A Manufacturing COO's Guide:
Using KPIs to Improve Performance
As the need to improve, and do more with less is becoming increasingly more important in today's manufacturing environment, one of the key ways to improve performance is through effective monitoring and improvement of key performance indicators (KPIs).
It's no secret, that for those intimately involved in the operations of their company, KPIs have proven to be paramount in determining quality, overall performance and efficiency and how it ties in to higher level company objectives. Whether you are a seasoned operations executive at a large manufacturing company, or a Factory Manager at a medium sized company, this guide will provide you with useful insight on how to effectively use KPIs to improve overall performance.
Determine your KPIs & key metrics
Our principal consultant recently completed a contract with one of the largest manufacturers in New England region. While working along side their Value Stream Manager, he found that, KPIs were not tracked effectively.
Depending on several factors such as volume and complexity of product being produced, some KPIs tend to be unique to a
specific manufacturing company, while others tend to be more standard to the manufacturing industry in general.
To get you started, some KPIs that apply largely to manufacturing as a whole are as follows: On-Time Delivery, Cycle time,
KPIs, Kaizen boards, OEE charts and scorecards, 5S/ Gemba walk, Visual Factory implementation
In many manufacturing companies, true process
standardization is not achieved. This makes improvement difficult because if your baseline for achievement and improvement is not defined, how can you hope to achieve world class service? How can you hope to embrace continuous improvement?
There are several tools that can assist in standardization of processes, training and . Having employees held to a certain standard based off of a training matrix is key.
All of this translates into consistent, high quality performance, and thus products or services?
Our principal consultant found that as much as 54% of data collected in OEEs, were inaccurate due to operators related errors, whether it be in the form of poor training,
Human and operator related errors account for a large amount of errors in manufacturing. Manually tracking OEEs and KPIs present room for errors. For example, three of the most cited issues with OEEs in manufacturing were 1. 2. 3. Poor data collection (failing to collect downtime, reasons for poor quality, etc)
Removing the human error element as much as possible works to improve processes and operations by
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