What’s your plan for increasing output? More yield and capacity are possible, if you’re ready to shine a light on outdated approaches and work on tapping into hidden operational reserves. 

Are poor forecasting, production inefficiencies, and machine downtime making continuous improvement difficult to achieve? Follow these three insights to create output strategies that will positively impact your people, processes, and bottom line.

Meet the Experts

Adam Kilgore is Maintenance and Reliability Leader at Hill’s Pet Nutrition, a subsidiary of Colgate-Palmolive, and has amassed more than 20 years of experience in manufacturing. 

Ed Ballina is the founder of OpEx Consulting, a former VP of Manufacturing and Warehousing at PepsiCo, and a 40-year veteran of the manufacturing industry. 

Adam and Ed spoke with Augury’s Chris Dobbrow about how they have succeeded in reducing inefficiencies on and off the plant floor and how manufacturers can start getting recognizable value from AI and IoT technologies.

#1: Know your problem before trying to solve your problem

Manufacturing can be a slow-moving industry, and it’s struggling to adapt to a tech-focused world that changes quickly. How can industrial organizations overcome this stagnation?

Technology is useless if it’s not directed at a specific problem–your specific problem. It can be tempting to get distracted by the latest shiny object, and many manufacturers have gotten burned by that approach. The only way to overcome your challenges is to know them well, from every vantage point. 

That means doing the work: Map your processes, perform criticality assessments, and understand what’s holding you back. When you start with solving a specific problem, you’re much more likely to find the technology that can help you. And once you have that, think big, demonstrate value every step of the way, and give people a stake in the game. 

“Don’t be nearsighted,” Adam emphasized. “Look at the long-term vision–you’re building a culture. You’re transforming your processes from a run-to-failure mode to a condition-based maintenance system.”

“Personally, I’m an unabashed early adopter,” admitted Ed. But when it comes to his business, that changes. Keep your challenge front and center, he argues, and don’t be distracted by the shiny object. “I don’t need to know how the magic in the black box works. I need actionable insights I can do something about.”

#2: Make employees the key to better output

Adam teed it up perfectly: “Put people first.” With so much attention on AI and IoT these days (and forever more), it can be tempting to make tech investment the primary focus as you look to maximize your output. But it’s vital to bring people into that technology adoption process as early as possible. Doing so will give individuals and teams more stake in the game, motivate them to gain new skills, and increase trust across the board. 

Expanding on that point, Ed called for manufacturers to rethink their reward systems. Historically, maintenance workers who could walk into a plant and, tools in hand, fix a failed machine were heroes on the plant floor. Today the heroes are those who can use incoming data to see problems before they happen, saving time, money, and effort before tools even enter the picture. The physical tools will always be important, but plant workers need to add predictive insights to their toolboxes.

As Adam put it, “Trust in God. All others must bring data.”

#3: Look everywhere, evaluate everything

Getting control over processes can feel like herding cats. With overlapping and interdependent inputs happening on multiple production fronts, how can manufacturers cut through the complexity and make meaningful progress? 

“Get out on the shop floor,” Adam advised. Meetings and board rooms and computer screens are not enough, because the shop floor is where money is made, value is added, and problems can be clearly seen. 

Bridging those divergent perspectives is paramount. If corporate leaders have an overly rosy view of operations, but plant floor teams are struggling, that’s a recipe for disaster, according to Ed. “We are still in crisis manufacturing,” he said, an environment exacerbated by COVID, when what the industry needs is precision manufacturing. Getting product out the door is your primary goal, but it can’t be the only goal. When it is, you miss opportunities for making small changes that result in big, across-the-board benefits. 

Adam stressed that a process is only as complex as you make it, and revisiting and evaluating the inputs and outputs is essential. “The best thing you can do is get rid of the waste and get rid of your variability within your processes. You’ll see better output and less complexity when you go through those exercises.”

The post Efficiency Everywhere: 3 Strategies for Boosting Productivity at Your Plant appeared first on Augury.

With more and more advancements in technology, implementing an achievable process optimization plan is no longer farfetched. However, you first need to find the right technologies and approaches that work best for your particular manufacturing operation.

