Numerous sources have declared data to be the most valuable resource in the world and the supply chain sector is trying to take full advantage of data. According to the International Data Corporation (IDC), “Digital transformation spending is to reach almost US$2trn by 2022, growing at a compound annual growth rate (CAGR) of 16.7%. … [These expenditures] will cover Internet of Things (IoT), artificial intelligence (AI), cloud, 3D printing, 5G, automation, and edge computing.”[1] Greg Siefkin (@Sief), enterprise sales director at Liaison Technologies, writes, “Forward-thinking supply chain professionals are looking to advanced technologies to streamline processes, improve accuracy, accelerate delivery, and reduce costs.”[2] The common thread connecting these technologies is data. Siefkin notes, “Complex supply chains generate more data, which companies can use to drive greater efficiency or engage in innovation that disrupts an entire industry — think Amazon. The prospect of using data to operate more efficiently and/or innovate is behind the impetus toward digital transformation that leaders across virtually every industry sector now pursue. At its core, digital transformation is about using technology and data to change the way business operates rather than just improving it.”

Leveraging big data in the supply chain

Siefkin writes, “More data is coming in than ever before. It arrives from an array of sources, and it is presented in a variety of formats. The potential value of that data is huge for logistics and supply chain operations, but it’s also enormously valuable for other business units, including marketing, sales, production, etc.” The editorial staff at Supply Chain Brain, adds, “Businesses have embraced the era of big data with open arms. The more information about market supply and demand that’s available to them, the better they can plan their supply chains to meet changing customer needs. Or so goes the theory. The problem is that many companies are in serious danger of becoming inundated with data. They simply can’t make sense of it all, can’t separate valuable information from noise.”[3] In other words, unlike gold, you can have too much data. Yasaman Kazemi, Industry Strategy Lead for the Supply Chain Department at Esri, explains, “Data, as opposed to capital, is useless without the tools that allow organizations to order, understand, and gain deeper insights from it. The big data revolution has made it necessary for business leaders to invest in technologies that enable big data analytics.”[4]

Managing data overload requires companies to identify the reasons they need data (i.e., what pain points are they trying to address) and then identify data sources that can help them address the challenge. Data is only valuable if it can be used. Steve Banker (@steve_scm), Vice President of Supply Chain Services at ARC Advisory Group, calls this “filling in information black holes.”[5] He explains, “‘Digital’ is a term that means different things to different people. To some it just means automation, particularly when the automation is based on newer or emerging technologies. In other cases, it means getting digital data, to replace manually entered data, or even worse — no data, in the places in a supply chain where these information black holes exist. And in many companies supply chains, it is the end-to-end portion of the supply chain, knowing what upstream and downstream partners are doing and how well they are doing it, where there are the most black holes.” Once the right data has been identified and collected, technologies enabling big data analytics then come into play.

Cognitive technologies and advanced analytics

Kazemi asserts, “Only decision makers with the best and most informed understanding of their data can set the standard for their business’s success. Big data analytics helps organizations reduce costs, make faster, better decisions, and create new products or services to meet customers’ changing needs. In fact, the future of supply chain digitization will be driven by data and analytics.” Most of the advanced analytics companies will use in the years ahead will be provided by cognitive computing platforms. George Bailey, managing director of the Digital Supply Chain Institute, asserts, “Artificial Intelligence and Machine Learning (AI/ML) are essential to capitalizing on the flood of data instead of drowning in it.”[6] He suggests ten ways cognitive technologies will help transform corporate supply chains. They are:

1. Identifying the right data. Bailey asks, “What’s the optimum mix of collecting, cleaning and storing data versus analysis and execution? Companies should spend 80 percent of their effort on analyzing data and making decisions and 20 percent on collecting and cleaning the data. Unfortunately, too many companies are mired in the opposite equation.”

2. Improving decision-making. “Forecast accuracy is important,” Bailey writes, “but Digital Supply Chains must do more; they must increase and manage demand. … Invest in the technology to capture new data, develop new algorithms and use it to make decisions in real time.”

3. Gaining insights. Bailey explains, “By uncovering hidden patterns and unlocking predictive power in information, supply chains will have the best chance of competitively satisfying customers.”

4. Improving visibility. According to Bailey, “Visibility into suppliers and customers is an essential way to reduce the risk of supply chain disruption. Developments in tracking and analytics will enable a true, transparent, end-to-end supply chain.”

5. Attracting new talent. Bailey writes, “New people with data scientist skills and deep analytical skills must be found and current people must be trained in data-based decision making.”

6. Changing business models. “Get the data and use AI/ML to discover new ways of adding value to customers,” Bailey writes. “Define critical business problems and determine what new data sources should be identified and gathered to help address them.”

7. Creating new products. According to Bailey, “Digital Supply Chain [DSC] knowledge should be added to the process of deciding what to make or do to drive growth. Winning companies will adopt a more integrated cross-functional view that includes their DSC to gather important customer insights and information.”

8. Improving dynamic pricing. Bailey explains, “Rapidly adjusting pricing as market conditions change will alter the way that the supply chain is managed and drive profitability. Predictive analytics will be used to continually set and adjust pricing for more and more products and services. AI will guide companies on customer buying behavior, mega trends and history.”

9. Establishing new vendor relationships. “Decide what DSC functions should be outsourced and which should be revenue producers using new tools.”

10. Implementing the best algorithms. When determining what algorithms to implement, Bailey recommends “members from Sales and Marketing, Supply Chain, Information Systems, HR, Enterprise Risk Management and Finance” be included on the decision-making team.

Concluding thoughts

Kazemi concludes, “The sheer quantity of data exceeds the capacity for analyzing that data in many organizations. As a result, many supply chains struggle to collect and make sense of the overwhelming amount of information across their processes, sources, and siloed systems. This leads to lower visibility into the processes and increased exposure to risks and disruption costs. Supply chains that adopt comprehensive advanced analytics, employ cognitive technologies, and enable visibility throughout their organizations will have a competitive advantage over those that do not.” As supply chain complexity increases, supply chain professionals can use all the help they can get to address it. Cognitive technologies are one of the best tools they will find in their kit.

Footnotes
[1] Sophie Chapman, “Supply chain operations to dominate digital transformation’s $2trn spending by 2022,” Supply Chain Digital, 15 November 2018.
[2] Greg Siefkin, “Using Data to Improve Supply Chain Operations,” Material Handling & Logistics, 15 December 2018.
[3] Staff, “Solving the Problem of ‘Too Much Data’,” Supply Chain Brain, 6 February 2018.
[4] Yasaman Kazemi, “AI, Big Data & Advanced Analytics In The Supply Chain,” Forbes, 29 January 2019.
[5] Steve Banker, “Digital Supply Chains Seek to Fill Information Black Holes,” Forbes, 1 August 2019.
[6] George Bailey, “Ten Ways AI-Powered Algorithms Will Transform Your Supply Chain,” Supply Chain Management Review, 16 April 2018.