More and more articles are being written about the need for supply chain transformation and, as a result, supply chain management is becoming more central to business strategies. “Supply chain management was once a relative backwater,” writes Mikko Kärkkäinen, Group CEO at RELEX Solutions, “but in recent years it has moved decisively center stage. A well-managed supply chain operation is, increasingly, a critical factor, even the critical factor, in a business’s success. The transformation process is primarily about aligning the supply chain with your overarching business goals for maximum efficiency.” According to Jonathan Webb (@), Head of Strategy Research at Procurement Leaders, a survey of procurement executives conducted by his company found supply chain transformation is in full swing. “One of the most surprising findings of the research,” he writes, “was that 82% of procurement organizations were currently undergoing a transformation.”
Supply Chain Transformation is being Driven by Technology
Supply chain transformation is inextricably linked to the digital age and the need for industrial age organizations to transform into digital enterprises. Mark Morley (@), director of strategic product marketing at OpenText, explains that today’s “CIOs wrestle with new types of networks; new disruptive technologies such as wearable devices, 3D printers, advanced robotics, drone based services and the Internet of Things (IoT); and new types of structured and unstructured information coming off of these devices.” He adds:
“In terms of enabling the digital supply chain, both from a technology and resourcing perspective, there are two key trends emerging. First, companies are keen to establish a single ‘digital backbone’ across their business, a backbone that seamlessly integrates internal enterprise systems such as ERP with external trading partner communities. This digital backbone provides the foundation upon which to build a digital business. Second, CIOs and CDOs are becoming more focused on new, leading-edge technologies, such as those mentioned previously, and applying extensive resources to explore and make use of these technologies.”
One technology he didn’t mention (and one that provides the digital backbone to which he refers) is cognitive computing. Lora Cecere (@), founder and CEO of Supply Chain Insights, explains, “Cognitive computing mines insights based on the interrelationships of contextual data. … As a result we are no longer limited to traditional hierarchical thinking and the visualization is no longer limited to rows and columns.” How will these technologies change supply chain management? Thomas J. Kull, Sangho Chae, and Thomas Choi, from the Arizona State’s W.P. Carey School of Business, assert, “Predicting the future of supply chain management is a fool’s game.” Nevertheless, their studies have found, “‘Technology advancements in supply base’ and ‘increased emphasis on supply chain security’ are the two most salient forces that influence every key supply mission.”
Transformation and Cognitive Computing
Cognitive computing, which I define as a combination of Semantic Reasoning (i.e., machine learning, natural language processing, and ontologies) and Computational Intelligence (i.e., advanced mathematics), can integrate and analyze both structured and unstructured data. This capability provides the digital backbone upon which a digital enterprise can be built. Thanks to natural language processing, cognitive systems can interact with users in non-technical ways providing them with actionable insights. The heart of a cognitive platform, however, is advanced analytics capabilities. Abe Eshkenazi (@), the CEO of APICS, explains, “Big data and analytics already allow supply chain managers greater insight into their operations, which enables increased efficiencies and greater data-driven insights.” He adds:
“The number of data sources in the supply chain is expected to grow exponentially, which will offer managers even more opportunity for contextual intelligence and more knowledge sharing across and between organizations. For example, with additional data, planning and decision-making will improve across an organization, which can lead to decreased costs and time spent on operations. Data-driven insights have the power to decrease risk, improve transparency, identify trends, and initiate automatic responses where it wasn’t possible before. The supply chain of the future will build on these benefits to become even more streamlined and efficient than it is now. However, it’s imperative that organizations are open to learning new processes for harnessing and interpreting this data, otherwise they may face intense pressure from more agile competitors.”
Cognitive computing platforms can help make any organization more agile. Cecere notes there are at least ten use cases in which cognitive platforms have great value. They are:
- Data Mapping. The average company has 5 to 7 Enterprise Resource Planning Solutions (ERP). While the solutions are sourced from the same provider, each has a different data definition. It is difficult to map data from one to the other. The use of cognitive learning with a rules-based ontology enables the mapping of data with different context.
