The worldwide Supply chain challenges that plagued companies in multiple industries throughout 2021 are continuing this year. One potentially effective solution for addressing supply and demand issues is to leverage data analytics.
Professional services and consulting firm KPMG in a recent report notes that several major disruptions are currently affecting supply chains. These include the ongoing global logistics disruptions stemming from the COVID-19 pandemic that continue to impact businesses and consumers — as the flow of goods into key markets is restricted by shutdowns of major global ports and airports.
The major logistics disruptions create a ripple effect across global supply chains that ultimately cause goods to pile up in storage, the firm says. Assuming that these disruptions decrease and access to sea and airfreight reverts back to pre-pandemic levels, it will likely take some time before things return to normal, it says.
Other factors contributing to supply chain problems include production delays, over reliance on a limited number of third parties, and labor market shortages. The report also points out that many companies are investing in technologies to automate key nodes within the supply chain.
This year will see an accelerated level of investment, KPMG says, as businesses look to enhance critical supply chain planning capabilities by adopting more advanced “digital enablers” such as cognitive planning and AI-driven predictive analytics.
“The onset of new technology has fundamentally changed the way supply chains operate globally,” the report says. “The consumers are becoming more demanding, and this is leading the supply chains to change and evolve at a faster rate. Modern operations are focused on technology and innovations, and as a result, supply chains are becoming more complex.”
How can organizations best use data analytics to enhance their supply chain management (SCM) efforts? Here are some best practices, according to experts.
Most companies are awash in large volumes of data, often stored in diverse systems and databases, says John Abel, CIO at networking technology company Extreme Networks. Supply chains have the added complexity of additional data sources being generated from extended partners such as outsourcing, logistics, and distribution operations, he adds.
“As a result, many struggle to use this data to generate meaningful insights beyond top-level metrics and descriptive statistics,” Abel says. “Data analytics tools can deliver deeper, actionable insights as well as improve accuracy of those insights.”
The foundations for a successful supply chain data analytics strategy include ensuring that internal and external data are brought together in a structured format; focusing the outcome of data projects on what actions need to be taken to move the performance needle; and making sure the results are simple to understand, Abel says.
“The last point is one of the most important,” Abel says. “It is often tempting to focus on the model used rather than the output,” as many technology leaders look to include AI in their processes. “But the more important goal is to focus on generating insights that are clear, explainable, and easy to digest by the business users, not just analytics teams.”
Any report or dashboard being shared with cross-functional teams must be able to tell a clear story that is easily understood. “Otherwise, the benefits of data analytics could be overshadowed by the need for lengthy meetings to explain why they are valuable,” Abel says.
This also works the other way around. “While most data analytics experts don’t have a deep functional knowledge of the business processes and systems that produced that data, they often have a broad knowledge of the upstream and downstream processes and systems,” Abel says. “Successful supply chain analytics projects start from a ‘what does the data tell us’ perspective, but then layer in an in-depth understanding of business processes.
