Beginning in 2017, food and beverage manufacturer Farmer Brothers began its shift to Industry 4.0 by integrating the TrakSYS manufacturing execution system (MES) platform into its operations.
The new MES platform takes into account multiple factors, such as floor constraints, weighted priorities, changeover time between SKUs and planned line throughput to create an optimally sequenced schedule capable of maximizing efficiency.
Production Scheduling Insights
Farmer Brothers embarked on their Industry 4.0 journey in 2017 after selecting TrakSYS as their manufacturing execution system (MES) platform to provide actionable insights into asset performance and optimization. Upon successful completion of these initial phases, Farmer Brothers turned to Production Schedule Optimization as their next phase to optimize their complex scheduling process, further improve production throughput and meet client commitments.
The scope of the project was to configure the Algorithmic Production Scheduling (APS) component of TrakSYS to optimize scheduling of all manufacturing assets at Farmer Brothers new Northlake, TX facility. Production Schedule Optimization was deployed in two phases. The initial phase included dump station, roasters and packaging lines. The second phase included incorporating additional assets such as packaging lines, flavor line, grinders, dump stations and sack line.
TrakSYS APS downloads the production orders, routing options and product attributes from the existing enterprise resource planning (ERP) and supervisory control and data acquisition (SCADA) systems then runs these through an algorithm in order to develop an optimal production schedule sequence. The algorithm takes into account multiple factors, such as floor constraints (routing options, staged materials, equipment status, etc.), weighted priorities (due date, longer running jobs, how to minimize number of changeovers, etc.), changeover times between SKUs, planned line throughput and more in order to create an optimally sequenced schedule that will maximize efficiency. The newly created/adjusted optimal schedule is then pushed back to the ERP/SCADA system for execution.
Most schedulers leverage experience and tribal knowledge in creating their production schedules, often having to do this multiple times a day as conditions and priorities change. This becomes more of an art than a science. Due to lack of clarity with what is considered a best practice process, getting stakeholder buy-in becomes more of a challenge. This in turn makes it difficult to define the appropriate rules and criteria, required to codify the appropriate algorithm.
Creating early stakeholder alignment with the scheduling team was critical to obtaining their input into best practices and help ensure buy-in. A side-by-side comparison was developed so that the schedulers can compare the old way and new way in order to build confidence with the results from the new system (TrakSYS). Once confidence was established, the duplicate system was no longer used, and the focus turned to further fine tuning of the algorithm in the new system.
Stakeholder buy-in and alignment was achieved by developing a side-by-side comparison of the old scheduling methodology and the new scheduling methodology to bolster confidence in the efficacy of the new system. Courtesy: Control Engineering, Polytron
As described above, the project helped define the criteria that affects creating an optimal schedule and create alignment between the various stakeholders. The schedulers can now work as a collective team to continuously refine the scheduling approach and develop best practices across all shifts instead of each scheduler operating independently. In addition, the schedule can be generated multiple times per day as needed based on changing conditions and priorities, which in the past was too time consuming to do.
One key lesson is that the schedulers need to be consulted early and often to identify all known components that actually contribute to creating the schedule, in addition to creating stakeholder alignment early in the process. This helps ensure the schedule produced via APS is optimally sequenced and realistic, as well as guaranteeing that the team remains engaged.
This article was published by Control Engineering and has been shared from its original source. Read the full case study by Joshua Montoya, Farmer Brothers and Brandon Brock, Polytron.