To gain an accurate understanding of the state of analytics in your supply chain, 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).

Gartner recommends you communicate supply chain analytics performance via the following success metrics.

• Financial metrics: For example, revenue growth and cost reduction.
• Supply chain metrics: For example, demand forecast accuracy and customer order cycle time.
• Process metrics: For example, the size of the team for preparing for the sales and operations planning meeting, or the elapsed time for generating performance metrics reports.
• Team sentiment: This is a more qualitative metric that focuses on users' perception of the value of supply chain analytics in supporting better decision making, or freeing their time for higher value-added tasks. This metric can be used to monitor adoption and anticipate potential roadblocks on future supply chain analytics projects.

There are four primary organizational structures for a supply chain analytics team:

• Centralized: The supply chain analytics team is a centralized supply chain function.
• Decentralized: Supply chain analytics competencies reside in individual business units or functions.
• Supply Chain Analytics Needs Are Supported by an Enterprise Analytics Group: A general enterprise analytics team handles supply chain analytics needs.
• Supply Chain Analytics Needs Are Supported by External Service Providers: An external service provider handles supply chain analytics needs.

While some companies choose one of these, others combine them to create a hybrid organizational structure, aiming to capture the best of each and avoid individual shortcomings.

To choose the right organizational structure for your supply chain analytics team, Gartner recommends:

• Understanding the current and future business goals that drive analytics adoption in the supply chain.
• Building a roadmap for supply chain analytics, clarifying the current requirements and the vision for future capabilities.
• Assessing the current and future investments available to build a supply chain analytics competency center.
• Understanding the current analytics skill sets in the supply chain and across the enterprise.
• Understanding the available analytics technologies in the supply chain and across the enterprise.
• Investigating the supply chain organization’s track record in successfully embracing various organizational structures.
• Determining whether the supply chain group can successfully manage outsourcing relationships.
• Continually evaluating the fit of the organizational structure and adjusting based on current business needs and conditions.