ML 100 Awards
 
ibm1.png“With the BIG-I system, we are able to optimize on a global level.” —Matthias Graefe, Manager of Manufacturing IT and Business Intelligence, IBM

 

 

 

PROJECT


Smarter Manufacturing/Business Intelligence Global Initiative (BIG-I)
Analytics project allows hardware manufacturing unit to optimize on a global scale

Think your global supply network is complex and volatile? Consider what IBM’s computer hardware manufacturing team faces. Production is spread globally across 20 plants, some IBM-owned and some owned by contractors. In 2009, the team fulfilled $17 billion in revenue. Each quarter, 30% of that revenue is fulfilled in the final 72 hours. Last-minute order and configuration changes are common.

Not surprisingly, IBM faces big challenges when it comes to optimizing for order fulfillment, production capacity, and supply. Until recently, in fact, most such decisions were made at the individual plant level by managers who based their analysis on spreadsheets populated from a variety of sometimes inconsistent sources.

Three years ago, the company decided to take a more consistent, global approach by launching the Smarter Manufacturing/Business Intelligence Global Initiative (BIG-I) project. This ambitious effort was intended to create a single near-real-time repository of supply chain data and standard reports that IBM managers could use to quickly understand computer hardware demand, supply, and production conditions and, ultimately, to make better decisions at a global level. Since the system was deployed last July, IBM has saved more than $1 million on increased process efficiencies, cycle time improvements, reduced inventory handling costs, and lower transportation expenses.

“With the BIG-I system, we are able to optimize on a global level,” says Matthias Graefe, manager of manufacturing IT and business intelligence at IBM. “If we have an unexpected peak [demand] for a given product, we now have the information to move production to a different site, for example, in a way that optimizes overall efficiency.”

Based on an internally developed data model and analytics tools from IBM units Cognos and SPSS, the BIG-I system provides consistent, up-to-date data and reports to different business units involved in hardware order-to-cash and plan-to-supply processes. BIG-I is fed data by several transactional systems, including order management, ERP, control, engineering, and quality management systems. Users can select a hardware platform or manufacturing site and drill down to information on the status of an individual customer order. And the system generates alerts when, for example, order loads exceed the build plan.

Next, Graefe says, IBM plans to extend the scope of Big-I further into the supply chain, adding more analytics to enable a sense-and-respond management system. However, the project is delivering big results right now; BIG-I-related savings this year are expected to total $2.5 million.