The key to optimizing a manufacturing process is to embrace some of the advanced industry 4.0 technologies available today. By understanding which technology is best for your manufacturing business, you will be one step closer to optimizing your process. Let’s dive a bit deeper into what this means, and into the four main technology pillars to process optimization in manufacturing.

1) Leverage Real-Time Data By Adopting Industry 4.0 Technologies

The implementation of automation and use of data in manufacturing is what’s called “Industry 4.0″, with use cases such as predictive maintenance and predictive quality. Industry 4.0 includes the following technologies critical to process optimization:

• Real-time data connectivity and capture – Use industrial IoT connectivity to securely connect to the production line assets and capture data in a central time-series repository – either on-premise or on-cloud.
• Process-based machine learning – Use process-based artificial intelligence to get visibility into the full manufacturing process in detail, and holistically, and to discover and surface process issues that need attending. By using machine learning algorithms to process and analyze real-time data, not only can process inefficiencies be identified, but they can be predicted and even avoided. 
• Digital Twin visualization – A digital twin is a virtual representation that matches the attributes and operational metrics of a “physical” production line through the captured production-line data. A digital twin of the production line enables you to quickly pinpoint performance anomalies and their root cause, providing you with actionable insights, and presenting them in the context of the production line. By having this ability, there is no need for data scientists – the system is easy-to-use and accessible for production teams.

2) Discover Primary Causes Of Process Inefficiencies

As mentioned above, by implementing process-based artificial intelligence, process engineers can identify inefficiencies, such as the formation of undesired side products, process instabilities, impurities and more. This can be done with Automated Root Cause Analysis.

Before understanding how this will help you achieve process optimization, let’s take a look at the difference between traditional root cause analysis, and automated root cause analysis.

Firstly, traditional root cause analysis takes time – often measured in days – and expert resources from multiple teams. With massive amounts of data captured from thousands of tags every minute, it’s almost impossible to find correlations between the operational variables that lead to a process inefficiency. The longer the analysis takes – the longer the process inefficiency happens in the production line. 

For this reason, production teams need a faster and more accurate way of finding early events that lead to production failures. 

Automated root cause analysis enriches historical and real-time asset data, and applies machine learning algorithms to automatically trace the causal chain of events leading to production failures. 

By doing so, investigation teams get fast and accurate insight into early symptoms of process inefficiencies, making it easy for them to pinpoint and mitigate the root causes.

3) Predict When Process Optimization is Required

Having the ability to identify why process inefficiencies in your production line happen, is priceless. But if you take this one step forward, you can also anticipate exactly when they will happen.

By applying industrial predictive analytics, you have the ability to translate data into predictive insights. 

Machine learning algorithms can then be implemented to identify relevant events and predict their outcomes. 

For example, predicting when undesired side products will form, or when a specific process instability will happen. By doing this, process teams are able to increase yield and prevent imminent quality failures.

4) Determine The Best Fit Process Values To Avoid Process Inefficiencies

Once we’ve understood why process inefficiencies happen and can predict them before they happen, it is fundamental to understand how to optimize the manufacturing process with these insights at hand. 

Predictive simulation determines how specific inefficiencies can be avoided by simulating how processes will behave in different scenarios, and how to avoid the anticipated process inefficiency. 

By using predictive simulation, process teams can:

• Close the loop and take action on analytics recommendations
• Adjust only the production settings that will eliminate process inefficiencies 
• Reduce the risks in mis-adjusting production settings

To summarize, the coming of age of industrial artificial intelligence, and machine learning specifically, has introduced an opportunity to harness production-line data to surface actionable insights and drive continuous improvement in manufacturing processes. And digital twin visualization makes it now possible for process engineering teams to use these insights independently of data scientists and take action in a timely manner.

Ready to get started with process optimization, driven by data and machine learning? Contact us!

The post 4 Technology Pillars to Achieve Process Optimization in Manufacturing appeared first on Augury.

With new AI-driven Process Health technologies, steel manufacturers can now achieve the seemingly impossible: increasing throughput and yield, while cutting CO2 emissions.

