Market dynamics and the availability of certain products and components constantly impact the supply chain. Concurrently, companies may need to adjust their purchasing practices and volumes if sales forecasts, product specifications, regulations or prices change. In addition, unforeseen factors, such as natural disasters, regional conflicts and defective materials, can also derail procurement practices. The best-laid plans of the smartest companies can become instantly erroneous due to any number of these external factors. One way to plan? Supply chain network optimization.
From a strategic perspective, supply chain success hinges on flexibility—the capability to adjust strategies, tactics and locations to reduce risk, cut costs and attain competitive advantage. Network optimization employs a combination of extensive data and sophisticated analytics that provide an end-to-end, quantitative snapshot of a company's supply chain. Simply told, it is a network imitation model that analyzes "what-if" scenarios for the supply chain.
For example, consider a company that manufactures Printed Circuit Boards (PCBs) in China and sends them to Mexico for integration into a high-level assembly or finished product. From there, the company ships the final product to distribution centers in the US to be sold to the end-customer. Through each step in this process, there could be opportunities for cost-savings and risk mitigation. It's a matter of asking the question "what if...?" Some of the questions below could serve as a good starting point:
To answer these questions and come up with alternative supply chain models, substantial data is required. We can group some of the data into the following categories:
Then, these data points can be utilized to compare elements such as transportation type and frequency, trade compliance, inventory levels and other optimum conditions. Through supply chain network optimization, companies can determine how, and to what degree, all these variables contribute to a total landed cost for any combination of circumstances, simplifying the bottom-line calculation that results from complex supply chain decisions.
For best results, the information is reviewed with an outlook of three years to analyze the riskiness. After all, the supply chain footprint can't be modified every quarter. It takes years to complete a full transition of the supply chain. Therefore, decisions must be made on a long-term basis. Such knowledge and flexibility are crucial to enabling companies to adjust their strategies and achieve market victory.
While each network optimization exercise is unique, the ultimate goal is to prevent supply chain disruption and drive cost-savings. Organizations can gain competitive advantage by running supply chain network scenarios, evaluating and proactively implementing changes in response to dynamic business situations such as new product introduction, changes in demand pattern, addition of new supply sources, changes in tax laws and currencies.
Network optimization provides a powerful modeling approach proven to reduce supply chain costs and improve service levels by better aligning strategies. By incorporating end-to-end supply chain costs—including purchase, production, warehousing inventory and transportation—a network optimization exercise enables your company to make proactive decisions toward planned or unplanned factors.
Most companies currently conduct their supply chain network optimization activities on a project-by-project basis. If trade regulations change, how will it impact your supply chain? If a component supply shortage is occurring, how are your products impacted? If a natural disaster occurs, what options will you have to move forward? While these questions can be answered with supply chain network optimization exercises, the process is still quite manual. However, in the future, we can expect network optimization to be an ongoing effort, where technology will take care of all data inputs—both internal and external—for constant monitoring.
When continuous tracking becomes standard practice, we can expect cognitive analytics to play an important role in supply chain network optimization. The best way to conceptualize cognitive analytics is to think of it like artificial intelligence. At its core, the term cognitive analytics describes algorithms that can learn the correct answer and become smarter over time. In the supply chain, the emergence of cognitive analytics means that many pedestrian, day-to-day decisions will be actively managed by supply-chain software that will only alert human operators when unforeseen situations arise.
Cognitive analytics technologies must perform four distinct tasks:
These capabilities will give practitioners the ability to see if their proposed solutions will actually produce the intended outcomes. These solutions can involve various areas, including transportation, sourcing, or even having the right level of inventory to meet demand.
The evolution of network optimization will be a critical one for the modern supply chain—one that will lead to better collaboration, visibility and decisions across the company.
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