Imagine for a moment a packing facility that operates several palletizers. During a shift, turned cases might cause one of these machines to stop upward of 70 times. The operators on the line witness the turned cases at the palletizer, which leads them to believe something may be wrong with the machine. Unfortunately, diagnosis of the palletizer will add more interruption to the already high amount of unplanned downtime caused by the turned cases and the shutdown of the palletizer.
This scene isn’t hard to visualize. But what if maintenance staff can’t find anything wrong with the palletizer? What if the cause of the turned cases is a guiderail two buildings back that is set to an incorrect width? More unplanned downtime would be required to solve the issue and locate the misaligned guiderail.
There are many thousands of moving parts throughout any given consumer product or food processing and packaging facility, and this can make it difficult to know where losses are occurring. Looking closely at loss analysis, it’s easy to find that almost every business has opportunities to recover lost revenue — some big, others small. And many times the collection and analysis of data plays a big role in showing these opportunities within already existing processes.
Determining Losses
In order to justify putting a large capital investment toward solving a specific loss, first determine the investment’s rate of return, and that means understanding where the losses are occurring and what solution is right for the loss.
But as much as key stakeholders understand that losses must be analyzed, the majority of companies don’t have the infrastructure or capability to understand where their manufacturing losses are coming from in the first place. And all too often, organizations put the cart before the horse, grabbing up the newest technologies without first understanding if that technology can solve for their needs.
In the methodology of driving toward zero losses, organizations gather, aggregate, correlate and understand their processes, then begin considering solutions for loss management. It’s not a question of “Can we?” — it’s a question of, “Should we?”
Enhancing the MES
Using a manufacturing execution system (MES) enables an organization to collect data on assets and processes by tracking and documenting the transformation of raw materials into finished goods. In this way, new opportunities for optimization can be found to improve plant output.
Organizations using an MES are already gathering the data needed to determine loss but require an analytics engine — developed as a data model — to move toward finding an effective solution. The analytics engine will run statistical analyses on the data gathered by the MES, locate areas of loss through the algorithm, group losses by priority and area, and provide the production gain an organization can expect from solving each loss.
Using this quantitative method of analyzing the data means organizations can use already-captured data to solve for losses they may be experiencing, and then have a clear path forward to a solution that addresses the most pressing concerns on the line.
Using Rapid Changeover as a Solution Example
Because of the variety of solutions available, choosing the right technology tool for your situation can be difficult. Of course, understanding why and what you’re trying to solve is the biggest piece of the puzzle. As an example of how a solution looks when applied to a very specific type of loss, rapid changeover is one of these tools.
Generally, any consumer product process uses different materials to achieve an end product. On lines that process several different SKUs, changes to equipment are required to accommodate different packaging sizes, formulas, brand codes, etc. These changes take place through a changeover process, during which human intervention is performed such that each machine on the line is out of production for a planned period.
As with the initial example, rapid changeover is about taking the human element out of the changeover process, thus removing a certain amount of risk. Rapid changeover is synonymous with automated changeover. By automating the changeover process, we remove the human error in the opening example: The guiderails are correctly aligned for the product, preventing the cases from turning.
Sometimes there are hundreds of critical dimension changes needed on equipment to run a new SKU. Instead of using a manual system without clear scales, you can add a servo or linear actuator on the machinery that is recipe-driven and provides position feedback. Then, by selecting a brand code on the human-machine interface, the equipment is automatically moved to the desired position.
In the rapid changeover model, all of these things happen simultaneously, accurately and repeatably on equipment throughout a line. Repeatable processes and repeatable centerlines mean fewer errors in the process and, therefore, minimized downtime.
Successful Application of a Solution
In our world of ever-developing technological advancements, there’s no shortage of tools designed to reduce capital loss. Whether you’re on the process side or packing side, there will always be a technology available that could benefit your organization. Such potential solutions include rapid changeover, robotics systems and automated guiderails.
If an organization potentially has multiple assets that are causing loss, it’s important to determine the loss for each asset, find a solution for that asset and then apply that solution across the company — across all plants — on all assets of the same kind. Systemic losses across multiple pieces of similar equipment are prime examples for developing a smart solution to systematically fix all similar issues across the company.
The goal for any consumer product line is zero losses and zero unplanned downtime. Determining and mitigating losses is essential to achieving this goal, and effective data collection, modeling and loss analysis is the starting point. Using data already being collected in the MES, organizations can find the most effective solution to garner a return on capital investment companywide.
With complexity of the manufacturing industry rapidly changing, stay ahead of the curve.