Every year, ASCM’s research team releases a report predicting the top 10 trends for the upcoming year. Topics come and go, but for the second year in a row, the number-one trend is big data and analytics. Not too long ago, the focus was often on data reporting, or descriptive analytics, in which information about past events is analyzed. Although this is a useful exercise, analytics has a lot more to offer. Big data goes beyond capturing and collecting the information that’s amassed from partners, customers and internal operations; rather, it’s about putting that data to work so it can be monitored and converted into useful insights in real time.
A key goal of data analytics is to build resilience, because a resilient supply chain has a clear competitive advantage over those that cannot quickly react and adapt to change. As the 2023 trends report states, “Big data, analytics and automation are enabling organizations to mitigate disruption via digital, agile supply chain management.”
Of course, after several years of major disturbances from COVID, the war in Ukraine and climate change disasters, mitigating further disruption is of significant interest to supply chain professionals. Jit Hinchman, CSCP, founder of Supply Chain Adviser, explains, “The right technology and data collection practices can help organizations and supply chains be more resilient; prepared; and perhaps capable of sensing, minimizing and preventing future disruptions. Interpreting and analyzing data to get insights is far more important than collecting it without purpose.”
Following the Great Recession, many business leaders realized that the only way to recover from uncertainty was to assume another disruption couldn’t be far behind; but not everyone learned that lesson. Suzie Petrusic, in the Gartner Supply Chain Leaders, It’s Time to Rethink Your Recession Playbook, outlines how to better manage processes in preparation for a major disruption. One point: Ensure your company adequately understands the risks it’s willing to undertake. “A critical flaw of the supply chain risk management strategy pre-2019 assumed that supply chains would have the ability to recover from any single high-impact disruption before the next one occurred. The past three years have demonstrated how consequential that assumption has been. Supply chain leaders must reevaluate their risk appetite and adjust to a level that is appropriate to the current environment.”
Strategizing for the future
Just as digital transformation is essential to preparing for the future, using data as a guide is another. In fact, the two work in tandem. Amy Augustine, CSCP, senior director, network supply chain at USCellular, explains that a key piece of a digital supply chain is forming a strategy for handling and interpreting data.
In discussing the transition to a fully digital supply chain at her organization, Augustine reinforces the necessity of having plans in place to interpret the data: “If you only collect data, you are at a disadvantage. You need to have a planned use for the data, understanding what it might or is telling you. Your team needs to be able to weed through the noise and find the data that is going to help your supply chain move forward. … How is the data being used to drive transformation of your supply chain? What is your strategic direction?”
Most organizations today employ predictive analysis, which involves using data to figure out what’s likely to happen in the future. But prescriptive analytics takes this a step further, identifying what should happen, then determining the best course of action. In other words, when is the right time to hire new staff, expand inventory or buy a warehouse?
Logistics company UPS, for example, has long used prescriptive analytics for route optimization and to provide drivers with what company leaders believe is “the best route to follow to ensure drivers can be safe, that we can meet our customer commitments, and that we improve productivity and efficiency in the network,” Juan Perez, former chief information officer, tells Forbes. This might mean anything from providing drivers who are out delivering packages with a new route to avoid road closures or restructuring the delivery schedule to get certain customers their parcels first. More recently, the logistics company has expanded its collaboration with Google Cloud to “increase customer visibility into shipments and to enhance how UPS gives them control over their deliveries” so that the customers themselves can track their packages and decide when or how they’re delivered, improving the overall customer experience.
Boosting value by creating visibility
The technology required for the transition to prescriptive analytics isn’t necessarily new or complicated; however, it does require integrating systems so they can communicate with each other to build sensing and prediction capabilities. “Organizations increasingly need to pull data across the value chain from intelligent sensors, programmed to identify critical events, assess their impact, and adjust planning and control variables,” according to McKinsey & Co. “Similarly, software capable of modeling the implications of various disruptions is also vital. Today’s algorithms can analyze a company’s network of suppliers and determine the total impact if a specific supplier goes down.”
Essentially, these experts are advocating for increased visibility up and down the supply chain. And data analysis streamlines that process. “Traditionally, it takes weeks for disruptions to rise up the supply chain, by which time market conditions may have changed,” Vinayak Agashe, vice president of product management for GEP, tells The Economist. “Real-time information flow to suppliers allows them to adjust their planning and reallocate capacity, while enabling the buy-and-sell processes to work in tandem.”
Zhong adds that immediately sharing this information along the supply chain requires technology that provides real-time, end-to-end visibility; intelligence that enables decision-making across the ecosystem; and better collaboration in multi-tier, multi-dimensional networks.
Augustine agrees: “The right technology and data collection practices help us identify when we do have a possible risk, to make decisions before it becomes a problem, and to pivot the supply chain and allow for very little or no disruption.”
Making the data work
Clearly, there is tremendous value that comes with using data to boost visibility and be a proactive, data-contributing member of your supply chain. Unfortunately, about half of benchmarked companies use only internal data to get their supply chain picture. Some are unsure of what data is useful and how to effectively tap into it. “We cannot skip the ugly part of cleaning [the data], uncluttering it and verifying it before using it,” Hinchman says.
Plus, there’s a learning curve for employees. Cara Curtland, a data science engineer at HP talked to ASCM about supply chain trends including big data. When asked what a supply chain professional needs to know, she explained, “You have to learn the right tools around visibility, around analytics, like artificial intelligence and machine learning. What are those techniques that will help you be predictive and prescriptive around how to orchestrate your supply chain?”
Finally, good, clean data is an asset that requires discipline. When supply chain organizations treat it that way, it becomes a true competitive advantage.
Take a deeper dive into this topic with the ASCM Supply Chain Technology Certificate, which covers blockchain, advanced analytics and automation, demand planning technogies and more.