This site depends on JavaScript to run. Please enable it or upgrade to a modern browser that supports it.

ASCM Insights

Improve SRM with Data Analytics


Data analytics plays an instrumental role in advancing purchasing and supplier relationship management (SRM). When all relevant information is aggregated and linked, purchasing professionals have more insight that can, in turn, help them maximize speed, visibility and agility; gain a competitive advantage; and improve supplier relationships.

A 2020 survey commissioned by big data company TealBook revealed the unnervingly high number of issues organizations are grappling with as a result of poor supplier data. Most surprisingly, 93% of procurement professionals have experienced negative effects because of misinformation about their suppliers. About half of the respondents said they experience these effects on a regular basis. The lack of information results in challenges for both the procuring company and the supplier, including lost time, project delays and terminated supplier relationships.

Data analytics can help organizations use fact-based analysis to determine when there are real business or supplier problems and when new suppliers are needed. From there, big data about supplier performance — including regarding cost, turnaround time, quality, agility, sustainability and other factors — can help the purchasing manager address supplier problems. Alternatively, this information can show an organization how much it stands to gain or lose by switching suppliers or sourcing from a different region of the world. 

Procurement organizations that apply data analytics also are better situated to collaborate and synchronize with suppliers, “enabling greater agility both within these organizations and across the extended supply networks,” Deloitte reports. 

Predict your supplier’s punctuality

The secret to this agility is predictive analytics. When organizations have their supplier data efficiently gathered and analyzed in big data tools, they can become proactive rather than reactive regarding a variety of procurement activities and thus minimize issues with suppliers.

Supplier lead time plays a critical role in the timing and sizing of purchase order decisions. Historically, supplier lead times are entered into an enterprise purchasing system upon supplier agreement and are rarely, if ever, updated. However, operations change, and supply chains become more efficient and also more complicated. For a variety of reasons, lead times can change. Many purchasing professionals have recognized the importance of supplier lead times and are looking to accurately predict lead times and to develop strategies for coping with problems caused by lead time variations.

A big data module can predict the lead time variation percentage of a supplier-manufactured part compared with the agreed-upon lead time. This type of module considers:

  • purchasing information from the enterprise system
  • goods received information from the enterprise system
  • daily supplier data — including data about which and how many items were late — from other business systems
  • order and delivery dates from completed purchase order confirmations.

Blending this wide range of information and sources helps build an accurate module. Once the module is trained, it can

  • help predict whether parts will be shipped on time or not
  • specify lead time data at a part level, including for work in process
  • recommend appropriate inventory levels for both buyers and suppliers
  • share this lead time data with the enterprise system to better manage the purchase order life cycle.

Furthermore, big data modules can predict on-time and late deliveries. By aggregating and analyzing information about historical inbound shipments from the enterprise resources planning system — as well as manufacturing data, purchase order confirmations and any other trend information from the supplier portal — the big data module can foresee a supplier’s on-time parts delivery problems in advance. This gives purchasing managers enough warning to activate contingency plans before operations are interrupted — which can be critical for organizations that use just-in-time strategies. In addition, this information can help organizations eliminate the hidden factory costs of late parts and enable procurement professionals to spend more time focusing on value-added and growth-inducing activities.

When procurement managers leverage data analytics effectively, they are better equipped to foresee supplier issues and help their organizations adjust operational strategies to keep the business running smoothly. By avoiding high-stress stock situations, purchasing managers can then work with their suppliers in a calm and collected manner to adjust the late delivery issues, rather than needing to put pressure on the supplier during an already tense situation. This risk management approach also fosters a better and more understanding supplier-customer relationship that opens the door to open communication and exceptional visibility.

Learn about the ASCM Supply Chain Procurement Certificate, a foundational education program designed to help both entry-level and experienced supply chain professionals expand their procurement knowledge and skills. 

About the Author

Gali Katz Chief Technology Officer , International Delivery Assurance Services

Gali Katz is the chief technology officer of International Delivery Assurance Services, which offers suppliers performance management solutions to ensure real-time communication, on-time delivery and full customer support throughout the purchasing and logistics life cycle. He may be contacted at