How to improve production efficiency AssetManager How to improve production efficiency

What does improving production efficiency actually mean? Q1. What does improving my production efficiency actually mean? Literally, improving the efficiency of a production operation means raising the levels of attainment in three discrete areas of production; 1. Performance (or rate of production, e.g. metres/hr, tonnes/hr, bottles/hour) 2. Availability (or machine up-time) 3. Quality (or the proportion of good products produced as a %age of the whole number produced) These can be considered on a machine-by-machine basis, by department, by site or as a business as a whole, all derivations will all be calculated from individual machine productivity data. The three component parameters are used together to determine what is now regarded as the industry standard metric for indicating production efficiency, OEE (Overall Equipment Effectiveness). This metric is calculated by simply multiplying together the three individual %ages for the three component elements to obtain an overall performance figure. This figure is a simple but very clear indication of just how a machine (or department or business) might be operating. Although there are three important component measures, many companies will concentrate on any combination of these three parameters without associating them, it can be any number of the three, they are often worked on individually by discrete departments. Although OEE is a simple indicating metric the complexity, resource and effort required acquiring the necessary data often limits the extent to which companies can engage in the OEE measuring regime. One thing on which all companies agree however is that improvement in productivity, be it one or all three of the component elements, delivers in a significant improvement in profitability.

This table shows a simple example of an OEE exercise. We were given data from a customer for a 6-colour printing machine. The table here shows the data supplied; we normalised the calculation to a 300 day/24 hour working year. The numbers are self-explanatory – you can see on the right-hand side a key table showing maximum theoretical running costs and the maximum theoretical profit.

Based on calculations shown in the previous slide, we tabulated the numbers in a table that shows the impact of operational efficiency on profits. We made simple assumptions for the material wasted during set up. The table indicates the financial impact of a modest change in efficiency.

A 5% uplift in OEE produces a €90 000 uplift in profitability. Bear in mind that we are still talking about only one process machine. Also, it is typical that the setup waste percentage in production is around 15%, not 3%. In summary, the profit uplift will be even more significant than the numbers presented in this simplistic model.

Annual losses based upon setup (scrap) material and operational costs over the time period Further analysis of setup times shows that further savings can be made. If the ‘total loss’ figures at the bottom of the page show the cost to the business carried by extended setup times.

Why do I want to improve my production efficiency? Improving the production efficiency of a manufacturing process is one of the most effective mechanisms for improving the profitability of an organisation. Very often the measures that need to be taken are not complex, nor are they costly to implement. The key to be able to make the changes is knowing where the losses in production occur. There is no ‘silver bullet’ for improving efficiency – a series of small steps will cumulatively make a significant difference. AssetManager is a system that is designed to make the acquisition and analysis of production data simple, instantaneously available and at a very low cost. The system hardware connects directly to production machinery and presents operators with an intuitive interface with which they can easily enter pertinent production data. Automating this production data collection minimises the cost, maximises the efficiency of the collection and analysis process, and provides reliable intelligence to users wherever they are in the world with web access. Data collection for production analysis is not a new phenomenon; companies have been arming machine operators with clipboards and pens for years. The collection of which has been expensive, tardy and with results that have been acknowledged as unreliable. AssetManager redefines the rules.

These are a few graphs that are automatically produced by the AssetManager system. This production report is aggregated using data gathered from our demonstration application. It shows us the ‘in production’ time as a proportion of the ‘available time’, and as a proportion of total time, as achieved by four rotating shifts.

This next slide shows us the unplanned downtime analysis that determines the availability performance that we saw on the previous slide.

The final slide describes the actual production quantities achieved across the 4 shifts, with corresponding scrap produced figures. From this we might conclude that there are important training requirements for our work force.

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