Injecting more intelligence into supply chains

There is a lot that analytics and AI-based applications can do to clear up supply chains, which have been rocked by pandemics and after-effects, never mind run-of-the-mill shipping challenges.
Written by Joe McKendrick, Contributing Writer on

In a time when supply chains have been choking, investment dollars have been flowing to intelligent, AI or analytics-driven solutions. For example, in recent news, The Wall Street Journal's Marc Vartabedian reports that Project44, which develops supply-chain analytics software for shipping and logistics companies, raised $420 million in a recent funding round, bringing the value of the company to $2.2 billion. "Venture investors committed a record $24 billion to supply-chain tech companies based in North America and Europe through the third quarter of last year, a nearly 60% jump from all of 2020, according to analytics firm PitchBook Data Inc." 

There is a lot that analytics and AI-based applications can do to clear up supply chains, which have been rocked by pandemics and after-effects, never mind run-of-the-mill shipping challenges. A recent study of 788 business leaders by Unsupervised is finding business intelligence approaches are proving to be instrumental in helping many organizations grapple with supply chain woes, but 40% still are not on board yet. The other 60% are reported to be using business intelligence -- which the survey's authors define as the practice of combining data mining and visualization, analytics, and data infrastructure -- to navigate current supply chain issues, and 29% implemented business intelligence into their business specifically for supply chain efficiency.  

See also: 91% of IT leaders affected by supply chain disruption: Survey.

"In the context of the challenges facing supply chains, it becomes clear that the old ways of working will not suffice and that even a best-in-class performance today is unlikely to be good enough in the future," according to IDC analyst Simon Ellis. (His report is available here via IBM.) The movement. Ellis writes, is toward "thinking" supply chains, "one that is intimately connected to disparate internal and external data sources such as social sentiment and IoT, enabled with comprehensive and fast AI-driven analytics, openly collaborative through cloud-based commerce networks, conscious of cyberthreats, and cognitively interwoven."

A digitally enabled thinking supply chain "that acts on all available structured and unstructured data to prioritize actions and deliver superior results," Ellis explains. "Being digitally enabled means connecting and automating internally across functional areas or with end-to-end processes such as order to cash and with suppliers, customers, and consumers. There will be a network effect where value grows exponentially with the automation of transactions, documents, and key partner enablement."

A challenge that has emerged on top of all this is the growing shortage of talent needed to manage supply chain processes. "Supply chain organizations have pursued cost reduction and traditional lean practices to the point that there are fewer people in the organization than at any time in the past," Ellis warns. "As data analytics capabilities invariably grow in the supply chain, there likely will not be enough eyeballs available to act upon the resulting insights. Thus, the role of AI and machine learning becomes critical."

Building intelligence into supply chains requires working closely with the business and the data that is being shared across these networks. 

Whitney Myers and Joel Stellner, both with Zuar, outline the key attributes analytics should bring to the business:  

  • "To see and understand demand trends and open customer orders,
  • "To see and understand current inventory and open order allocations against replenishment plans from production teams and/or vendors, and
  • "a combination of the above to create world-class forecasting, alerting, and supply chain management tools."

To start in this journey, Myers and Stellner state, "you will need a data strategy that includes an automated data value chain, and a data staging platform that includes a pipeline that moves data from your tool into the database on schedule." In addition, they point out, IT teams need to introduce a business intelligence platform "that connects to databases and refreshes reports automatically, builds calculations and interactive dashboards, and shares dashboards with your team, along with the ability to set up personal alerts and subscriptions."

Data -- and the ability to apply it to rapid analysis -- is the core of the intelligent supply chain that is required for today's and tomorrow's economy. "Integration with all data sources is critical, as is automation of all documents across both internal functions and process and supply chain partners," says IDC's Ellis. "A thinking supply chain cannot learn from data it does not have. Connected means being able to access unstructured data from social media, IoT (including structured, semi-structured, and unstructured data), and structured data from traditional data sets available via traditional ERP and B2B integration tools."

(Disclosure: I have performed work with IDC, mentioned in this article, over the past 12 months.)

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