Most supply chain planners are familiar with Dwight D. Eisenhower’s observation, “In preparing for battle I have always found that plans are useless, but planning is indispensable.” While I agree with the late five-star general and president about the indispensability of planning, I disagree that plans are useless. Why? Because new planning techniques are making plans more realistic and viable. Supply chain planning is difficult in the best of times and made even more challenging during the worst of times. Justin King, a Technical Business Consultant at Kinaxis, writes, “Pause for a moment to think about what the world’s supply chains are tasked with on a daily basis and how much they achieve. It’s staggering. Now factor in the platforms, processes, and procedures upon which many of the world’s critical supply chains are built and it quickly becomes clear that the day will come that they can no longer keep up.”[1] He adds, “Pair present reality with supply chains that continue to employ legacy processes and siloed technologies and you have a model that, if left unchecked, is unsustainable. And as any planner knows, any delay in their ability to respond to an exception in its company’s supply chain spells certain doom for the company’s ability to meet production and delivery deadlines.”
Cognitive technologies and supply chain planning
Supply chain and business planning expert Niels van Hove agrees with King that legacy planning systems are no longer adequate. He insists companies “need to implement a third wave of integrated supply-chain planning software.”[2] He goes on to argue “these technological advances can lead to either (a) planning-process and decision automation or (b) planning and decision augmentation.” Some analysts believe the goal companies should be pursuing is autonomous (i.e., lights out) planning. Van Hove is not among them. He “believes that human centricity is critical so that decision augmentation should be the more desirable form for business planning.” Cognitive computing, a form of artificial intelligence, was designed specifically to augment human decision-making. The Cognitive Computing Consortium explains:
“Cognitive computing makes a new class of problems computable. It addresses complex situations that are characterized by ambiguity and uncertainty; in other words, it handles human kinds of problems. In these dynamic, information-rich, and shifting situations, data tends to change frequently, and it is often conflicting. … To respond to the fluid nature of users’ understanding of their problems, the cognitive computing system offers a synthesis not just of information sources but of influences, contexts, and insights. To do this, systems often need to weigh conflicting evidence and suggest an answer that is ‘best’ rather than ‘right’. Cognitive computing systems make context computable. … Their output may be prescriptive, suggestive, instructive, or simply entertaining.”[3]
Hank Canitz, a Product Marketing Director at Logility, notes, “There are a number of artificial intelligence/machine learning capabilities available today to automate supply chain processes and augment your supply chain team’s ability to make decisions.”[4] Among those capabilities, Canitz notes, are: Optimized Forecast Algorithm Selection capabilities; Forecast Parameter Optimization capabilities; Demand Outlier Adjustment capabilities; Demand Sensing from Unstructured Data capabilities; Probabilistic Demand Simulation capabilities; and use machine learning to understand forecast variability at the record level. In contrast to single-valued forecasts, probabilistic demand simulation builds a range of possible Automatic Data Cleansing and Parameter Population capabilities. He goes on to note:
“In study after study there is evidence of the growing use of AI & ML across all business functions, including supply chain planning. There are AI/ML capabilities available today being used to enhance supply chain planning operations. What AI/ML capabilities are advanced supply chain solution providers actively developing that will be available in the near future? Cognitive Analytics, the most advanced type of analytics, enable users to identify ‘New Insights’ through the use of AI, Machine Learning, and Natural Language Processing. Cognitive analytics also enable autonomous analysis and response, freeing up manpower to work on more value-adding activities. Additional cognitive analytic capabilities are being developed to automatically sense, analyze and respond to unplanned disruptions and opportunities, helping to minimize risk and maximize company benefits.”[5]
Gaurav Palta, head of the Consumer Goods Business at Noodle.ai, writes, “Given everything that’s unfolded in 2020, it’s not surprising that the top concern for supply chain planning teams is unpredictability. … The most effective antidote to unpredictability — implementing predictive, probabilistic planning tools that use AI.”[6] He admits many vendors have tried to oversell the capabilities of cognitive technologies and, as a result, many AI solutions are deemed little more than snake oil. He concludes, “The skepticism about AI-Snake-Oil and marketing hype by supply chain leaders is understandable. However, there are plenty of examples of organizations successfully handling the current uncertainty by using intelligent planning tools. In order to be successful, an AI/ML application vendor needs to be explicitly clear on their target functional use case, the value impact timeframe and how they will measure it and prove it.” If decision-makers keep in mind that cognitive computing is about augmenting decision-making rather than providing black-and-white answers, they can effectively use its capabilities to navigate the new normal as they emerge from the pandemic.
Anil Kaul (@anil_kaul), co-founder and CEO of Absolutdata, insists, “Consumer packaged goods companies who use artificial intelligence are finding a shortcut to the new normal.”[7] To get to the “new normal,” Kaul suggests CPG firms need to answer two questions: “What will consumer behavior and profiles look like in the new normal? What will the demand pattern look like going forward?” He adds, “To put it another way, you need to know what people will buy, where they will buy it, how they’ll buy, what quantities they’ll buy and how often they’ll buy. Prior to the pandemic, historical data provided some guidance. Now, all that manufacturing, logistics, pricing, customer engagement and general commerce data is obsolete.” At Enterra Solutions® we found ourselves supporting clients faced with just such a dilemma. As a result, we found new ways to combine and analyze data in the Enterra Global Insights and Optimization System™ to help find answers to the questions Kaul poses. As Kaul notes, “CPGs are building AI models to help them predict general demand curves. AI can evaluate multiple data sources, such as internal company data and publicly available information like anonymized mobile or text data. That way, it can predict when significant changes are imminent, such as accelerating or decelerating movement in regional infection rates that might trigger a shutdown or a reopening that could affect supply chains, production or demand.”
Concluding thoughts
Canitz concludes, “The introduction of AI & ML into supply chain operations can propel your business into the future — harnessing automation, optimizing supply chain planning, and evaluating multiple scenario outcomes to boost your confidence in decision-making.” Decision-making is hard and supply chain planners have to make some of the toughest decisions in business. Cognitive technologies can help augment their decision-making with the latest, cutting-edge analytic tools.
Footnotes
[1] Justin King, “Concurrent planning, technically speaking,” Kinaxis Blog, 7 November 2019.
[2] Niels van Hove, “Technology Support in Integrated Business Planning: Automation, Augmentation and Human Centricity,” Supply Chain Trend, 13 August 2020.
[3] Staff, “Cognitive Computing Definition,” Cognitive Computing Consortium.
[4] Hank Canitz, “Artificial Intelligence (AI) & Machine Learning (ML) in Supply Chain Planning (Part 1 of 2),” Logility Blog, 7 July 2020.
[5] Hank Canitz, “Artificial Intelligence (AI) & Machine Learning (ML) in Supply Chain Planning (Part 2 of 2),” Logility Blog, 21 July 2020.
[6] Gaurav Palta, “Supply chain concern #1: Unpredictability. The cure? AI-enabled supply chain planning,” Diginomica, 28 July 2020.
[7] Anil Kaul, “Using AI as a Shortcut to the New Normal,” Path to Purchase IQ, 30 June 2020.
