By Karen Lynch
In the MHI survey, companies large and small cited the most important applications of predictive analytics as demand forecasting; supply chain and warehouse planning; inventory management; and logistics, shipping, and transportation. Nearly nine out of 10 supply chain managers plan to use predictive analytics within five years. That includes 30 percent who are already using it and 34 percent who expect to do so within two years.
Predictive analytics recognizes patterns and anomalies in the stores of data culled from both the supply chain and external sources to help supply chain managers forecast, plan, and take action. More advanced versions involve machine learning, which trains computers to make the predictions based on known outcomes of past data. Even more sophisticated forms of artificial intelligence (AI) can prescribe next steps based on predictions.
In global supply chain management, “data analytics is about using data to drive useful business intelligence, answering the questions, ‘What just happened?’, ‘Why did it happen?’, and ‘What are we going to do next?,” according to a separate report from the World Trade Organization (WTO), titled Technological Innovation, Supply Chain Trade, and Workers in a Globalized World. “The on-going digital transformation provides significant scope to boost global growth, and indeed, trade, especially for SMEs,” the WTO report noted.2
Even at a basic supply chain management level, SMEs using e-commerce marketplaces and other digital platforms—whether business-to-consumer (B2C) or business-to-business (B2B)—gain greater access to data for assessing demand, understanding markets, and knowing what sells and what doesn’t at any given moment. Additionally, “the development of digital platforms has spurred growth and further efficiency of logistics services and is leading to increased access to such services for SMEs,” according to a report from the International Trade Centre, titled SME Competitiveness Outlook 2018.3
The MHI report noted that, for more complex global supply chain management, companies have been utilizing inventory- and network-optimization tools, sensors, robotics, driverless vehicles, and other technologies that are part of the Internet of Things (IoT). Data exchange among suppliers and buyers is starting to be consolidated with distributed ledger (blockchain) technology. Increasingly, predictive analytics applications run statistical models on data flowing to and from all these sources through cloud networks.
For companies large and small, the trend toward self-serve analytics holds the promise of accelerating predictive analytics. “Rapid advancements … are making it easier and more cost-effective than ever before for non-specialists to perform effective analysis and better inform their decision-making,” according to the Gartner research firm.4 Quite simply, this means that supply chain managers lacking IT or coding expertise can more readily set up and run analytics models and dashboards—without having to hire data analysts or data scientists.
Nevertheless, most small businesses in the U.S. aren’t taking full advantage of data analytics and other digital tools, according to the Deloitte management consultancy. In fact, four out of 10 think such tools are not relevant—or wouldn’t be effective—for their business. Interestingly, Deloitte has found that digitally advanced small businesses earn two times as much revenue per employee, experience more significant growth, and are more likely to export than those that shun digital.5
Further, the MHI report listed technical, cultural, and business challenges for companies of all sizes. In the field of predictive analytics, for instance, “incompatible systems require new interfaces and time-consuming data consolidation/cleansing, which can slow the implementation to a crawl.” Self-service analytics tools that address such tasks are also coming of age.
In global supply chain management, “decision-makers are equally worried about missing the mark and missing the boat,” the MHI report noted. “Ultimately, every company is unique, and its path will be strongly influenced by a variety of factors, including budget, customer demands, competitive insights, and available talent.”
The overall effect of digitization on global supply chains remains uncertain, the WTO report noted. Developments such as e-commerce marketplaces and predictive analytics might lengthen supply chains by reducing coordination and matching costs between suppliers and buyers. Other technologies, such as robotics, could shorten supply chains by reducing labor cost and encouraging the re-shoring of production.
What is clear, though, according to the MHI report, is that SMEs are under-represented in global supply chains, but “the digital economy provides new opportunities for SMEs to play a more active role.”
Digital innovations hold the promise of easing SMEs’ access to international trade, with predictive analytics emerging as a top priority in global supply chain management. Digital platforms and self-serve analytics tools are key enablers for smaller businesses.
Karen Lynch is a journalist who has covered global business, technology and policy in New York, Paris and Washington, DC, for more than 30 years. Karen also is a principal at Content Marketing Partners.
1. “Technologies Will Increase Dramatically in 2019,” MHI; https://www.businesswire.com/news/home/20190410005005/en/New-MHI-Deloitte-Report-Finds-Investment-Supply
2. “Report Sheds Light on Impact of Digital Technologies on Global Value Chains,” World Trade Organization; https://www.wto.org/english/news_e/news19_e/publ_15apr19_e.htm
3. SME Competitiveness Outlook 2018, International Trade Centre; http://www.intracen.org/uploadedFiles/intracenorg/Content/Publications/SMECO2018.pdf
4. “Gartner Says Self-Service Analytics and BI Users Will Produce More Analysis Than Data Scientists Will by 2019,” Gartner; https://www.gartner.com/en/newsroom/press-releases/2018-01-25-gartner-says-self-service-analytics-and-bi-users-will-produce-more-analysis-than-data-scientists-will-by-2019
5. Connecting Small Businesses in the U.S., Deloitte; https://www2.deloitte.com/content/dam/Deloitte/us/Documents/technology-media-telecommunications/us-tmt-connected-small-businesses-Dec2017-old.pdf
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