Key Performance Indicators (KPIs) allow organizations to periodically assess overall performance or the performance of its divisions, departments, and employees by defining a set of values to measure against. Businesses use KPI to measure performance and to gain a full understanding of how the organization or a specific department is performing. KPIs are an important part of Lean manufacturing as it is a great tool for quantifying and identifying waste. It can show managers where waste is accumulating and the areas that can be improved.
The raw values that are collected are called indicators. There are two primary categories of KPIs:
- Quantitative facts: These are specific values (typically a numerical value) that is removed from personal feeling or interpretations.
- Qualitative values: On the other hand, these values are based on personal feelings or opinions, and assigned a numeric value to represent these interpretations.
Some key performance indicator examples used in manufacturing could include: defect rate, 10/10 customer satisfaction cycle time, rejection rate, utilization, availability ratio, mean time to repair, etc. A popular KPI used in Lean organizations is OEE (Overall Equipment Effectiveness). Managers can use this tool to measure the success of their production line, cycle time, and the quality of products being manufactured.
As an example, we’ll look at a facility that wants to reduce downtime in the production process. A manager will want to choose the KPIs that will drive the desired behavior in order to meet this objective. This may include total downtime, the events/actions that contribute to downtime, changeover time, and OEE availability.
Once these are set, KPIs are targeted objectives that will add the most value to the business. They are set against a benchmark to determine if they are meeting expectations or not. It is important the KPIs set are well-defined and communicated clearly to workers so they understand what is expected from them. Not only will KPIs help a manager to track efficiency, compliance, and project performance, this data can provide important information to help in decision-making processes.