How this job shop increased machine utilisation by 10% in one year

Once the implementation phase was finalised, visibility into the job number information and associated shop floor activity really helped Stephanie see the productivity by machine. “I could see that a job ran for three hours and which machine it ran on. I could go back and see the percentage of utilisation for a job with Alora if need be.”

Stephanie often refers to a puzzle analogy. She says, "Optimisation is a matter of scheduling the right jobs on the right machines, at the right times.” She credits Alora with providing the visibility she and the Shop Manager need to determine the most efficient sequence of steps for every combination of projects on the shop floor. One of the most noticeable improvements is the ability to immediately respond to utilisation declines, rather than wait a week for the traveller to reveal a problem.

“We are monitoring our machine utilisation processes in real-time, and much more closely than we were able to prior to Alora. Being able to analyse the data on jobs and to cost out parts is a lot quicker, because we’re not doing it manually anymore.”

“For example,” she continues, “in the past, a machine operator would think a part only cost $200 because they estimated their hours at the end of the day. Now, we can see the reality of how long it took and its true production cost. We have the setup vs. cycle time detail that we did not track previously. We’re also not adding to that time by rifling through loads of paperwork.”

Before JobBOSS², the team had to backtrack from information in the traveller a week or two after the fact. During that time, the part might have gone out for anodising or finishing, and then it sat in a stack of products to QC, test, and review before Stephanie got it. There was no doubt that profit margins were inconsistent as a result of getting incomplete information too late in the process.

“With Alora,” Stephanie says, “I know when the machine shop finished every part, and I know about the utilisation of each machine going into each part. That’s essential to our revenue and profitability growth and being able to provide accurate estimates on similar jobs going forward.”

Before Alora and JobBOSS², the company was generating $6 million annually. Today, that figure stands at roughly $10 million. By automating the process with JobBOSS², Tamarack has been able to maintain the revenue increase.

As the shop has become more efficient using Alora, the company has grown without adding any new employees or machines. That’s due largely to how Alora often reveals that scheduling difficulties are the cause of underutilization. Stephanie says, “It shows that we had availability in our schedule. As the scheduler, I could have had a machine operator load something in one machine while still working on the other machine. Alora brings these issues to the forefront.

Stephanie reports that JobBOSS² was not telling the team what jobs or tasks to schedule ahead of time because all the necessary shop floor data was not yet in the JobBOSS² system. “Alora is helping us to get that data into JobBOSS² so we can drive process improvements,” Stephanie says.

Looking forward to the next 12 months, the Tamarack team expects to schedule smarter using the data it is gradually accumulating in the system. Stephanie expects overall machine utilisation to go from the current 30% to 40% in the next three months and to 60% in the next year.

Stephanie also expects Alora to help decrease project lead time. “Instead of taking six months to get the machine out, we should be able to get down to, at most, four months, and still respond quickly to customer needs and requirements.”

Stephanie and her team are always eager to recommend Alora to friends in the industry. She says, “If they have the personnel and they are looking for ways to do more with a streamlined staff, I think Alora is the way to understand how to put the machine utilisation puzzle pieces together for optimal efficiency.”