The list of issues plaguing manufacturers is daunting, with intensifying global competition, less technical resources, industry consolidation, environmental regulations, and higher raw material costs being among these pressing concerns. These pressures have increased the urgency for smarter decision-making, particularly in areas of continuous improvement, to ensure that manufacturers can survive and thrive.
Working as a system integrator, we often see behaviors in factories that destroy value, both on the production floor and in the planning processes. Following are explanations of three value-destroying behaviours that, once resolved, can lead to continuous improvement.
1. In siloed operations
For most manufacturers, supply chains are split into silos for geographical, regulatory, commercial or complexity reasons.
Operating on closely linked silos can be detrimental to overall productivity, as managers between siloed operations will often work toward misaligned KPIs (key performance indicators). Production decisions are made in isolation without understanding the impact on the performance of the overall system. Unified KPIs are often lacking, meaning that optimal performance of one silo can destroy value in another. In these situations, decision-makers may settle for what a precursor silo tells them, leading them to make suboptimal decisions without sufficient trade-off analysis based on metrics and facts.
These relationship dynamics spotlight the need for integrated value chains, though these issues can exist even with aligned systems, processes, and metrics. We often hear about the difficulty managing complex operations at the required level of detail, particularly considering the dynamic nature of near real-time requirements across manufacturing processes.
2. Value leaks within manufacturing process areas
Sequencing of orders and associated routes can quickly multiply to be an enormously complex problem. Optionality can be mind-boggling, and planners and schedulers may resort to their own personal philosophies on how to schedule.
This results in embedded business processes that are reinforced through stubborn repetition.
We witnessed an example of this recently. A production scheduler responsible for managing a complex 10-line environment was doing a great job sequencing production orders with an ERP capacity scheduling software system. Orders were placed on lines, constraints were managed, and there was a dynamic interplay between staff, including system interaction, verbal discussion, and production meetings prior to committing the schedule.
But there were red flags in the details.
Resource and route capacities were set once a year based on financial budget models and there was high variation between expected and actual run times. OEE metrics looked great, the team felt the job was being done well, and changeover times were always within spec.
Imagine though, the scheduler creates a conservative schedule that is not updated regularly enough.
The operators receive this easily achievable schedule, complete each order, look at the expected changeover time and use this to work on other duties. Before long, there is a cascading and accumulating inefficiency that’s difficult to measure in the defined KPIs. Focusing on to-plan performance and using operational data to update the tolerances on decision models can reduce costs or provide huge latent capacity.
3. The outdated balancing act of production vs. maintenance
Departmental walls are commonplace and can create a systematic lack of collaboration between production personnel. In fact, it’s rare to see a completely integrated production and maintenance scheduling and execution system that balances the most efficient, cost-effective, and proactive maintenance strategy against the all-important production impact.
It’s also not unusual for maintenance to be completed at the end of a shift, but it’s not certain within operations that this is the best time to carry out this work.
Wouldn’t it be better to ensure the production scheduling system has real-time visibility into the current planned or required work against each resource and that the production scheduler and maintenance planner can run multiple what-ifs to determine the best time to perform maintenance?
Intelligent decision-making overview
We see investment in a few key areas as critical to enabling quick operational improvement:
- Understanding manufacturing plant performance;
- Measuring downtime;
- Modeling appropriate OEE metrics; and
- Implementing processes.
Businesses can use this information to continuously validate decision making with systems such as TilliT’s Master Production Scheduling, Detailed Scheduling, and Execution tools that ensure the data is reflective of what is happening on the shop floor.
James Balzary is the CEO and co-founder of TilliT, a SAGE Group software solution. SAGE is a certified member of the Control System Integrators Association (CSIA). For more information about SAGE Group, visit its profile on the Industrial Automation Exchange.