By having a greater understanding of the production process, a steel manufacturer was able to reduce blast furnace emissions by 3.5% and increase blast furnace productivity by 2%. As a result, they enjoyed €1.76 million savings on a single line while reducing energy intensity by 1.5%.

These are numbers that make sense to everyone. How did they do it?

The Challenge Of Conflicting KPIs

Like many steel manufacturers, this factory was in a constant race to throughput, together with other key objectives like energy efficiency and yield.

While their team had optimized the process considerably, the complex, dynamic nature of the production process meant that throughput, yield and energy levels were still unstable. And of course, such a dynamic process produced very messy data – further complicating matters for the process experts.

What’s more, recent ambitious targets to reduce CO2 emissions added yet another layer of complexity.

This challenge was particularly stark at their blast furnace, where process experts struggled with maintaining blast furnace productivity while decreasing coke rates and cutting emissions.

Their question became: ‘How can we increase throughput while decreasing emissions?’ And they were fully aware that these two objectives appear to directly conflict with each other.

Their answer: calling in an AI-driven Process Health solution.

Unlocking The Full Potential Of Their Production Line

After an initial meeting with the Process Health team, the decision was made to start at the blast furnace, as this is where the most immediately-addressable emissions and throughput losses were identified. 

After connecting to the production line data, the team could create a digital model of the entire production process, which in turn allowed algorithms to understand the intricacies of the blast furnace process, and in doing so provide accurate insights from their data.

Based on these insights, a set of clear, actionable recommendations could be offered to help achieve both main goals: increase blast furnace productivity (throughput) and reduce emissions.

Let’s break it down: 

1) A Single Metric for Global Efficiency

First off, one has to create a multidimensional objective model: a unified metric for overall efficiency at the production line.

This metric takes into account all of the factory’s production objectives, as well as any other necessary constraints – from blast furnace productivity to emissions reduction, energy intensity, and coke rate (yield).

2) Quantifying Untapped Potential at The Line

Using the multidimensional objective model, the process experts could finally identify precisely when their production process was operating more or less efficiently than average.

Specifically, they found that for 38% of the time their line was performing above their average across all objectives – including both high throughput and low emissions!

It was clear, then, that the potential for improvement existed. They just needed to replicate the conditions that led to those higher efficiency levels.

3) Identifying The Most Important Process Parameters – And Their Optimal Ranges

Next, one needed to calculate an Operating Envelope, which detailed the precise process ranges and set points that would optimize all their objectives – resulting in higher blast furnace throughput, as well as lower emissions and optimal energy efficiency.

Meanwhile, another keen insight was revealed to the manufacturing team. The blast furnace was already achieving that more efficient Operating Envelope 27% of the time! This meant that the target was even more realistic than they had imagined since their production line was clearly capable of it.

All that remained now was to ensure that the process remained within the envelope more often. Having revealed the full, hidden potential of their production line, the process experts could now give clear instructions and recommendations to the operators. 

4) Preventing Inefficiencies Before They Happen

To ensure these ideal conditions are maintained on the line, the process experts then created Proactive Alerts, which alert the production team to any inefficiencies as they occur. These alerts tell the team precisely what tags need adjusting, and also include Standard Operating Procedures – so operators know precisely how, where and when to act to prevent inefficiencies and continuously maintain the optimal process settings.

5) Onward And Upward With Continuous improvement

Of course, the key to Industrial Artificial Intelligence is a focus on continuous improvement – not a one-time benefit. Once implemented on the line, an AI-driven Process Health solution can continuously monitor the process and adapts to any changes in the line.

In addition, the process experts can also apply an Impact Analysis tool to monitor how changes on the line impact its performance over time. This enables them to refine their existing Proactive Alerts and add new ones, as well as to adjust their production objectives and constraints as appropriate.

Result: Global Process Efficiency

Armed with the optimal set points, and the ability to physically maintain those set points on the line, the manufacturing team were able to increase blast furnace productivity (throughput) by 2%, while lowering energy intensity by 1.5% and maintaining a stable coke rate.   

This resulted in €1.76 million in savings and extra profit on that single production line.

