Evolving Supply-Chain Analytics to Meet Business Needs

Analyst insight: With big data getting even bigger, organizations large and small are trying to find the best way to distill it into meaningful insights that help leaders make strategic business decisions. Best-in-class organizations are creating formal, centralized and hybrid supply-chain analytics programs or structures. While they provide governance and oversight, hybrid programs help organizations combine the holistic perspective and control of centralized guidance with flexibility and insights into the business needs provided by decentralized implementation.

In APQC’s 2019 Analytics in the Supply Chain study of over 200 organizations, 79% had seen an increase in their investments in supply-chain analytics over the past three years. It’s no surprise that with the budget increase, organizations are also seeing a rise in the number of formal analytics programs formed. The majority of respondents to the study (95%) had a formal analytics program, versus just 77% in 2016. This indicates that organizations see analytics capabilities engrained in their supply chain as a must-have, rather than a nice-to-have.

Regarding the structure of supply-chain analytics programs, 40% of organizations in 2019 reported that they had a hybrid structure, compared with only 29% in 2016. Another 40% had centralized supply-chain analytics in 2019, compared with 32% in 2016. This difference over the past three years is primarily the result of a greater operational and execution-focus in supply chains. Decentralized structures have remained steady at 15% over the past three years.

Organizations with centralized analytics structures benefit from having:

• One source of the truth — integrated data sources, methodologies, policies templates, and training;
• Holistic perspective — the ability to tie analytics projects together into a portfolio and look for synergies or overlaps between projects, and
• Top-down, bottom-up monitoring — a centralized portfolio of projects providing the ability to look at analytics across the organization, measure project impact against organizational goals and programs, and drill into specific projects for root-cause assessments.

Meanwhile, organizations with hybrid analytics structures receive centralized structure benefits, as well as embedded analysts in the business, which allows the analysts to develop business domain knowledge, build trust with decision makers, and look for solutions specific to the business’s needs.

Many organizations have specific analytics needs that spur their shift to data-driven decision making, such as improving their understanding of customer needs or supporting staffing and policy decisions. Hence, they start their analytics journey with function-specific analytics teams or programs. Others use a hybrid model because they either have enterprise-wide data initiatives, mandates by executive management, or can leverage pre-existing analytics and data-driven capabilities. For example, Sam’s Club, a division of Walmart Inc., uses a specialized in-house supply-chain analytics team to centralize key skills, so that the supply-chain function can pre-emptively deliver the right data and insights to drive decisions, and ultimately place the function in a key advisory role to Sam’s Club leaders.

Cementos Argos S.A. a cement manufacturer, wanted to gain a competitive advantage in the cement market and deepen its existing culture of data-driven decision making. To do this, the company set up a dedicated center for business analytics composed of data scientists, model analysts, and business analysts. The center has produced tangible benefits, including standardized and automated processes, reduced cycle times, and lower costs. Because it sits within the finance function and reports directly to the CFO, the data and analytics team is well-positioned to see challenges and opportunities for improvement across the organization, and approach leadership with them.

Outlook

The need to balance centralized governance with decentralized implementation remains more important than ever before. In 2020, organizations can expect to see an increase in the formation of hybrid supply-chain analytics programs that are aligned with business needs. For analytics programs to keep moving in the right direction, it will require organizations to have a well-defined process with governance that includes knowledge sharing and collaboration between centralized and decentralized groups.

Andrea Stroud is senior statistician with APQC.