In the first of this two-part series, we looked at Oden’s Golden Run recommendation engine and how it uses data to identify the most efficient way to make a product to a manufacturer’s specified quality. In this post, we will look at the Golden Run in action and share the results of a test we conducted recently over a six-month period.
Just to recap, Oden’s Golden Run recommendation engine considers the product quality levels required by the manufacturer and uses Artificial Intelligence (AI) and Machine Learning to calculate optimum settings for making a product as quickly and efficiently as possible.
Achieving peak production with the Golden Run
We conducted a test simulation where we ran a six-month data gathering test at an American industrial manufacturer. We measured metrics relating to the quality, performance and control parameters of the factory’s core product.
In this simulation, the metrics included:
- Quality: Diameter of product
- Performance: Line speed
- Control parameters: Temperature, Motor revolutions per minute (RPM), Pressure
The manufacturer had a minimum goal of 0.75% in CPK (Process Capability Index), which is a statistical measure of a factory’s ability to produce output within specification limits deployed by many manufacturers.
Using a complex system of data analysis, segmentation and extraction over that six-month period, we identified how much the manufacturer could save by using our Golden Run settings.
The optimum conditions we identified delivered a CPK of 0.83, some way above the target minimum. Also, we now had on record the best duration, line speed, temperature, motor RPM and pressure conditions that the manufacturer needed to create its most efficient production run.
We found that by operating at the optimum Golden Run settings, the manufacturer could have saved more than 230 hours over that six-month period – that’s almost ten days of full-time production. This means that the manufacturer could use the new Golden Run settings to execute the next run 15% more efficiently than previous runs.
Providing a dollar value to Golden Run savings
How do we put a dollar cost on savings? Savings vary from company to company, as every organization’s overheads are unique to them. The machine cost, material cost and required human resources will all need to be considered.
There are other variable costs associated with savings particularly in the areas of power, maintenance and wastage. The longer you run a machine, for example, the higher the costs of maintenance and power.
What if you could control the cost of power without sacrificing the product quality? Operating with the optimum Golden Run settings to reduce production time will result in savings around maintenance and power. Similarly, if you’re prepared to be flexible with your product quality parameters, you may be able to deliver more products faster.
The Golden Run recommendation engine gives you the flexibility and control to make decisions that will drive the most revenue for your organization.
How you can achieve your optimal Golden Run
The Golden Run recommendation engine is available as a part of the Oden platform, and it’s powered by its Artificial Intelligence framework – Mímir.
Our goal is to enable manufacturers to release even more value from their data by expanding the applications of the Golden Run. In the near future, the Golden Run will also process environmental data, adding yet another dimension that you can control to drive revenues from your production.
Ultimately, the Golden Run recommendation engine will execute machine control – manufacturers’ machines will automatically identify their optimum performance settings and seek to achieve them without human intervention.
If you’re interested in learning how the Golden Run recommendation engine can drive revenues for your factory, please get in touch with one of our experts.