrained by other costs, such as raw materials, labor or other resources involved in creating the product(s)
- The combinations of suppliers and materials to best achieve this lowest cost
- Logistics options to assure on-time delivery and lowest transportation and warehouse costs for products.
CO benefits supply chains in other ways:
Transportation efficiency. CO can be used to identify optimal locations for plants, distribution facilities and other logistics hubs. Even a difference of 1 mile in the location of a plant can make a significant impact in network costs and productivity.
Warehouse management and distribution services. CO can optimize global and local shipping and loads, warehousing and delivery for lowest cost, optimum efficiency and productivity.
Inbound logistics. From order levels to delivery to the production line, optimization can drive maximum production levels at the best cost. One lost shipment or forgotten vendor can wreak havoc on a production line.
Consider, for example, how a major chip manufacturer — before the current semiconductor supply chain and shortage issues — utilized CO to reduce supply chain costs significantly for a new chip.
When the process started, manufacturing costs were approximately US$5.50 per chip. When units were selling for $100, that was a reasonable cost. However, its newer chip cost more to manufacture, about $20.
The only way to profit on the newer chip was to reduce inventory levels, which had been kept high to support a then-lengthy nine-week order cycle. To achieve the supply chain cost needed, the cycle time and resulting inventory costs/time needed to be reduced dramatically.
Through the power of CO computations, the chip manufacturer identified the optimum cycle time and how to adjust to meet a new order time of two weeks.
The result? A supply chain cost reduction of about $4 for each new chip, fulfilling the required cost to deliver the profit margin required for mass success.
The Future is Quantum
The computational load required to process large datasets in a reasonable time frame is huge, and current computers typically lack the performance and accuracy needed. Quantum computing techniques, however, can empower CO to a new levels of accuracy and performance by allowing you to represent variables (like trucks, people or stock-keeping units), constraints (cost, volume and time) and objectives within a multidimensional state.
Thus, quantum techniques can be used to process data, requirements and goals in a way that represents the real world, including interrelationships and dependencies. As the data is processed, it changes states to represent the probabilistic cause-and-effect relationships, and the real-world scenarios that result. Organizations can view the probable outcomes of various potential business decisions and select the one that best meets their current situation.
When supercharged with quantum-ready techniques, CO can deliver the deeper insights organizations need to get and stay ahead. These techniques help companies make better decisions to improve supply chain efficiency, leading to better business outcomes.