- Automation of Master Data Management and the Replacement of Data Standards. Today master data is hard-coded. The data definitions are manually determined and mapped. They are inflexible. As the business changes, the data becomes obsolete. With cognitive learning we redefine master data. It is adaptive.
- Redefinition of Rules. In the first generation of planning technologies the rules were single “ifs” to single “thens.” Rule sets — like ATP, VMI, Transportation Routing, Allocation and Assortment logic — are simple rule sets. Too simple to meet the business need. The problem is that they are not robust enough. An order is not an order. A customer is not a customer. An item is not an item. Each status needs mapping based on business rules. Cognitive learning will redefine rules in the supply chain. Today’s supply chain does not play by the rules; however, we try to constrain and limit the options through rule definition that limits the possibilities.
- Rethinking Demand Management. Today companies try to get very precise on imprecise data. Rows and columns define forecasting. Companies lose visibility on the patterns and demand flows. Cognitive learning solutions provide systems of insights that can combine profile pattern recognition along with learning on unstructured data. Examples include the number of google searches on an illness or symptoms, which is a predictor of the spread of an illness and subsequent prescription sales. Social sentiment on twitter and Facebook combined with point-of-sale data drives insights to understand regional sales in days. Today it takes weeks.
- Planning Master Data. I only know a handful of companies that have a planning master data layer feeding their supply chain planning engines. Most companies install supply chain planning solutions, stabilize the implementation, and then forget about them. The data parameters of lead times, cycle times, and rates quickly become outdated. In most cases this data is a variable not a constant. For example, moving rail through Chicago in the winter is not the same as the summer. Unloading a container in Long Beach varies by the season; yet, the planning systems have a fixed value.
- Rethinking Decision Support. Building Systems of Insights. Cognitive computing, layered on existing decision support tools — revenue management, trade promotion management, demand planning, production planning, transportation planning and supply network planning — drives deeper insights. The goal is to stabilize the current solutions and layer cognitive solutions on top of more traditional solutions like Logility, JDA, and SAP APO.
- Listening Posts. Today’s analytics drive answers on the questions we know, but we cannot track the important data that answers the questions which we do not know. Examples are many. Why are consumers raising concerns on quality? What do patterns tell us?
- Quality. Many production environments — coolers, dryers and distillation columns — are complex with many variables. Once the variables exceed 5 to 7 it is hard to model outcomes without collinearity. Cognitive learning enables new forms of insights to better control quality.
- Customer-Centric Supply Chains. Cognitive learning is ideal to map customer policies to fulfillment. The dynamic nature of inventory to order matching is difficult. This is an area of great opportunity.
- Network of Networks. The connection of data between trading partners is a use case that excites me. … In short, today’s business networks lack agility and are too costly. We believe that cognitive learning improves data mapping and gives insights to data relationships and inferences which are not obvious.
The Digital Age is maturing rapidly and digital enterprises (supported by digital supply chains) will thrive in the decades ahead. Businesses slow to transform could be consigned to history’s dustbin. Cognitive computing platforms are a good foundation on which to build the digital enterprise of the future.
 Mikko Kärkkäinen, “Supply chain transformation: having the competitive edge,” Supply Chain Digital, 15 November 2016.
 Jonathan Webb, “2017: The Year For Supply Chain Transformation,” Forbes, 30 December 2016.
 Mark Morley, “Disruptive Technologies Are Driving New Supply Chain Transformation Initiatives,” Spend Matters, 8 February 2017.
 Lora Cecere, “Different Strokes for Different Folks (to Yield Better Results?)?” Supply Chain Shaman, 10 February 2017.
 Thomas J. Kull, Sangho Chae, and Thomas Choi, “The Future of Supply Chain Management,” Supply Chain 24/7, 27 January 2017.
 Abe Eshkenazi, “Envisioning the Future of Supply Chain,” EBN, 15 December 2016.
 Lora Cecere, “Not the Jetsons: Ten Use Cases for Cognitive Learning in Supply Chain,” Supply Chain Shaman, 7 December 2016.