The Digital Supply Chain needs to be a Smart Supply Chain

Stephen DeAngelis

October 17, 2018

Most analysts agree supply chains need to go digital. More recently analysts are also talking about the importance of supply chains becoming smart. By that they mean supply chains need to take advantage of artificial intelligence (AI) capabilities to get the most out of collected data. Deloitte analysts John Ferraioli (@Ferraioli) and Rick Burke explain, “The digital supply network (DSN) can be a powerful tool for companies, allowing them to harness data and information to make more effective decisions in the physical world via their assets, machines, and people. The benefits can be myriad: deeper visibility into the supply network; greater connectivity with suppliers, partners, and customers; smarter factories; and the ability to act, respond, and adapt intelligently to shifts in the ecosystem. Whatever the result, data — and the ability to analyze and derive insights from it — lies at the heart of the DSN.”[1]


Digital supply chains start with data


Ferraioli and Burke bluntly state, “By definition, data is the lifeblood of the DSN and the technologies that make it possible.” They go on to note that obtaining data is not generally a problem. “Most companies,” they write, “are already awash in the data they need to create a DSN, whether through networked systems on the factory floor or back-office databases.” They explain data can take many forms, including:


  • Master data: “Business-critical data that is consumed by applications to enable business processes. It largely relates to material master, supplier master, customer identities, product material specifications, etc.”
  • Transactional data: “Post-business-process information such as purchasing inventory records or sales volumes by region.”
  • Sensor data: “Unstructured data that characterizes the conditions of the enterprise’s physical assets, from voltage to vibration.”
  • Other unstructured data: “Data existing within the organization such as spreadsheets, emails, engineering schematics, drawings, and beyond.”


“What may be a challenge,” Ferraioli and Burke write, “is that while companies may have all the data they need to become a truly digital enterprise, some may not be able to access such data particularly well — much less understand how to turn the data into actionable insights.” That’s where artificial intelligence enters the picture. John Mabe explains, “The fact that supply chains and logistics companies already have a vast amount of data and processes that could be turned into actionable insights rather quickly, just means that there is potential for AI to take supply chains on a journey from very good to perfect.”[2] Although Mabe may be a bit optimistic about achieving perfection, AI can certainly help digital supply chains improve from very good to great (or as Mabe admits, “almost perfect”).


Cognitive technologies make digital supply chains smart


According to Mabe, “Supply chains are going to change dramatically over the next few decades, and that change is going to be accelerated by the convergence of new technology and evolving consumer behavior which is going to require unprecedented levels of agility and flexibility in supply chains.” As noted above, Mabe believes AI will play a significant role in helping make supply chains better. The term “artificial intelligence” covers a lot of technologies. The subset of AI known as cognitive computing is what companies require to make their digital supply chains smart. Keep that in mind during this discussion. Cognitive technologies aim to improve enterprises not take over the world. Eric Bussy (@Eric_Bussy), Worldwide Corporate Marketing and Product Management Director at Esker, explains, “AI is rapidly gaining traction in the commercial space, from customer-facing call centers to back-office applications. It’s also reaching into the complex world of supply chain and logistics helping to: automate and streamline various functions, improve shipping times and order fulfillment, reduce costs and bolster profits. When machine learning is applied to inventory and supply and demand, forecasting becomes more accurate, warehouse management is improved by reducing under and overstocking and supplier management is made easier.”[3]


B.S. Teh, a global sales and field marketer at Seagate, explains other ways cognitive technologies are going to improve digital supply chains. He writes, “AI can also help in streamlining functions and optimizing routes to reduce shipping costs. Geocoding and location-based services make this possible by calculating the shortest route for delivery personnel to process timely package delivery. Through geocoding and location-based services, companies can gain real-time traffic insights, day-to-day freight trends, thus helping them execute coherent internal operations that will lead to optimum vehicle allocation for delivery.”[4] My company, Enterra Solutions®, is heavily involved in the consumer packaged goods (CPG) sector. AI is demonstrating how that sector can also be improved using AI. Robert J. Bowman explains, “The consumer-goods sector is growing more complex and harder to predict than ever before. Challenges include the need to enable ‘mass customization’ of product, geared toward fickle and demanding customer tastes; do a better job of synching supply with demand, to avoid the twin evils of stockouts and overstocks; balance inventories in a manner that meets the needs of both traditional retailing and online sales; and fulfill orders at lightning speed.”[5] He notes a new study concludes, “A.I. and robotics automation ‘could become the autopilot behind a supply chain, which handles planning and fulfillment, designs products, monitors inventory levels, optimizes sourcing, synchronizes production and maintains machinery.'” AI has a bright future in the supply chain arena and is already making an impact. Bussy reports, “According to a survey by BI Intelligence, supply chain and operations is the third most active area for the use of AI, with 42 percent of respondents seeing revenue gains from investments in the technology. Pointing to a McKinsey study, BI Intelligence noted that businesses in multiple industries that adopted a proactive AI strategy for transportation and logistics saw profit margins of more than 5 percent, while those without such a strategy lost money.”




Mabe concludes, “It seems we are in this fascinating moment of time where, regardless of industry, the speed of business is far outpacing the level of insight into the supply chain. We may be collecting troves of data, but the ability to convert that data into real-time actionable insights is still limited. And, due to the evolution of consumer behavior, the inherent unpredictable nature of supply chains, and the availability of data in the industry just means that AI techniques hold a lot of promise.” Many companies are still trying to transform their supply chains into digital supply chains. They would do well to ensure their efforts result in smart, digital supply chains.


[1] John Ferraioli and Rick Burke, “Drowning in data, but starving for insights,” Deloitte Insights, 11 April 2018.
[2] John Mabe, “Ways to think about the Supply Chain of the Future: Artificial Intelligence,” Tech|Gistics, 5 September 2018.
[3] Eric Bussy, “AI Making an Impact in Enterprise Supply Chains,” Supply & Demand Chain Executive, 10 September 2018.
[4] B.S. Teh, “Transforming Supply Chain And Logistics With AI,” Inc42, 16 September 2018.
[5] Robert J. Bowman, “A.I. and Robotics Are Coming to Consumer Goods Supply Chains. But Where’s the Payoff?SupplyChainBrain, 27 August 2018.