Supply chains are a mess, inflation is on the rise, the pandemic lingers, natural disasters are intensifying, fraud is growing more prevalent, and global tensions are increasing. This is a big problem. James Johnston (@JamesA_Johnston), Regional Vice President at Cloudera, notes, “Supply chains can be considered the lifeblood of the global economy. When it falters, the knock-on effects can be tremendous. This is none truer than in the last 18 months, where supply chain issues have made news headlines worldwide. From the Suez Canal blockage costing traders more than five billion euros in lost revenue and the Colonial Pipeline ransomware attack that resulted in massive spikes in oil prices, to the desperate rush by retailers to get shelves adequately stocked ahead of Christmas. The scale of supply chain disruptions has reached new heights in recent months.”[1] Risk managers have a term for these conditions: VUCA. VUCA stands for Volatility, Uncertainty, Complexity, and Ambiguity. VUCA conditions may be intensifying, but they are not new. A decade ago, supply chain analyst Trevor Miles (@milesahead) wrote, “I almost feel that we should be shouting ‘Vuka’ which in Xhosa (one of the South Africa languages) means ‘wake up.’ Wake up to the new reality that VUCA is a new norm.”[2] His warning begs the question: How can supply chain risk managers wake up? One answer is advanced analytics.
Big Data and Advanced Analytics
Johnston notes that a rapid rise in e-commerce during the pandemic — “ten years growth in just the first three months of 2020” — made it “apparent that predicting future supply chain demand using outdated data is no longer feasible.” Nevertheless, he notes, “Strong data and analytics capabilities are crucial in understanding the complexities of the modern consumer, anticipating possible disruption, and developing a quick response to resolve any interruption that arises.” The right data and the right analytics, Johnston asserts, “can ultimately help businesses be prepared for any eventuality and avoid hiccups in the supply chain that lead to profits being impacted.” As President and CEO of a company involved with cutting edge analytics, I’m not sure even the best analytics can prepare a company for EVERY eventuality. Like Johnston, however, I am convinced cognitive technology solutions, like the Enterra Global Insights and Optimization System™, can help better prepare businesses to confront most emerging challenges.
Tech writer Vandita Grover observes, “Big-Data and Data Analytics have made a significant contribution to serve the risk management requirements of organizations.”[3] She goes on to note several ways data and analytics can help businesses. They include:
1. Identifying Potential Fraud: Grover notes, “Big Data can be put to use to detect frauds which could take hours of manpower and numerous interviews to zero-in on the likely source.” Several years ago, Susan Lacefield, Executive Editor of CSCMP’s Supply Chain Quarterly, reported that a study released by Deloitte found, “More companies are starting to use supply chain analytics software to alert them to the possibility of supply chain fraud before it happens. … Examples of supply chain fraud include overbilling from third-party vendors, suppliers not complying with contract terms, and suppliers overstating hours worked or billing for material that was not delivered”[4]
2. Identifying Churn and Reducing Customer Defection: Grover, writing before the pandemic, noted, “Using Big Data, Predictive Analytics can look into historical data to identify potential churn.” As Johnston notes, “Using outdated data is no longer feasible.” With consumer preferences and behavior constantly changing, analyzing the latest data is essential to obtain meaningful results. With the right data, solutions, like the Enterra Shopper Marketing and Consumer Insights Intelligence System™, can provide actionable insights concerning customer churn.
3. Adapting to Change: Grover notes, “A good business is one which can react to change and adjust its plans according to market conditions thereby mitigating risks.” Cognitive solutions, like Enterra Supply Chain Intelligence System™, can help businesses understand changing market conditions.
Suresh Acharya, a Professor of Practice in the Online Master’s in Business Analytics at the University of Maryland, insists, “A thorough understanding of how to leverage data analytics to address supply chain disruptions and strengthen overall processes can help to soften that blow considerably.”[5] Acharya recommends risk managers fully acquaint themselves with three types of analytics. He writes, “Keep in mind that these three types of data analysis are more like nesting dolls than a menu of selections; your supply-chain team needs to unpack one type before moving on to reap the benefits of the next.” They are:
1. Descriptive Analytics. “Descriptive analytics leverage data for the purpose of determining what happened in the past. This method of analysis involves choosing data and running reports designed to generate insights based strictly on what has happened so far to give yourself and your company a clear, useful representation of the past.”
