Global Best Practices Meet Local Realities

Companies often use enterprise resource planning (ERP) and knowledge management (KM) systems to facilitate company-wide business process improvement and innovation. They mine, analyze, and package global best practices in ERP and KM databases, thinking it will be easy and efficient to share the information across their organization. It’s not. Best practices almost always have to be adapted to local conditions, and data captured in ERP and KM systems rarely reflect these nuances. What’s usually missing? Human interpretation.

For example, a global clothing manufacturer’s Italian subsidiary adopted an ERP system’s human resources module, which required automated recruiting and selection procedures. But this proved counterproductive; informal, in-person recruiting worked best in the region. Recruiting performance actually declined until the automated parts of the new module were bypassed and personal interviewing was revived.

KM systems also fail to deliver value when their best practices are accepted without a human being’s critical judgment. An international bank introduced a KM system as a source of standard global solutions for customer-service problems. One department, seeking to quicken mortgage application processing, adopted a best practice — electronic mortgage applications — that had worked well in another country. The problem was, most of its customers didn’t have Internet access, so customer complaints about the application process increased.

In each of these cases, managers assumed the ERP and KM systems held all the answers, and they did not bother to confer with colleagues working with similar problems and circumstances. Had they used the systems as brainstorming tools, rather than blanket solutions, the benefits of having a database of global best practices to augment their local knowledge might have been much greater.

ERP and KM should be seen for what they are, data-processing systems that capture information: ERP for business processes (e.g., documenting process flow and procedures); KM for ideas (e.g., capturing and transferring specific expertise). Such systems efficiently collect and transfer data, but don’t make sense of it. It’s up to managers to interpret and modify data in each new context.