The application of analytics to gain a competitive advantage is an established trend in today’s business world. In fact, a growing number of asset-intensive organizations are applying analytics to optimize their MRO inventory and other activities across the inbound supply chain.
Cultural resistance is an important element that needs to be addressed to ensure the successful adoption of new analytics capabilities at an organization. While it is natural for organizations to focus on the technical and project-management aspects of an analytics initiative and to neglect the cultural aspect, doing so would be a mistake. For no matter how technically advanced the analytics are, they provide no value if the would-be users do not actually use it.
There are typically multiple reasons for cultural resistance to analytics adoption. Most people naturally resist a change from their comfort zone. For some skeptical users, analytics are perceived as being inferior to experience and “gut feel”. For others, the analytics application can be perceived as being difficult to use or not capable of addressing the users’ real-world problems. Some measures that can be taken to mitigate the resistance include:
1. Ensuring that the application is designed and configured to address the practical, real-world pain points that confront the users. Ideally, the users are co-opted to provide input into the configuration to make sure that the final product meets their requirements. Besides improving the functionality of the application, soliciting users’ input also provides them with a sense of ownership of the application and mitigates the not-invented-here dynamic. Additionally, user concerns that crop up can be addressed at an early stage of the initiative.
2. Ensuring that the application is intuitive and easy to use. Today’s computer users have the expectation, driven by mobile-phone technology, that software applications are intuitive and can be mastered with minimal effort. This culture of impatience requires an elevated ease-of-use standard to gain user acceptance. Nobody expects or wants to read detailed technical instructions to learn how to use new technology.
3. Ensuring that there is transparency in the algorithms that underlie the analytics capabilities. If the analytics functions as a “black box”, users become uncomfortable in passing control over to unknown algorithms to make decisions that affect them. Basic users want to understand the algorithms at a conceptual level while technically-savvy users seek to understand the detailed workings, assumptions and limitations of the algorithms before trusting its recommendations.
Aside from the mathematics of the algorithms, transparency also includes precise definitions of the metrics that are calculated and reported in the application. This is important because a user’s intuitive recollection of a metric’s value can deviate significantly from an application’s calculation due to differences in metric definition. When this happens, the user’s trust in the application is diminished.
4. Ensuring that adequate communication is provided. Adoption of new analytics capabilities constitutes a change in the established business process. Standard change-management practice dictates that such change is communicated clearly and consistently by the leadership so that the users understand the rationale for the change and management’s expectations for the results of that change.
5. Appointing an analytics champion to address the unforeseen problems that inevitably crop up during implementation and also to address the users’ concerns.
Cultural resistance can make or break an analytics initiative, including those applied to optimize MRO inventory, but following these basic and common-sense steps will help to facilitate the smooth and seamless adoption of new analytics capabilities at your organization.
Download our whitepaper 12 Best Practices of Inventory Optimization to learn how you can minimize costs and maximize uptime with MRO inventory control.