Responsive Enterprises Need a New Definition of Supply Chain Resiliency
Companies have traditionally judged a supply chain’s resilience by the ability to recover after black swan disasters and upheavals. Although preparedness for natural disasters is important, black swan events remain the exception, not the rule. A resilient supply chain in the age of the responsive enterprise must tackle a more complex challenge: it must help a company react to small, frequent changes such as those in customer expectations and demands, supply constraints, regulations, market variability, and competitor moves. This capability provides significant competitive advantage, by allowing companies to capitalize on fleeting opportunities and respond to changes in days or weeks, instead of months and quarters.
In the past, companies could manage the amount of variability their supply chains would confront, and develop strategies to deal with those variations. Today, however, business complexity is mounting relentlessly, and it is no longer possible to identify all scenarios from the start. For example, companies across vertical industry segments face ballooning product proliferation and micro-segmentation of customers. Also, supply chains have shifted from vertically integrated systems to virtual, global networks of partners. Consumer product companies, for example, that used to own manufacturing plants, distribution centers, and trucks, now outsource much of that work.
To meet today’s supply chain challenges, companies are building capabilities to predict changes, incorporate data from new sources, identify best courses of action, and implement decisions quickly.
Predictive analytics and simulation tools help companies assess and model the impact of changes and actions.
Increasingly inexpensive computing power is making it possible to use these technologies to manage supply chains at granular levels. Retailers, for example, can spot items selling faster at one location than at another and shift inventory to avoid stock outages and increase market penetration. This is an example of modern supply chain resilience: acting quickly on valuable data gleaned in everyday operations to prevent losses and promote gains. Often, however, the granularity of data being considered is not fine enough, given the availability of new data sources such as Internet of Things (IoT) devices, social analytics, and customer sentiment tools.
Reinventing the supply chain also requires overcoming significant talent hurdles. Let us explore the reasons why the supply chain wisdom of yesteryear does not work for today’s digital business, and determine how to overcome the barriers to a resilient supply chain.
The Trouble with Traditional Approaches
Traditional approaches can help companies build resilience into the supply chain through excess capacity, inventory, or expedited supply chain processes. However, this flexibility comes with additional costs, thus lower margins.
Many companies also divide their products into abstract, ‘manageable’ categories that miss a great deal of detail. This abstraction hides information available in the supply chain which could be used to predict supply chain issues or opportunities. For example, rather than trying to forecast and manage thousands of television stock keeping units (SKUs) at a store level, an electronics retailer may focus on a few product families or categories, such as smart TVs at a regional level like Texas, instead of 42-inch smart LED TVs at a particular store. Such abstraction loses a lot of the detail which could allow the company to spot supply chain issues or opportunities and thus build resilience.
The performance-to-cost ratio of computing power has steadily improved, making it possible to manage data at a more granular level and eliminating the need to manage by abstraction. Thanks to technologies such as in-memory computing and improved analytics algorithms, companies can now analyze complex supply chain questions that used to take days or weeks, in a matter of minutes or hours. For example, one major consumer goods manufacturer uses its Internet of Things data to replan production lines based on actual demand variation once or even twice daily. Cloud computing and managed services have provided options to take advantage of technologies without laying out large capital and large teams to maintain and support the systems.
In addition, granular data about customers is much more readily available. Social media and consumer analytics can surface changes in customer tastes, expectations, and attitudes about products and services, including competitive ones. Based on popularity trends shown by social media, product can be redeployed according to demand. Social data can also reveal sentiments about specific channel partners. Incorporating this unstructured external data into analysis, along with sensor data and IoT data, can provide further insights into supply chain changes or opportunities.
Another challenge companies face is traditional thinking of supply chains as monolithic processes where all pegs are square. A single physical supply chain needs to accommodate customer needs through various channels and customer segments. Different customer segments have different expectations on service levels and costs, which will lead to different supply chain policies and approaches. Amazon.com’s book division caters to everyone from casual fiction readers to students and professionals who need books in a hurry. Amazon does not have separate warehouses and trucks for each customer segment. Its supply chain is engineered to respond to different customer needs based on what they are willing to pay and how long they are willing to wait.33