“What if it’s cold inside the movie theater?” “What if I win the lottery?” “What if it starts to rain?”
We ask ourselves “what ifs” every day, and how we answer those questions often determines our course of action. Considering a situation from every possible angle is an essential aspect of decision-making.
The same concept applies to supply chain management. Possibly the most predictable aspect of supply chain is the impossibility of predicting the next crisis with total certainty. Ever-changing market dynamics and unreliable component availability force supply chain leaders to wonder, “What if?”
That is the crux of supply chain network optimization: contemplating every possible obstacle and developing a subsequent strategy. Therefore, instead of thinking on the fly and reacting as situations crop up, companies can readjust their course according to their established contingency plan. But how can supply chain managers harness network optimization to construct the most effective and resilient supply chain possible?
Before getting into the challenges modern supply chain managers face and how network design can help, let’s rewind for a minute and retrace the roots of many of today’s most prevalent supply chains.
Although supply chains have matured significantly, the decisions that led to their evolution were largely based on the most convenient choice at the time rather than a thoughtful consideration of long-term consequences.
As you can imagine, this has created a tangled web of suppliers, customers, deliveries and distribution centers. The global supply chain is more complicated than ever and becoming even more complex as it strings together continents and bolsters multiple market segments. In this convolution of resources, volatility isn’t an anomaly; it’s almost a constant.
There is a wide array of external factors contributing to the messiness of supply chains. In a recent Jabil survey of more than 300 supply chain decision-makers, participants listed several troubling market dynamics, including increased demand, higher labor costs, supply constraints and more.
Faced with the daunting challenges of navigating these uncertainties, supply chain managers are turning to supply chain network optimization to ensure their supply chain is as flexible and durable as possible.
On an elementary level, supply chain optimization is imagining various circumstances and catastrophes that could arise at different points of your supply chain. 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 allows managers to analyze "what-if" scenarios for the supply chain.
There is a crucial difference between planning and design. Planning deals with the day-to-day logistics, such as transportation, warehousing and packaging. Planning and execution systems automate, streamline and optimize the existing operations of an organization. On the other hand, “design” requires more big-picture thinking. Network design establishes the operations that the logistics run on. While planning is responsible for running an effective business, design determines how a business should run.
As I mentioned before, many modern supply chains were established based on short-term goals, which means that they were not made considering long-term effects and impact. In other words, supply chain managers have historically allowed logistics to determine the maturation of their supply chain when network design-thinking reaps longer-term benefits.
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Looking back over the past couple of years, the supply chain has weathered turbulence and uncertainty. Throughout 2017 and 2018, electronics demand was outstripping supply, subsequently extending the lead times for commodity devices like capacitors, resistors, diodes, transistors and memory. And that wasn’t the only factor threatening the stability of the supply chain; geopolitical uncertainties, higher labor costs and rapid technology transitions also came into play. Considering how heavily a strong supply chain depends on visibility and predictability, this insecure and troubling environment poses challenges for supply chain managers, which a strategic network design can help alleviate.
There are several benefits of network optimization, such as determining the flexibility and resilience in your supply chain strategy, figuring out how to increase or reduce gross margins, assessing how to enter emerging countries and conducting a compliance study on taxes.
In fact, according to our survey, 93% of supply chain decision-makers said that in the past few years, they’ve made changes to their supply chain to respond to existing market dynamics. More than half vouched that they have made significant changes to their supply chain strategy or operations.
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 introductions, changes in demand pattern, the addition of new supply sources and 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. It can also help companies evaluate their current stability of the four pillars of a resilient supply chain: people, process, technology and design.
To illustrate what a supply chain network optimization model looks like, let’s run through a hypothetical scenario.
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.
As I’ve said, to develop a strong supply chain, decisions need to be made based on longer-term goals versus what is convenient at the time. Unfortunately, many supply chain managers continue to think short-term. Most companies continue to conduct their supply chain network optimization activities on a project-by-project basis:
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 it comes to the supply chain, the digital era is well underway. Already, many experts are turning to technology to enhance their supply chain management and assist in their network design. In our survey, “adopting new technology” was the number one response when participants were asked what they were doing to manage risk.
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 as 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.
In other words, technology will evolve to the point that it can handle the logistics, freeing up people for the more intensive work of network design.
Cognitive analytics technologies must perform four distinct tasks:
These capabilities will give practitioners the ability to see if their proposed solutions will produce the intended outcomes. These solutions can involve various areas, including transportation, sourcing or even having the right level of inventory to meet demand.
Supply managers will also start turning more toward predictive analytics to better manage risk.
With such an elaborate network of touchpoints, one mishap could have a calamitous domino effect throughout the entire supply chain. It could cause several additional challenges, such as increasing supply chain duplication and costs and negatively affecting customer satisfaction and loyalty.
The supply chain is an inherently risk-filled field. This has led supply chain decision-makers to invest in a variety of strategic and tactical solutions to minimize their risk, such as adopting new technology, revising their approved vendor list and evaluating their multivendor sourcing strategies. Nearly six out of 10 have also begun including risk management plans for potential price fluctuations in their pricing strategy.
Almost every industry has already started leveraging predictive analytics to some degree. The predictive analytics market is expected to grow from $4.56 billion in 2017 to $12.41 billion in 2022, according to Markets and Markets.
Supply chain predictive analytics software combines big data with “little data to paint a comprehensive picture of what may happen up to 10 days before tender. Modern supply chain predictive analytics rely on advanced technology that can generate logistical forecasts more accurately and over a longer span of time than manual reporting, thereby allowing supply chain managers to make the most well-informed decisions.
“What ifs” can be some of the scariest questions we ask ourselves. To find the answer, we must suspend our feelings of security and confidence and face some the potential of very troubling and difficult scenarios. But by confronting those questions, we can be better prepared if the worst does happen.
After all, a truly strong supply chain isn’t one that can run well when everything is blue skies; it’s one that can continue to grind on when everything is collapsing around it. The evolution of network optimization will be critical for the modern supply chain—one that will lead to better collaboration, visibility and decisions across the company.
Insights from over 300 supply chain decision-makers at OEMs with more than $500 million in revenue on how they are managing their supply chains in light of market forces.