In a recent APQC survey of 133 organizations, most respondents (79 percent) have increased their investment in analytics over the last three years, and 77 percent have a formal analytics program or structure. This indicates that organizations see the value in analytics activities.

But with Big Data turning into Gargantuan Data, analytics in the supply chain is evolving rapidly. There are different types of analytics to reduce costs, hasten decision-making with visual tools like dashboards, and boost productivity. Yet supply-chain leaders won’t realize those goals without the right portfolio of analytics types.

Currently, most supply-chain functions use descriptive analytics (i.e., mean, median, mode, frequency distribution of discrete data points, and percentile rankings). Descriptive analytics combines business intelligence with existing data to provide a vision of what’s currently happening in an organization. Although useful as an indicator of current performance, descriptive analytics does not provide information on why performance is what it is and how to improve.

To remain competitive, organizations must also leverage predictive and prescriptive analytics. Predictive analytics uses historical data and various algorithms to predict outcomes of various what-if-scenarios to help predict future events and predict trends. Forecasts and statistical models are used in this form of analytics to judge and provide recommendations about what could occur. Prescriptive analytics uses optimization or embedded decision rules to find out what should happen in a certain situation. This form of analytics is the most advanced because it uses insights gleaned from predictive analysis to recommend business decisions or actions that are likely to produce a specific result given particular business variables, inputs, and objectives. Predictive and prescriptive analytics help you move to the next level of understanding relationships and drivers for key business process outcomes (i.e., strategies that decrease cycle time). Among supply-chain functions, these analytics are most applicable to planning/operations.

A recent APQC survey also found that almost one-third of organizations employ machine learning in predictive analytics models to allow for large amounts of structure ERP and SCM data to be processed. This reinforces the importance of analytics to organizations, because data is being used to train computers that are freed from direct reliance on employees once trained, which improves costs and efficiency

With a portfolio of analytics to solve strategic supply-chain challenges, organizations need to build the following four key analytics capabilities:

• The ability to interpret results

• The ability to visualize/communicate results

• Making quality data available

• The right technology/tools/infrastructure

With these capabilities, organizations can maintain momentum for analytics by communicating the big wins. Organizations will find it is crucial to also balance business acumen and the technical skills of people supporting the supply chain function. This means that supply chain domain experts, analytics experts, and data management experts will need to come together to ensure that analytics supports supply-chain operations.

The Outlook

In 2020 and beyond, organizations can expect to see more supply chain professionals with analytical skills supporting a data-driven culture. An increasing number of organizations will be conducting more predictive and prescriptive analytics to inform business decisions. Analytics be a component of the next wave of technological innovation in cognitive computing, machine learning, and artificial intelligence.