Traditionally, purchasing and supply management (PSM) has strongly relied on data management, as procurement managers need to dispose of, clean, and update data of different natures to compare suppliers’ performance, and 20% to 50% of working time in procurement is related to searching for information accordingly.
Big data analytics has obvious applications in the PSM field as the tools link and aggregate all relevant information from all related parties and systems, and help supply chain stakeholders gain improved insights, visibility, and traceability with their supply chain partners.
However, despite the relevance of data management, the PSM field has been relatively slow to identify the potential role of new technologies and implement big data analytics modules than in other areas, such as marketing or manufacturing.
Big data analytics can play an instrumental role in improving supplier management. It resolves several pain points at strategic, operational, and tactical levels. Big data is making an impact on all supply chain activities. It ranges from improving delivery times to identifying ways to reduce the communication gap between manufacturers and suppliers.
A Procurement and Sourcing executives survey revealed a staggering number of critical issues that organizations are dealing with as a result of poor supplier data. Probably the most shocking result was that 93% of procurement and supply chain leaders had experienced adverse effects of misinformation about their suppliers, and nearly half (47%) experienced such negative effects regularly. consequences include wasted time (63%), delays in projects (47%), and worse, terminated supplier relationships.
Following are two examples of how big data can be used in daily procurement work:
Predict suppliers’ late deliveries
In order to ensure the On-Time Delivery of critical parts and mitigate risks, manufacturers and assembly houses need accurate predictions of potential problems. Using big data analytical modules can help foresee suppliers delivery issues and provide warnings before any damage is caused.
Such a big data analytical module is based on data inputs from various systems:
- Historical inbound shipments Data from the ERP system
- Manufacturing data via a supplier portal
- PO confirmations via a supplier portal
- Supplier service levels and PO confirmations updating trends
This enables a company to become proactive rather than reactive, to improve its selection of suppliers, management of supplier relationships, and ordering processes, and eliminate line stoppages by achieving higher rates of On-Time Delivery. It enables the Supply Chain team to focus its attention on potentially late deliveries and to prioritize the actions required to prevent these delays before they occur. It removes the burden of manual analysis of vast amounts of data – and makes endless phone calls and emails to suppliers a thing of the pas
Supplier lead time analysis
In most enterprise purchasing systems, supplier part number lead time input is entered upon the initial agreement signature and kept as static data which is not updated frequently or at all.
Since supplier lead time plays a critical role in the timing and sizing of purchase order decisions, many purchasing professionals have recognized this importance, and are looking to accurately predict lead times and develop strategies for coping with problems created by lead time variations.
So, a module that analyzes big data captured from various internal and external systems can help predict the lead time % variation of a supplier-manufactured part number compared to the current static lead time maintained in the enterprise purchasing system.
The module captures and analyzes data from the following systems:
- Purchasing information from the enterprise system
- Goods received information from the enterprise system
- Daily supplier logs such as: late parts, late PO lines, late quantity
- Supplier portal inputs such as date differences between PO contractual due date and supplier confirmation date
Blending this wide range of information and sources helps build an accurate prediction system, which highlights the following insights for the supply chain organization:
- Fine-tune supplier’s metrics to better predict if parts will be shipped on time or not.
- Specify lead time data on a part level which includes WIP (Work in process) and inventory qty recommendation for both buyers and suppliers.
- Return updated lead time to enterprise system to better manage the purchase order life cycle.
About the Author
Lior is the CEO of IDAS.AI – International Delivery Assurance Services.
Lior brings 35 years of knowledge and experience working closely with global aerospace tier 1 supply chains helping manage suppliers and –ensuring high-quality products, delivered on time, with full customer support throughout the product life-cycle, and from RFQ to LTA.
In 2018 he established IDAS.AI which provides a comprehensive solution for the Manufacturing and Assembly sectors that delivers full visibility across the entire PO Life Cycle – ensuring the On-Time Delivery (OTD) of hundreds of components by hundreds of suppliers.
