Case Studies

Business Intelligence Solution Provides Essential Measurement Capability in Manufacturer’s Process Improvement Efforts

With approximately 30 bottling plants and more than 170 distribution centers, Stonebridge’s client is responsible for handling the entire supply chain in North America for one of the world’s largest soft drink manufacturers.

This manufacturer’s leadership is committed to Lean Six Sigma™ as its approach for improving processes, gaining efficiencies, and increasing productivity across all locations. Central to the company’s process improvement initiative is its ability to establish metrics or key performance indicators (KPIs) that can be used to track the efficiency of core processes. This company, using the Lean Six Sigma methodology as its guiding principle, had defined its processes and KPIs, with reporting to be sliced by plant, soft drink brand, line, and shift; and it had determined the initial targets for process improvement: Production Efficiency & Downtime and Days Sales in Inventory. The problem lay in the existing data capture and reporting processes, which ironically were themselves inefficient. Data-collection practices were inconsistent from one plant to the next, and the primary data capture mechanism was Microsoft Excel™. For instance, each plant manager spent an average of one hour each day manually entering production data into individual spreadsheets; at month-end, these same managers spent two hours consolidating the month’s production results in a single spreadsheet. On the reporting side, there was no centralized data architecture, which prevented operations managers and other decision makers from getting the information they needed to isolate production and inventory glitches and drive improvements in these areas. The company’s leadership realized that the lack of consistent and accurate measurement and reporting tools was severely undermining the effectiveness of its process improvement effort. Budgetary constraints also compounded the client’s problem.

Working within the client’s budgetary limitations, Stonebridge consultants standardized data collection methods and created a Web-based dashboard-style reporting capability that provides detailed drill-down analysis of production efficiency by plant, line, product, and shift as well as inventory by location, product, and day. To create this solution, Stonebridge first performed a concentrated “discovery and design” variation of its Phase Zero™ assessment methodology to determine the precise scope of the project, define business-user requirements and business rules governing each process, and make recommendations for go-forward technical methods and tools. Upon the client’s approval of the Phase Zero findings, Stonebridge consultants began Phase 1 of the development effort focused on establishing a standard data architecture and reporting and analysis framework. Stonebridge developed a custom Web-based data capture application with a data validation mechanism using Microsoft’s ASP.NET 2.0 framework. SQL Server 2005 supports the custom data capture and reporting applications. Stonebridge defined the underlying data extraction routines using SQL Server Integration Services (SSIS) to collect production- and inventory-related data from other source systems, including existing ERP systems. To give the client Web-based dashboard-style reporting with mouse-over and drill-down capabilities, Stonebridge used the Cognos 8 business intelligence software suite.        

The Stonebridge solution enables the client to combine data from multiple sources and gain greater insight into the actual effects of its Lean Six Sigma process improvement initiative. Key benefits include:

  • A consistent environment for reporting and analysis across all bottling operations
  • Dashboard-style reporting that allows decision makers to track KPIs on a single screen and drill down for more detail about the root causes of performance issues
  • Time savings via easy-to-use Web-based data capture application
  • Data consistency and accuracy via unified data collection and validation mechanism
  • Graphical reporting of production efficiency by day, line, product, shift
  • Analysis of downtime “root cause” by day, line, shift, machine, reason, product
  • Consistent data collection and reporting of daily inventory by location, product, day
  • Ability to compare inventory levels across product and location in a meaningful way due to “normalized” KPI definition

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