Supply chain analytics refers to gaining insight and extracting value from the large amounts of data associated with the procurement, processing, and distribution of goods. Supply chain analytics are important for companies to fully digitalize, build an autonomous supply chain and enable real-time decision making. Through connected data sources companies can quickly react to current and anticipated future disruptions.

There are various types of supply chain analytics techniques that support individual supply chain functions or, for more mature organizations, end-to-end supply chain use cases. These are:

• Descriptive analytics answer the question of what is happening or has happened in the supply chain (through mining, pattern analysis, visualization).

• Diagnostic analytics clarify the “why” behind an event or an observation (through query, root-cause, what-if analysis).

• Predictive analytics assist with identifying future scenarios (through causal forecasting, regression analysis, simulation).

• Prescriptive analytics offer actionable recommendations or execute actions (through rule-based, heuristics, optimization).

Supply chain analytics leaders have consistently pointed to talent and culture as top roadblocks to successful deployments. Specifically, they cite issues like the availability of analytics skills, cultural readiness and ability to take advantage of analytics insights to incorporate into decision making.

To assess the performance of your supply chain analytics strategy, Gartner recommends:

• Investigating what role supply chain analytics plays in business and supply chain strategies, and what key initiatives supply chain analytics can support (e.g., greater customer collaboration, more accurate demand forecasts, lower inventory costs).
• Examining data governance and gauging the quality and availability of the data.
• Creating an inventory of current supply chain analytics solutions and investigating technologies that could support supply chain analytics projects.
• Auditing supply chain analytics skills and talking with both core and extended teams to uncover the availability of required supply chain and technical resources.
• Performing a high-level, current-state assessment in order to gain and share an understanding and overall maturity level of supply chain analytics.
• Defining the obstacles that exist in the supply chain today in order to prioritize the challenges that analytics must address (e.g., data availability, skill set shortage, lack of funding for new technology, cultural resistance).

Financial improvements due to better decision making are the most instrumental metrics in demonstrating the success of analytics initiatives. These are the improvements discussed at board meetings, and they generate the most immediate benefits to stakeholders. Financial improvements can span growth in market share, growth in revenue, reduction in working capital and improvement in the return on assets (ROA). But it is sometimes challenging to point to hard financial benefits resulting from analytics initiatives. In these situations, analytics leaders should emphasize the fact that, at a minimum, analytics were a contributing factor to top- and bottom-line financial improvements.