2. Predictive Analytics. “Predictive analytics allow supply-chain managers to wield the vast quantities of data in their midst — and the insights yielded by descriptive and diagnostic analytics — to make projections about the future. It’s in generating dynamic forecasts that data analytics begins to demonstrate its power. … An especially useful sub-category of predictive analytics is probabilistic forecasting, which offers a prediction not as a hard number (or ‘point number’ as it’s called in industry) but as a distribution, or a range to plan within.”
3. Prescriptive Analytics. “Prescriptive analytics builds on smart predictions and incorporates a set of rules, constraints, and operational limitations with a specified corporate goal. Its focus is not only on telling you that a disruption is looming but seeks to help you answer what you should do about it. As useful as this type of analysis can be, it’s also the most difficult, and therefore the least attempted — among these three types. For example, if predictive analytics detect latent trends that manifest themselves in a demand projection that is significantly different from original plans, prescriptive analytics can help mitigate that impact by recommending alternate supply scenarios or by prescribing actions that can help shape the demand in the desired direction. Prescriptive analytics may represent the pinnacle of the ways in which data analytics can benefit your supply chain and smooth out disruptions, but it’s important not to try to start there.”
Although the above discussion may make it sound like using advanced analytics is straight forward and easy, that’s not the case. The supply chain is way too complex. Analysts from the University of Pennsylvania’s Wharton School of Business explain, “Globalization can bring numerous benefits, but these come with risks. Each global economic enterprise needs to assess value, and more importantly physical and financial risks that are embedded within an apparently fragile system. This is where the tools and techniques that are housed in the domains of decision sciences and analytics can have enormous impact.”[6] The staff at GlobalTranz, agrees. They believe advanced analytics represent the “better mousetrap” risk managers need to make supply chains more resilient. They write, “Supply chains across the globe need a better mousetrap to keep freight spend under control and efficiently manage freight. And, investment statistics reveal an increasing trend for more technology to enable multi-modal shipping and the application of big data analytics to increase scalability, flexibility, and demand-driven management decisions.”[7]
Concluding Thoughts
When businesses fail to make their supply chains more resilient, bad things happen. The GlobalTranz staff explains, “The challenges of poor supply chain resilience contribute to a loss of visibility and poor responsiveness when circumstances change. The recent disruptions to the supply chain reflect the severe shortfalls that may occur when a back-up plan is unavailable.” Johnston concludes, “Advanced analytics and enterprise data empower companies to have a completely transparent view of movement of materials and products within their line of sight. What’s more, these tools can leverage data from their suppliers to have a holistic view two to three tiers deep in the supply chain. Both of which ultimately help businesses to reduce the risks to supply chain. … Business leaders should therefore consider developing a robust framework that includes a responsive and resilient risk management operations capability. That capability should be rooted in technology, utilizing platforms that support supply analytics, artificial intelligence, and machine learning. This will not only ensure full transparency across the supply chain but also get organizations up for business-as-usual, even if the worst happens.” Despite hyperbolic terms such as “completely transparent” and “fully transparent,” for most companies achieving that type of visibility remains aspirational. Nevertheless, advanced analytics capabilities are an essential first step towards that goal.
Footnotes
[1] James Johnston, “Minimising supply chain disruption with advanced analytics,” IT in the Supply Chain, October 2021.
[2] Trevor Miles, “VUCA, a useful acronym for today’s supply chain,” Kinaxis Blog, 9 June 2011.
[3] Vandita Grover, “6 Ways Big Data helps Companies Mitigate Risks,” MarTech Advisor, 2 May 2018.
[4] Susan Lacefield, “More companies are using analytics to detect supply chain fraud, says Deloitte,” Supply Chain Quarterly, 8 December 2017.
[5] Suresh Acharya, “Three Types of Analytics That Can Be Used to Address Supply Chain Disruptions,” Industrial Distribution, 18 March 2019.
[6] Staff, “Analytics, Risk and Managing the 21st Century Supply Chain,” Knowledge@Wharton, 17 April 2020.
[7] Staff, “Big Data Analytics to Improve Supply Chain Resilience,” GlobalTranz Blog, 27 April 2020.
