Gaining analytics insights that extend beyond single-equipment
performance requires looking at multiple sources of data in a
combined way. It’s becoming common for companies to look
at manufacturing data combined with safety, product field
performance, warranty data, and other non-traditional content.
Learn how to build a strategy through actual examples.
When we talk about data in the connected enterprise, we aren’t talking about the quantity of data, we’re talking about the RIGHT DATA which is likely found across multiple sources.
But the challenge isn’t the AMOUNT of data…its finding the RIGHT data, across multiple sources
…creating the right association so that the data is transformed into INFORMATION, and the information is transformed into WISDOM.
The challenge is to take PAST and PRESENT DATA, and transform it into FUTURE DATA allowing us to be predictive, not just reactive
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Modern workforces are tasked to support multiple plants and functions
Getting the right information to the right people helps to drive improved collaboration and efficiency
Management - see my production status and recommend adjustments to better manage my global operations and maximize profits.
Operations - gain insight into usage patterns from operators, materials, equipment, enabling me to improve the process for better performance.
Maintenance - know when to deploy the right resources for predictive maintenance to minimize equipment failures and eliminate unscheduled downtime.
Our high level scorecard is the place to start as we see how all our plants are operating, from here we will be able to dive into more details as needed.
Let’s start with Preventative Maintenance
In a Connected Enterprise, you can see what is happening to your machines in more detail, and you can also compare what’s happening with similar machines between multiple plants and facilities. This gives you the ability to see areas for improvement like never before. It also helps you identify and fix problems before they get to the critical stage.
From the Operations Scorecard the manager can drill down into a Preventative Maintenance dashboard for the Chicago plant that shows all of the relevant preventative maintenance activity for this plant.
If he wants to know more, he can drill further into the Chicago plant to see the specific line assets. He can look across systems, all the way down to the machine motor dashboards. He can see live data for each machine, plus historical data and fault history. He can even see the machine specs and maintenance system information, all together in one clear dashboard.
In the past, there would have been no easy way to figure this out. They would have had to go to the specific plants, one by one, and look through separate systems and machines. Not anymore because the Chicago plant is connected.
Let’s talk about OEE for a second and why we are emphasizing in our demo:
OEE - Overall Equipment Efficiency - is one of the most common measures for how manufacturing operations and machines are running. OEE = Availability x Performance x Quality (APQ)
It’s also one of the areas that we can begin a conversation around regardless of the person we are talking to at the show as it effects pretty much everyone in the business or in their operations.
So, which factor could be reducing my OEE?
Is one asset lowering my Availability? Is the asset being over maintained? Many times preventative maintenance is done on a time basis vs when it actually is needed? WHY – many times they just don’t know when it’s really needed, so its just schedule based on history.
An Example of this is: An OEM did a study and then recommended an oil and filter replacement time extension from 30 days to 45 days, this had a saving of $1.3 million in filters and labor alone for their customer. An even further analysis showed that, with predictive maintenance, the change cycle could be extended to 60 days and save an additional $1 million annually in filters and labor.
Is there a specific condition that causes unscheduled downtime?
When someone stopped the line or machine WHAT was the reason – most times this is not documented.
Daily downtime summary report that captures all the relevant data are a way to control those costs.
To that point, the biggest cost most manufactures have is downtime.
If 85% OEE is ‘world class’ that means that there is 15% downtime . Let’s just say that on average ~8% is scheduled downtime, which could be scheduled change over times or maintenance, that means 7% is unscheduled.
let’s go after the 7% of unscheduled downtime!
For examples: ARC reported that $20B is unscheduled downtime in F&B industry, and an Automotive industry survey resulted in an average cost of $22K/min in Automotive.
Most companies today have established a Corporate Responsibility Report, and Safety is a big piece of this.
In the Connected Enterprise, increased visibility also means dramatically improved safety.
Here’s the Operations Scorecard again. In this case, an EHS professional sees a live view of the safety and compliance KPIs across their company’s multiple plants.
They can drill down into a particular plant and see a more holistic view of what’s going on, in this case we are going to look at Chicago.
Safety KPI’s provide a means to track and report on safety system events, proper operation of a system, and safety system validations.
For example
We can Identify potential safety hazards and take corrective action before an accident occurs.
We can have Automated Validation Reports that reduces the manual process and are generated regularly without being "forgotten“
And we can ensure we keep the machines in compliance insuring safety devices are within their operating lifespan
In this case we see that a number of devices are highlighted red, this can give the EHS professional the view that they should contact the plant and correct the problem before anyone gets hurt or the problem grows. After all no company wants to be in the news for worker safety or worse worker deaths.
In addition, it could be that a device is operating beyond its limit or nearing the end of its safety compliance lifespan.
And here is our linkage to OEE again when we keep machine in a running state and eliminating that unplanned downtime.
