Over the past several decades, the world has become increasingly digital. Digitalization not only increases the amount of data being generated it increases the speed at which business is done. As Deloitte analysts note, “The new normal in consumer interactions is speed with agility.” In fact, speed and agility apply to all aspects of business operations, not just interactions with consumers. Supply chain operations are no exception. Analysts at Sprint Logistics note, “Technological advancements like the Internet of Things (IoT) and Artificial Intelligence (AI) are transforming the way we live and work, and traditional supply chains are not exempt from the digital revolution.” The explain, “Traditional supply chains, reliant on manual processes, simply cannot keep up with the pace of today’s market; the processes for balancing inventories and forecasting used ten years ago are quickly overwhelmed. Using digital technologies to capture, store, process and share huge amounts of data means increasing efficiency at every stage. Automating processes enables supply chain managers to reduce costs and increase supply chain throughout, whilst giving businesses the information they need to respond in real-time to rapidly changing market conditions.” They believe traditional supply chains need to transform into cognitive supply chains. Deloitte analysts agree. They write, “Accomplishing [faster transactions] requires organizations to create cognitive supply chains that augment human capabilities with advanced technology and analytics.”
Fundamentals of a cognitive supply chain
Every supply chain is unique; which means, every cognitive supply chain will also be unique. Nevertheless, Sprint Logistics analysts assert, “A cognitive supply chain is made up of four steps: predict, plan, control and share.” Mani Iyer, Assistant Vice President for Analytics & Research at Genpact, insists, “Unlike transactional supply chains, a cognitive supply chain will be a digitally led, yet process-centric — as opposed to being merely digitally enabled. This new model is already evolving, as digital technologies with embedded analytics converge to capture, store, process, and share data.” Merge those two thoughts and you begin to get a clearer picture of what a cognitive supply chain encompasses, namely: A cognitive supply chain leverages data and cognitive technologies with embedded advanced analytics across the full range of supply chain activity, including forecasting, planning, controlling, and sharing. Deloitte analysts note, “To seize new opportunities — and fend off competitive threats — supply chain leaders need to spot and act upon a host of diverse, long-range issues. This requires the ability to gather and analyze voluminous data across end-to-end processes. Humans can’t do it alone in a timely manner, if at all. But by incorporating new technologies like AI into their supply chain, they can, without breaking a sweat. Yet many companies underinvest in technologies like supply chain AI and machine learning.”
Advanced analytics are at the heart of a cognitive supply chain. Daniel Bachar (@d_bachar), a Product Marketing Director for Advanced Analytics for Logility, suggests several ways advanced analytics can help a company. They include: Cost reduction (savings in staff, in reduced warehouse space, in fuel, in retained customers, and in lower costs-to-serve); insights (timely analytics tied with a singular version of internal data truth and third-party data means Sales and Operations Planning (S&OP) is an inherently more efficient process as forecasts reflect reality); and risk analysis (knowing the areas where your supply chain is vulnerable). He concludes, “These are just some of the many areas that derive increased value from supply chain analytics. Everyone has a supply chain. Successful companies are using their supply chains as a competitive advantage.” As the Deloitte analysts noted, cognitive supply chains require AI systems, like the Enterra Cognitive Core™, which lies at the heart of the Enterra Supply Chain Intelligence System™.
Why companies need a cognitive supply chain
Journalist Nick Easen asserts, “As complex algorithms, machine learning, and artificial intelligence become mainstream, it seems inevitable that they’ll disrupt global supply chains.” As of yet, he notes, “Fully digitalized supply lines and inventory management systems that think for themselves are still thin on the ground.” The reason for this, Easen writes, is that, even with oceans of available data, getting the data right is difficult. He explains, “The age-old issue of siloed, unrationalized-data legacy systems, as well as companies that don’t talk to each other, is widespread.” Alex Saric (@alexsaric), a procurement expert at Ivalua, told Eason, “There is a huge appetite to develop cognitive supply chains, which is only set to increase as more success stories come to light. But for many, achieving a fully cognitive one is currently just a pipe dream because of poor quality data.” When dealing with clients, we often find getting the right data can be difficult. HERE Technologies analysts agree getting the data right is the first step towards a cognitive supply chain. They write, “Leveraging data introduces substantial challenges. One is data integration. Much of the data that will be useful to you resides in the servers of your supply-chain partners. There’s often a problem of data standardization as well. The good news is that a lot of the larger players are forcing some degree of standardization by demanding that the companies that they work with give them data in a form that allows it to be easily fed into their own systems.”
However, as Eason notes, “Get it right and the benefits are legion. Algorithms can tune supply lines based on human behavior. The outcome can be better, personalized customer service with lower inventories and a better utilization of factory hours.” Iyer writes, “The benefits of moving from a transactional to cognitive supply chain include faster time-to-market, improved market share, and increased growth and profitability. As metric outcomes, you can expect to see: 50% improvement in productivity; 70% reduction in excess or unproductive inventory; 8% year-on-year reduction in logistics costs; 40 percentage point improvement in forecast accuracy; [and] 15 percentage point improvement in customer service.”
Deloitte analysts agree companies implementing cognitive supply chains will see significant positive impacts. They write, “To help decide where to go in your digital and analytics journey, follow these steps:
- Think big. “Size up your opportunities. Talk to others in the organization — from logistics and distribution experts to sourcing and procurement staff — to find the biggest needs across your supply network.”
- Start small. “Prioritize your opportunities based on where you can gain value quickly. Be sure to consider the level of risk and ease of implementation — and think beyond immediate financial benefits.”
- Scale fast. “Once you’ve achieved some early successes in a pilot environment, design a governance framework and rollout strategy for broader implementation. Then socialize your plan across the company.”
At Enterra Solutions®, we call this a “crawl, walk, run” approach to implementation.
HERE Technology analysts conclude, “We need a cognitive approach when it comes to supply-chain optimization. If we get it right, there’s an opportunity to transform the business. If we embrace the revolutionary possibilities presented by developments in artificial intelligence and machine learning, we can anticipate problems and manage disruptions before they even occur.” Deloitte analysts add, “While digital and analytics can create value across the entire cognitive supply chain, a handful of applications has emerged as the most promising. These include inventory visibility optimization, real-time manufacturing asset intelligence, and control tower–enabled visibility.” As cognitive supply chain implementation continues to mature, companies will find cognitive systems, like the Enterra Global Insights and Optimization System™, extremely valuable. This system brings together in one place four models (crisis, demand, supply, and econometric) and applies them in the areas of product strategies, regional strategies, and retailer strategies. This holistic approach is one of the primary advantages of transforming traditional supply chains into cognitive supply chains.
 Staff, “Cognitive supply chain and a new way of working,” Deloitte.
 Staff, “What is a Cognitive Supply Chain?” Sprint Logistics Blog, 27 March 2020.
 Mani Iyer, “The cognitive supply chain: Why your firm needs one,” Genpact Blog, 10 May 2018.
 Daniel Bachar, “Where’s the Value in Supply Chain Analytics?” Logility Blog, 17 June 2020.
 Nick Easen, “Why you need a cognitive supply chain,” Raconteur, 12 March 2019.
 HERE Technologies, “Entering the Cognitive Era of Supply-Chain Optimization, Supply Chain Brain, 14 November 2019.