A clear operating model to combat disruptions
In perfect production, the machinery would run non-stop at full speed and there would be no quality losses. In real life, however, variations and deviations are part of the daily life of every production plant.
A clear operating model to combat disruptions
Disruptions challenge the company’s goals with respect to product quality, quantity, and time of delivery. Production efficiency plays a significant role in creation of company value and implementation of profitability.
In order to improve production efficiency, it should first be determined why production losses occur. In this context, losses refer to various needless and non-productive activities in production. The key to loss identification is measurement of what kind of requests for help are made in production and where they occur.
When this is determined, a quick and reliable flow of information must be established between the production and support functions. Once the actions above are completed, it is time to specify who and how reacts to requests for help.
On paper, the process sounds simple, but the reality is often quite different. Imprecise guidelines or time-consuming practices frequently lead to situations where the problem-solving process policy is vague and roles and responsibilities are not clear.
The above is just an example (albeit completely feasible!) of what kind of excessive work and idling trouble production day in, day out. The problem-solving process policy is vague and roles and responsibilities unclear. Management of support requests can also be confusing and time-consuming.
Using the Andon by Pinja system, requests for help can quickly be targeted to the right people, the situation made clear to the entire personnel, and transparency of the operations improved. In a nutshell, the system introduces a clear process for handling production disruptions and requests for help, rendering the data associated with production-related requests for help visible and measurable. Data collection allows identification of actual production bottlenecks and addressing the root causes of problems.