The late President and 5-star General Dwight D. Eisenhower once stated, “In preparing for battle, I have always found that plans are useless, but planning is indispensable.” Over the years, there has been a lot written about why planning is indispensable, even if plans are useless. Here’s my two cents. Planning is indispensable because, done right, it explores a number of scenarios and involves a lot of “what if” thinking. Exposure to numerous scenarios and what-if questions means that organizations are much better prepared if the future doesn’t unfold in exactly the way their plan predicts. The emergence of artificial intelligence (AI) technologies, particularly digital twin technology, is making it much faster for organizations to explore hundreds of scenarios in very little time.

Supply Chain Planning and Digital Twins

Chap Achen, Vice President of Product Strategy & Operations at Nextuple, explains, “A digital twin allows a business decision-maker to test and validate a complete set of strategies and objectives across teams and make smart decisions that improve the customer experience and maximize profitability.”[1] That simple sentence is packed with information. First of all, it links planning and decision-making. Supply chain expert Niels van Hove notes, “What used to be planning, or a planning system, is now sometimes referred to as planning & decision making. This is most likely influenced by the acknowledgement of a new category decision intelligence by Gartner.”[2] According to Gartner, “Decision intelligence is a practical domain framing a wide range of decision-making techniques bringing multiple traditional and advanced disciplines together to design, model, align, execute, monitor and tune decision models and processes.”

The second concept contained in Achen’s sentence about digital twins is the idea that digital twin technology can validate “a complete set of strategies.” Cognitive technology solutions, like the Enterra Global Insights and Decision Superiority System™ (EGIDS™), can help companies perform important “what-if” exercises to help them anticipate and plan for the future. The System can run hundreds of scenarios in a very short period of time to help decisionmakers better understand their options. Within the consumer packaged goods (CPG) industry, digital twins have potential uses for optimization, simulation, decision support, root cause diagnosis, reporting, alerting, and serving as a data repository for training machine learning (ML) models with a much broader data scope.

The third concept contained in Achen’s statement about digital twins is that they can help improve planning and decision-making “across teams.” Planning and optimizations are done in many departments within a business, such as supply, manufacturing, distribution, warehouse, transportation, budget, labor, and so on. Often, departmental planning functions are disconnected from one another and may have conflicting goals. In order to deconflict goals, companies need to create an objective function that balances these goals. Developing an objective function ensures corporate alignment in order to optimize operations and maximize profits. The Enterra Supply Chain Optimization System™ can help achieve that goal, once digital twin technology has helped decisionmakers determine the best strategies to implement. Companies are looking to improve their supply chain processes by having all planning decisions aligned to maximize the same overall enterprise objectives, and to reduce and improve upon manual operations within the process performed by human planners.

Finally, Achen’s sentence about digital twins insists the overarching goal of digital twin technology is to make smarter decisions that improve and maximize profitability. Not all decisions need to be made by human planners. To that end, Enterra Solutions® is advancing Autonomous Decision Science™ (ADS®) so that human planners are freed to concentrate on decisions requiring nuanced expertise. Achen concludes, “Digital twins offer an effective solution that allows [organizations] to model a host of scenarios via virtual models of their production supply chain.”

Better Decision-making Matters

Van Hove writes, “Let’s start with what a decision is. According to the Oxford dictionary: ‘[A decision is] a conclusion or resolution reached after consideration.’ This implies there needs to be some human or other cognition, reasoning, trade off and judgment involved. [A second definition is] ‘… the action or process of deciding something or of resolving a question.’ In this definition, one needs to ask him/herself: ‘What should I do under these circumstances?’ and provide an answer to it.” His point is that the best decisions are made when a number of choices are available from which to select. Van Hove goes on to explain that decisions only matter when resources are committed to that decision. He writes:

“According to authors of the book Decision Quality, a decision is only made when resources are irrevocably allocated to the execution of the decision. Without it, multiple planning options, as advanced as they may be, remain calculations, maybe insights, at best recommendations. Decision making is, therefore, taking an action when there are alternatives, or as a human, take responsibility for the action taken by another entity (often a machine). … Decision quality for a planner can only be achieved if a planner chooses one of multiple significantly different scenarios. If not, the decision quality is very low, not likely a better decision.”

Bain analysts, Michael C. Mankins and Lori Sherer, explain, “The best way to understand any company’s operations is to view them as a series of decisions.”[3] They add, “We know from extensive research that decisions matter — a lot. Companies that make better decisions, make them faster and execute them more effectively than rivals nearly always turn in better financial performance. Not surprisingly, companies that employ advanced analytics to improve decision making and execution have the results to show for it.” If van Hove is correct — and I believe he is — the benefits of decision-making are dramatically improved by increasing the number of scenarios a decisionmaker can examine. Digital twin technology is key to making that happen.

Concluding Thoughts

According to Roshan Gya, an executive with Capgemini, “By bridging the ‘physical-digital’ gap … digital twins offer a unique opportunity for organizations looking to accelerate their journey towards intelligent operations while increasing profitability and enabling a sustainable future.”[4] As I noted in a previous article, “Digital twin technology will continue to help companies perform ‘what if’ scenario analysis and, when prudent, make autonomous decisions to optimize processes as they improve their phygital fitness.”[5] Dick Weisinger, Vice President and Chief Technologist at Formtek reports, “A [study] by CapGemini found that companies are increasingly using digital twins to help them more efficiently utilize resources and optimize processes like logistics. … The CapGemini report found that ‘digital twin deployments are being driven by both top and bottom lines, as well as safety, sustainability and brand reputation. Organizations working on digital twins have already seen, on average, a 15% improvement in metrics such as sales, turnaround time and operational efficiency, as well as an improvement upwards of 25% in system performance.’”[6] If your company hasn’t considered leveraging digital twin technology, the time is right to do so.

Footnotes
[1] Chap Achen, “Digital Twins: The Secret to Better Retail Planning,” RT Insights, 13 March 2023.
[2] Niels van Hove, “A Better Plan is not Necessarily a Better Decision,” Supply Chain Trend, 22 January 2023.
[3] Michael C. Mankins and Lori Sherer, “Creating value through advanced analytics,” Bain Brief, 11 February 2015.
[4] Dick Weisinger, “Digital Twins: Virtual Simulations that Help Answer ‘What If’ Scenarios,” Formtek Blog, 4 January 2023.
[5] Stephen DeAngelis, “Digital Twins and Phygital Fitness,” Enterra Insights, 25 January 2023.
[6] Weisinger, op cit.