As big-data initiatives create expectations of competitive advantage, organizations are beginning to come to terms with the challenges ahead.
While data quality issues take center stage, the ability to meaningfully integrate structured enterprise data -- frequently housed across multiple enterprise resource planning (ERP) systems, planning systems, manufacturing and production systems, customer relationship management (CRM), warehouse management systems (WMS), and transportation and compliance systems -- with high velocity unstructured data streams, is causing significant challenges. Organizations with globally dispersed supply chains face an additional layer of complexity. They must also attempt to integrate and leverage useful data that resides with upstream and downstream partners across dozens of nodes in value chain.
In the high-tech industry, most current business applications of big-data analytics are in sales, marketing, customer service, and manufacturing. Demand-related big-data analytics are being leveraged to optimize pricing, promotions, product variations, and product availability, as well as positively impact customer satisfaction. Supply side analytics though, have been slow to take off. With procurement teams and supply sources located globally and working off multiple systems and data warehouses, data accuracy and timeliness of data have posed bottlenecks.
Procurement organizations can transform four important areas by leveraging supply side analytics: planning, procurement, inventory management, and supply risk management. Top procurement organizations recognize that this transformation goes well beyond creating procurement dashboards, spend analysis, and color-coded supplier scorecards. These organizations leverage multi-tier supply base data, supplier financial data, product and process quality data, transportation, regulatory, and other non-spend data to create savings, transparency, procurement intelligence, innovation, risk mitigation, and competitive advantages. Along with the right talent and resource deployment, technology plays a key role, as real or near-real time information delivery and minimal latency are critical to meaningful big-data analytics.
During my conversations and exchange of ideas with other supply chain leadership teams pursuing big-data initiatives in procurement, the following three areas have generated the most interest:
1. Predict supply and supplier performance relative to perfect order fulfillment, predict a supplier's negotiation strategy, optimize sourcing awards, and address downside risks by leveraging:
a. Supplier financial performance information
b. Supplier current capacity, future capacity expansion investments, and top customers
c. Lead time/availability/pricing changes/promise versus actual supply date
d. Alternate supply source lead time/availability/pricing changes
e. Delivery expedite instances
f. Raw material pricing volatility
g. Currency swings
2. Drive micro-segmentation of supply sources and products based on factors such as manufacturing location, product category, product technology, pricing, lead time, inventory model, supply contracts, carrier contracts, trade regulations, etc., to enable:
a. Scenario planning during procurement strategy finalization
b. Inter-enterprise process optimization and process innovation across suppliers, carriers, manufacturing locations, and contract manufacturers
c. Product and process cost savings, while driving alignment of economic incentives
3. Significantly improve advanced modeling capabilities of component inventory profile across the entire network through:
a. Integration of planning signals from demand chain, NPI, engineering, and repair/refurbish
b. Component level traceability, quality, failure rate, and root cause analysis across the value chain
In each case, organizations have begun the journey by creating an MDM strategy and followed that by investing in understanding, harmonizing, and aggregating relevant data from upstream and downstream value chain partners, as well as from third-party sources. In the high-tech industry, distributors like Avnet have traditionally leveraged structured data (including partner data) residing in ERP, supply chain systems, and data warehouses, to enhance capabilities in planning, procurement, and inventory management. Distributors are now leveraging technology and next-generation visualization tools to create competitive advantages for their business partners.
I invite you to share your insight and experiences related to application of big-data analytics in procurement organizations in the high-tech industry in the comments section below.