At the same time, they also reduced annual emissions by 3.5%!

In addition, by knowing the Operating Envelope, their process experts gained a deeper understanding of their production processes in general, with concrete metrics and recommendations. This saves time and effort, as process experts no longer have to spend hours theorizing and guessing the root causes of process inefficiencies.

Are you interested in learning more about unlocking the full potential of your steel factory? Contact us today!

The post Mission Impossible? Increasing Blast Furnace Productivity While Reducing Emissions appeared first on Augury.

Augury’s VP of Strategy, Artem Kroupenev, wrote an article for Manufacturing Tomorrow about how manufacturers dealing with staff shortages and other challenges can still implement Industry 4.0 solutions – but they will need to do it in a focused, agile and organic manner.

The full version of this article was first published at Manufacturing Tomorrow on 13 September 2021.

It’s All About Agility

“Short-staffed teams in the manufacturing industry don’t only impact production. A survey from McKinsey found that the number of manufacturing organizations that were successfully implementing and scaling industry 4.0 issues plateaued in 2020 before dropping by more than 40% to below 2017 levels.”

“Even as the pandemic (hopefully) fades, factors such as dynamic changes in consumer demand, the desire for extensive product customization, and the importance of sustainable manufacturing are all fueling the need for the agility and capability offered by industry 4.0.”

Focus, Centralize, Keep It Natural

According to Artem, it will take a concerted effort to implement industry 4.0 implementation but the rewards will make it worth it. He then goes on to elaborate on three change management strategies that will help ensure success.

1. Determine specific use cases to drive business value
2. Centralize decision-making to improve agility
3. Create the conditions for organic rather than engineered change

In conclusion, Artem states: “Industry 4.0 can revolutionize the manufacturing sector, but it will take a deliberate change management process to overcome the capacity constraints that are currently hampering adoption and innovation. For organizations seeking to identify and implement the most promising industry 4.0 use cases and drive business value, the three strategies above are a great place to start.”

Read the full article.

The post To Overcome Capacity Constraints, Industry 4.0 Implementations Must Follow These 3 Steps appeared first on Augury.

The Covid-19 pandemic has hit manufacturers hard. Many organizations have to respond to dramatic spikes in demand or switch production to in-demand products, all while operating with less than 50 percent of staff onsite. How can you do more with less? Here are five practical tips.

The post Infographic: 5 Ways to Increase Capacity in a Crisis appeared first on Augury.

The Covid-19 pandemic has hit manufacturers hard. Many organizations have to respond to dramatic spikes in demand or switch production to in-demand products, all while operating with less than 50 percent of staff onsite. How can you do more with less? Here are five practical tips.

1. Create More Capacity

Tissue, bottled water, and baby care products are seeing huge spikes in demand as consumers panic-buy. Every spare minute of production in those industries must now be harnessed to produce greater levels of output.

The paper industry already runs continuous operations 24/7 which doesn’t give you much scope to increase capacity. What you can do is reduce downtime. Paper machines run with a 12-18 hour maintenance window every two or three weeks. You may find yourself doing things like deferring shutdowns and maintenance windows. In order to generate instantaneous capacity, you may decide that you can afford to forgo this 18-hour shutdown to get over the hump. Maybe next week you’ll take four hours and do a “gas and go” to get the really necessary stuff done.

In industries that do not have continuous operations, you can try to stretch the time between maintenance shutdowns. If you are running your line five days a week around the clock, you may ask people to work overtime (while minimizing social contact) to run more operating hours. If you have a major project that was going to take you down for a week or two to do a major rebuild, you may choose to skip that.

Eventually, of course, that catches up with you. The reality is once you start utilizing the top ends of your productive output curve, that extra 1% of output comes at a very high price. You’re running over time. You’re risking the failure of equipment. But it will allow you to get more capacity online right now.

2. Defer Shutdowns Safely

In the old days before we had continuous monitoring of equipment, if we knew a bearing was bad, we would do things like go from applying grease once a week to every eight hours. We’d waste a ton of grease, but at least the grease that was there was clean, was cool and didn’t contain contaminants. It allowed you to kind of limp through the problem a little bit before you have to do a major shut down.