In addition, some of the safety improvements in a Connected Enterprise can have an immediate ROI. For example, many companies are replacing machinery guarding with digital safety sensors — a move that reduces downtime for example when you have to make adjustments, or parts jam on the line.
Normally, when you have to go into a machine to adjust the sensors, or clean out the jam, you have to remove all of the machine’s guarding, make your adjustments, then put all of the guarding back in place. It’s a time consuming process that takes you out production time, and in reference to a jam, it is again pointed at the expensive UNPLANNED downtime.
Companies that are taking steps into a Connected Enterprise are doing it a better way. By using smart safety sensors, the machine collaborates with its operators, so you don’t have to remove all that guarding to make adjustments. By having reliable, digital sensors, you can know the machine is in the safe state for the operator to make the adjustments, and you can get back up and running quickly.
This of course helps improve that OEE we talked about before, and is just another way a Connected Enterprise helps reduce costly downtime, saving you money, while also improving the safety of your workforce.
At MightyQ’s we have to manage a diverse mix of recipes, and we need to act on decisions based on equipment and material readiness
In the Connected Enterprise, it’s a whole lot faster and safer because software manages the recipes, the raw materials, and the logging of the entire clean-in-place process. Enabling faster changeovers with a high degree of confidence improves our overall manufacturing efficiency.
Example - in response to a market-driven demand for Chocolate chip vs. Oatmeal cookies we would traditionally have had to spend many hours of not production time to create new recipes, clean the equipment, and notify operators about adjusted procedures. This is costly and can even carry a degree of risk around product quality or safety.
To that end: Water, chemicals and energy are the 3 major consumers of a CIP system, so any optimization for those saves $$
Better management of the process, like tracking the exact amount of chemicals used, the temperature of the water, and the time it takes to do the clean process all help control and manage costs and ensure correct procedures.
This is very important today as CIP is a Critical Control Point that manufactures in F&B have to be tracking as part of the Food Safety Modernization Act, which states that manufacture must have digital records showing compliance.
For background, there are the 4 T’s to CIP that are important to remember:
• Time – cleaning cycle duration
• Temperature – cleaning product temperature
• Titer – concentration of the cleaning products
• Turbulence – speed and impact of liquids projected by cleaning products that need to be generated to perform the cleaning task
So we can now track, record and report on:
When the CIP happened and why – scheduled or non-scheduled
Who initiated the CIP procedure
Was the CIP procedure validated
Monitor and track the temperature, amount of chemicals used, and energy
In a Connected Enterprise, everything is monitored, tracked, and electronically stored by the system so you can prove that you did the cleaning if you ever had a problem. It’s faster, safer, and gives you more accurate logs for legal protection if anything ever went wrong.
Today, when a traditional company gets a new large order, the person taking the order doesn’t always know if they can handle it. The status of inventory, the machine availability, workforce availability, are all separate systems that don’t talk to each other. Wouldn’t it be nice if you could know for sure whether you have capacity to take on a new order, and precisely how to reshuffle things to accommodate it?
Well, in a Connected Enterprise, you do, and it’s simple.
When your company gets a new order, you can see exactly where your raw materials are, what your machine availability is at your different facilities, what your workforce availability is, and now you can make that intelligent decision to make these products - in this place - in these lot sizes, etc.
It’s all about demand based manufacturing.
And of course we can never ignore quality, and in a Connected Enterprise your able to verify the quality of materials, batch or recipes by verifying and validating in real time and in-line with production. Having this information when you need it helps reduce costs by eliminated non value add time and waste as quality issues can be resolved before they get out of control, or during final inspections.
To maximize your investment in a Connected Enterprise it is crucial to manage assets effectively.
It also means managing change and how it impacts uptime, productivity, quality, employee safety or regulatory compliance.
Monitoring and auditing equipment or network health, as well as overall resource availability creates a foundation for optimizing maintenance and plant operations.
With a centralized tool for securing, managing, versioning, tracking and reporting automation related assets across our facilities we can better help impact uptime, productivity, quality, employee safety or regulatory compliance.
We can better:
Secure access to the system
Track detailed users’ actions
Automatically track firmware versions
Provide automatic backup and compare operations on intelligent devices
Manage process instrumentation calibration schedules and certificates
Solve problems with comprehensive diagnostics and device configuration tools
In a Connected Enterprise you can put these technologies to use and improve your plant operations from the inside out
Returning to this slide again. How do we make the transition from reactive to predictive? Where do we look for data that can be collected, aggregated, and correlated ? It starts in the automation system.
But the challenge isn’t the AMOUNT of data…its finding the RIGHT data, across multiple sources
…creating the right association so that the data is transformed into INFORMATION, and the information is transformed into WISDOM.
The challenge is to take PAST and PRESENT DATA, and transform it into FUTURE DATA allowing us to be predictive, not just reactive
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