The organization faced persistent supply chain operations challenges due to inaccurate and unreliable data and a lack of clearly defined data management responsibilities. Rework resulting from inaccurate planning data and order delays resulting from unnecessary manual order processing led to extended lead times and frequent supply shortages. Despite prior attempted data cleanup and ad hoc remediation, the organization continued to face data integrity issues due to several underlying factors:
WP&C leveraged our Data Multishield framework to establish proper data management practices for the client, focusing on business goal alignment to improve supply chain operations.
- Process and Organization: By working with cross-functional stakeholders from leadership to data end users, we developed business rules to align data management processes with organizational objectives and standardize data creation and maintenance processes. We redesigned the operational model to align data management practices with business objectives and to create accountability where data issues disrupt operations the most.
- Collaboration: Guidance and tools clarify data management responsibilities and foster cross-functional collaboration to align stakeholders across all organizational levels on solutions.
- Training: We developed training plans and materials, trained the trainers, and supported enterprise-wide training on the new data management practices. We also provided supplemental education to address skill and knowledge gaps in the workforce, including RASIC training and data analysis workshops.
- Data Reviews and Governance: We established a data review process and conducted data cleanup to ensure data accuracy. In collaboration with leadership, we also developed and published data governance policies that clearly outline expectations and accountabilities.
In addition to using this framework, WP&C employed an agile approach and led the change management efforts to resolve data issues in critical supply chain processes that were eroding operations.
- The agile approach delivered rapid improvements in supply chain operations, including reduced order lead times, with the system now able to automatically release over 90% of parts
- Through a structured change management process, leadership ensured successful solution implementation and adoption—with minimal disruption or friction across the organization
The WP&C team developed and implemented the following data management recommendations to address the data failures in the supply chain:
- Prioritize Supply Chain Data Based on Operational Objectives
Data issues and corresponding supply chain data elements were ranked according to their impact on supply chain operations. We collaborated with leadership to identify key focus areas and reached a consensus on prioritized data management improvement initiatives.
- Develop Code-Setting Rules and RASIC for Key Supply Chain Data Elements
Code-Setting Rules for the prioritized supply chain data were developed to guide the creation and maintenance processes and the Code-Setting RASIC assigned accountability for data management activities. Key stakeholders' early engagement in the development process ensured solutions could be successfully implemented and sustained.
- Conduct Data Management Training and Change Management
WP&C developed a workforce communication plan and training, conducted train-the-trainer sessions, and supported rollout efforts to ensure employees understood new data management rules and expectations. As a key step in the change management process, training fostered the right employee behaviors to improve data integrity.
- Perform Data Cleanup on Key Supply Chain Data Elements
To reinforce the changes, we established a data review process that encompasses the generation of recommended data values, stakeholder feedback, and the determination of final data entry. The client’s data team used the list created in the review to clean up data issues in the systems.
- Publish Supply Chain Data Management Policies
WP&C supported the client in publishing data management policies to define the data governance responsibilities, formalize the business rules, and align systems and processes with the updated practices. As foundational pillars to drive and institutionalize changes, these policies articulate the leadership’s vision for data management and formally authorize the appropriate teams to lead and execute data management initiatives.