If you have continuous monitoring with machine health, you can use machine data to make a judgment call on the risk of deferring a shutdown. I may know I have an alarm condition on this particular gearbox, but based on the history and the failure predictions that machine health gives me, I could try and run another couple of weeks before I shut down. That helps harvest every minute of operation. If you have reliable, real-time data and analytics, you’re more likely to be able to take that risk without undue side effects.

3. Use Mothballed Equipment

You may have equipment that has been mothballed because you had capacity which you can now use. That equipment might’ve been mothballed because you had excess capacity or it was not efficient or it was expensive to run. But when you’re facing a situation like this, you’re going to grab it and every other piece of equipment that’s available.

When I ran Pepsi’s Denver facility, we had three canning lines and we used to run all three. Over time, as we got more efficient, we shut the biggest one down because we really didn’t need the capacity. But there were a couple of times when we got into a pinch that we started it up again. It might take us a day or two to make sure things were back in shape or to find out who stole what part from the line, but you quickly figured out how to get that up and going.

4. Limit SKUs

Changeovers cause downtime and not every product runs at the same level of efficiency. Most changeovers take some amount of equipment interaction so you’re down while that equipment gets reset. Secondly, the smaller volume SKUs usually do not run as well. You don’t have as much experience with them, so they rob you of uptime when you’re actually running.

In the paper business, you might decide to make nothing except the four-pack and 12 pack of toilet paper. In the beverage industry, some of our lines are capable of producing both water and carbonated beverages in different package sizes. Water is generally easier to process than soda once you get going so you may decide we’re only gonna produce water. In the Gatorade operation, they produce several different sizes of Gatorade bottles. They may decide they’re not going to lose eight hours of operations for a changeover and will stick to the iconic 32-ounce Gatorade bottle. It’s the right size and we can make tons of it.

5. Reconfigure Teams

You have to develop contingency plans for how you’re going to deal with protecting your employees and dealing with a high absentee rate while still keeping production running.

Some pieces of equipment can operate with less monitoring than normal. You might have people covering multiple pieces of equipment on the understanding that you’re going to give up some response time. But if the option is slowing the equipment down, then you’ll take 50 percent output versus zero output any day of the week.

This is where it pays to know your workforce. Figure out how much of your operators’ mechanical abilities you’re really tapping into. In many locations, you’ll find that your operator rebuilds cars as a hobby at the weekend. You may have a forklift driver that’s a tinkering inventor on the side. How can you tap that talent to help you?

If you have more operators than maintenance people, maybe Rob the operator can work side by side with the mechanic because not all work the mechanics do is technical. The operator can go grab tools for them, get parts out of the store room and to help clean up an area so they can be more effective. You might create skeleton crews by taking people from your current lineup and supporting them with temporary employees or people from other roles to help you bring more shifts of operation online.

When I ran the San Antonio plant for Pepsi, we had a virus go through San Antonio. Almost half of our people in manufacturing were out. We grabbed a few temps and shifted regular employees into jobs that they’re skilled to do and what that opens up, you try to fill with unskilled labor. I ran a piece of equipment and I was a plant manager. My production manager ran another one. The supervisor was running the filler. When you have those crises, everybody pitches in.

6. Reliable Machines

The value of reliable equipment really shows up at times like this where every customer is asking you for more. The more reliable your equipment is, the more uptime you get. I know for a fact that manufacturers are starting to place customers on allocation when it comes to hot products like water. A customer might call and want a million cases. You might say “We can give you 500,000 and that’s all we can give you”. When you do that, customers don’t like it, but they understand.

The worst thing you can do in that situation is not deliver upon your commitments. If your equipment is not reliable, if your equipment fails when you are producing that order, that’s just terrible. So having equipment that’s reliable, having equipment that can tell you when it’s gonna fail, gives you the ability to really maximize your productive output and capacity to meet the customer demands that you have in front of you.

Let’s talk about what Augury can do for you.

The post 5 Ways to Maximize Capacity in a Crisis appeared first on Augury.