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Ensure maintenance isn't a pain in the asset
11 June 2018
Three experts share their tips on how to maximise OEE on food production pick and pack lines and explain why having the basics in place can lead to greater success during the transition towards Industry 4.0. Representing the end user viewpoint is Geoff Hann (MIET), engineering manager at Morrisons Thrapston manufacturing site; David Jahn, director at Brillopak looks at maintenance from a machine build perspective; and Dan Rossek from Omron reviews the latest technology to help streamline and imp
The lifespan of automated equipment will vary by application and depends how hard each machine is worked and how well it’s looked after, notes Brillopak’s David Jahn. “Maintained correctly, and providing flexibility has been built into the initial design, an automated packing system could last over 20 years,” he suggests.
Plant that’s poorly maintained will likely breakdown more frequently and impact quality, as well as productivity and OEE. Breaking the cycle of reacting to emergency maintenance tasks starts with changing the mindset of your factory workforce.
With a lifetime experience in asset care, Jahn shares his tips on how to engage with engineers and machine operatives. “Always start by sorting the area. Get the team to red tag all the stuff you don’t need and create a space spares and change parts, lubricants and tools. So much time is wasted searching for a specific spanner or hex key during scheduled and emergency maintenance. Simple actions like returning tools to the shadow board alleviates frustration and saves on valuable machine downtime.”
Next, identify the equipment maintenance task that can be performed by non-maintenance personnel, e.g. changing worn parts, lubrication and lock off procedures. “By increasing their mechanical knowledge operatives start to take ownership of their machine and feel empowered.”
Engineering manager, Geoff Hann, also advises measuring each line’s OEE at the start of training, although he recommends holding off divulging this data to trainees. He explains: “Hearing a poor OEE score at the start is disheartening and can switch people off. Reward and recognise the improvements, using initial data such as running time, products packed per hour and how many can be sold. This is your quality gauge and a true benchmark to measure improvements against. Releasing the earlier data in comparison to the new data emphasises the significant change that has been achieved."
Scoring and auditing each line at the start and end of each shift is a good habit to get into, ensuring consistent standards once introduced are maintained. Defects and issues can be documented during each handover, along with corrective actions. “This helps to prevent a blame culture and ensures people take accountability for their actions,” says Hann.
Failure to resolve a machinery fault or production bottleneck issue swiftly can inadvertently set automation project up to fail, claims Jahn. “It’s psychological. If operatives fear that their jobs are on the line and then see that a machine isn’t functioning properly, it almost certainly creates a sense that it’s doomed to fail. So they won’t even try and resolve an issue. In order to curtail this, it’s imperative that operatives feel involved at the outset of scoping out the specifications for a new automated installation.”
By looking at the things that might fail, and addressing those aspects proactively, there can be a risk that key components – mechanical and electrical – might be replaced before they get to the end of their useful lifespan.
End users and machine builders may consider installing technology tools, enabling the machine to monitor its own components and operations, and provide more accurate and useful information relating to production trends and component problems. Self-diagnostic tools, like Omron’s condition monitoring function blocks are an important step towards the Industry 4.0 concept of the Smart Factory.
Rossek highlights several of the self-diagnostics facilities available today that can assure higher availability and therefore improved OEE. “Some functions are easy to implement: variable speed drives, for example, have long offered the ability to monitor the current to the motor, with a gradual increase in required current indicative of developing problems within the mechanical power train. Then there are temperature sensors, vibration monitors and other add-on condition monitoring products that can all be used as part of a predictive maintenance strategy. The key to effective condition monitoring is hidden in the automation architecture, enabling vertical integration of all the components of the system by means of field networks like EtherCat or IO Link, which allows seamless data transfer between all the field devices and fast and effective realisation when a problem occurs.”
Brillopak uses tools like this to create simple, intuitive maintenance HMIs that pinpoint exactly where on the line a fault has occurred. “To optimise OEE, speed is always of the essence. Standard on our pick and pack systems, rather than codes the HMI delivers a simile error message directing operatives to the exact place so they can resolve it without having to go through multiple steps or calling an engineer over. It might, for example, be a simple thing like a door not being locked.”
The company also uses cabling systems to connect machines to Omron function blocks. These function blocks make it simple to monitor the condition of devices such as pneumatic cylinders, sensors and servo drives/motors that can cause intermittent machine stoppages. “It’s a very accessible and visual way to monitor machine issues and make a rapid diagnosis,” adds Jahn. “If the plug light isn’t flashing green, the operative can immediately determine where the issue lies, thus optimising availability.”
The Morrisons manufacturing site where Hann works uses similar visual aids to identify downtime and efficiency data, which will ultimately be grabbed from the control system. “The benefit of machine data is there’s no bias - it’s a pure, clean and cannot be manipulated. Making it a true real time report of what is happening on your production line. We take the data, act on it and run the report again. This is central to continuous improvement.” Data like this is also used to calculate the financial loss to the business in real time, which production managers can use to drive improvements in OEE.
Rossek adds: “. It means that sophisticated functionality such as advanced self-diagnostics can be implemented as standard as part of the machine design process, helping to deliver a real OEE and asset care advantage.”
Summarising, Jahn says: “It’s always challenging in a fast moving food production environment to predict what the future landscape is going to be. But by building in the machine flexibility and giving end users access to simple visual aids to measure data, identify faults and equipping people with the skills to solve issues in the fastest possible time without causing further damage or disruption, you are on the way to increasing machine availability.
“There are multiple pitfalls than can sink an automation project. You can have the most advanced Industry 4.0-compatible technology in the world. Yet if the end user is too scared to approach the machine or nonchalant about asset care, your competitive advantage and OEE ambitions will be swiftly sunk.”
Likening Industry 4.0 to the Asset Care pyramid, Hann adds: “There’s no end game. To achieve World Class Organisation perfection is the Holy Grail quest. Even the most advanced factories will continuously be setting new improvement targets and striving for the next goal.”
All three spokespeople conclude that regardless of the size of a production and packing plant, the journey to Industry 4.0 starts and ends with enforcing rigour in asset care combined with a comprehensive grasp of all the potential maintenance issues that could disrupt production.
- Plant that’s poorly maintained is likely to breakdown more frequently and impact quality, as well as productivity and OEE
- Self-diagnostic tools, like Omron’s condition monitoring function blocks are an important step towards the Smart Factory
- With accessible enabling tools, end users benefit from increased capabilities without the complexity or incurring significant programming effort