Its 11am. I walk the floor to see how things are going. I do this every day. Most days are good, but sometimes there are issues. A machine may have stopped or I see our workforce running around trying to resolve a problem. Understanding the financial impact of these inefficiencies is critical to the future of our business – Manufacturing Director (Medical device manufacturer with 400 resources)
Manufacturing is often unpredictable. No matter how well you plan, things will go wrong and time will be lost. On average every labour hour costs a manufacturer $35 and every machine hour costs between $15 and $65 depending on the type of the machine, depreciation and other factors. While costs are important, when each hour is translated into lost revenue, the impact of downtime, inefficiencies and output quality is significant, often costing today’s manufacturers tens of thousands of dollars each and every month.
The challenge at this particular manufacturer was that they lacked the visibility of where time was being lost. They had attempted to produce this information using spreadsheets but found that by the time they had the information in their hands, the environment had changed and the countermeasures that they would have applied were no longer appropriate. More importantly they were concerned about the accuracy of the information being produced. They decided to spot check a few timesheets to make sure. As expected, they found that the downtime and reasons being recorded by their operators were inaccurate, resulting in a general distrust of the analysis that had taken them over a month to produce. They had to find a better way…
They needed a system that not only gave them visibility of the losses in production, but also identified the losses they were building into their production plans to reduce the idle time of their machines and the skilled workforce required to run them. Any system then needed to collect the production data they needed easily and accurately, with the ideal solution being to gather this data automatically from their machines where it made economical and logical sense to do so.
Achieving this level of timely and accurate analysis requires a system that can schedule in detail to create an optimised production plan. That plan then needs to be efficiently dispatched to the resources needed to execute it in the form of job lists. Those resources need to gather the data required to track the status of jobs and that data needs to be detailed enough to provide an insight into the performance of the factory. Specifically it needs to provide the other side of the performance statistic to answer the question: why is our performance not 100%? In other words, the loss, and not just the percentage loss. It needs to provide the reason and scale of each and every loss so that countermeasures can be applied.
LYNQ provided all of this and drove the effectiveness of their resources to new heights. In fact, after applying countermeasures, they increased their resource effectiveness by 10% in under 6 months. In financial terms, $224,000 every month (when applied to the 400 resources being managed by the software). Crucially this efficiency gain allowed them to ship over $1 million of more product out of the door in the same period, much to the delight of their customers and their Board of Directors!