YOUR RECIPE FOR STATISTICAL SUCCESS
THE PREDICTIVE MAINTENANCE COOKBOOK
Ultimate predictive maintenance explainer
The rise of industry 4.0 â€“ what is it?
Your secret sauce and recipe, mapped out
What’s inside 02 An introduction to Predictive Maintenance Statwolf’s CEO Simone gives us the lay of the land.
04 Industry 4.0 and the rise of the machines Explaining what lies behind the jargon.
06 Let’s get cookin’
Your four-page ingredients list and method for creating your own predictive maintenance routine.
10 A winning recipe
Why predictive maintenance spells success.
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An introduction to Predictive Maintenance
hat is the recipe for success in maintenance? Itâ€™s a question more and more companies in the industrial sphere are asking themselves. And as a result, these companies are looking to predictive maintenance to improve their performance. Predictive maintenance can reduce budgets, cut unplanned downtime and make factories more efficient, reliable and predictable. At its most basic level, predictive maintenance is about monitoring data to assess when machinery
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is most likely to encounter a fault or breakdown. Parts can be ordered, costly downtime avoided and machine lifetime extended â€“ if the data is monitored and interpreted correctly. McKinsey reported that predictive maintenance in factories could reduce the overall cost by up to 40 percent. Plus a predictive maintenance regime could also reduce downtime by up to 50 percent and lower equipment and capital investment by three to five percent by extending machine life.
While the benefits of predictive maintenance are clear, companies aren’t making the most of the opportunities available to them. An IBM report shows that only 27 percent of manufacturing plants have made significant investments in predictive maintenance. For example, only one percent of the data coming from offshore oil rigs across the world is being measured. Recent studies also show that unplanned downtime is costing industrial manufacturers an estimated $50 billion each year. However, with the rise of Industry 4.0 and the potential for cost-saving, companies can’t afford to ignore the predictive maintenance revolution.
Unplanned downtime is costing industrial manufacturers an estimated $50 billion each year, while predictive maintenance in factories could reduce the overall cost by up to 40 percent.”
STATWOLF'S CEO: SOURCING THE RIGHT DATA “Manufacturing companies often come to us and say they want to implement a predictive maintenance regime. The very first question anyone should ask before thinking about predictive maintenance is, ‘do I have the data to be able to do this?’ “If a factory owner contacts Statwolf because they have a machine that’s breaking down unexpectedly every couple of months, that’s a huge cost in terms of downtime, as well as the maintenance staff costs, replacement part costs and maybe even specialist repair staff. “This machine causes a lot of disruption but the maintenance data is basic, and the machine doesn’t have sensors. In this case, we’d work with an on-call sensor specialist and create a sensor system on the machine’s vulnerable parts. “Then we’d wait for a period of time until enough data is created by the sensors before we draw up a full predictive maintenance solution. “Making the predictive maintenance regime accessible towards the end-users is one of the most important points to remember. You can design the best algorithm in the world, but if it doesn’t make the end-user’s life easier, then you’re wasting your time.”
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Industry 4.0 and the rise of the machines NOT JUST A JARGON-LED TAG, THIS NEW WAY TO WORK MEANS BUSINESS.
Smart factories are equipped with technology that enables machine-to-machine (M2M) and machineto-human (M2H) communication in tandem with analytical and cognitive technologies so that decisions are made correctly and on time. Predictive maintenance uses data from various sources, for example critical equipment sensors, enterprise resource planning (ERP) systems, computerised maintenance management systems and production data. Smart factory management systems couple this data with advanced prediction models and analytical tools to predict failures and address them proactively. Additionally, new machine learning technology can increase the accuracy of the predictive algorithms, leading to even better performance.
10 BIG BENEFITS OF SMART FACTORIES According to Deloitte, smart factories bring a host of potential benefits including: 1
WHAT’S IN A NAME?
The definition for Industry 4.0 can be traced back to the Hanover Messe trade fair in 2011 when Germany Trade and Invest defined it as: “A paradigm shift… made possible by technological advances which constitute a reversal of conventional production process logic. Simply put, this means that industrial production machinery no longer simply 'processes' the product, but that the product communicates with the machinery to tell it exactly what to do.”
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Material cost savings (5-10% in operations and MRO material spend). Reduced inventory carrying costs. Increased equipment uptime and availability (10-20%). Reduced maintenance planning time (20-50%). Reduced overall maintenance costs (5-10%). Improved HS&E compliance. Less time spent on brute-force information extraction and validation. More time spent on data-driven problem solving. Clear linkages to initiatives, performance, and accountability. More confidence in data and information leading to ownership of decisions.
THE CURIOSITY OF A CAT(ERPILLAR) HOW THIS ENGINEERING CORPORATION ISN’T ALL AT SEA. The marine division of Caterpillar has a Big Data solution named Shipboard which it offers to customers. It monitors and analyses the data from generators, engines, air conditioning units and fuel sensors. For many of its fishing fleet customers, fuel usage is the number one cost. And the main use of fuel is not for sailing – but for powering the generators onboard which, in turn, power the refrigeration units. By performing a multi-variate predictive maintenance analysis, Caterpillar discovered that running more generators at lower power was a more efficient approach than maxing out a few. The savings were estimated at around $30 per hour. Across a fleet of 50 ships operating 24 hours a day and 26 weeks a year, that’s $650,000 in savings.
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Let’s get cookin’ PUTTING THE PIECES IN PLACE
oing predictive maintenance is like cooking: you need to follow the recipe carefully, adding all the ingredients in the correct order and dosage to get a well-baked result. Much like a novice charging into a kitchen without having a grasp on cooking’s core concepts, you’ll need to ask yourself several questions before you start, in order to give yourself a little schooling on the basic ingredients of a great strategy.
Your goal is your base ingredient. Your data is the secret sauce, and the project team is the element that keeps everything together.” 06 | STATWOLF.COM
ARE YOU READY FOR PREDICTIVE?
What is the wear and tear on my machinery?
How often am I performing maintenance on each machine?
How much is maintenance costing me each year?
How many maintenance staff do I have right now?
How much is unplanned downtime costing me annually?
What maintenance data do I have right now?
What data is my machinery producing and how can I analyse it?
Follow these steps for a perfectly cooked strategy.
YOUR GOAL (THE FIRST INGREDIENT) Flour is the first ingredient in many bakes – it’s the base with which all the other ingredients should be mixed. Similarly, your goal is the flour of your predictive maintenance regime and is the first thing that should be laid out on the project table.
Method: 1. Consider your goals within the two main subsets of predictive maintenance:
3. Have a chat with your data scientists, engineers, and domain experts and go through each stage of production to ensure the smaller milestone goals are achievable. 4. Align KPIs alongside your goals so you can measure the success of your predictive maintenance regime.
THE DATA (THE SECRET SAUCE)
The clarity of the data makes a huge difference to the potential success of the new regime. It really is the secret sauce to delivering real predictive maintenance results.
Seeking cost savings in terms of reducing unplanned downtime, maintenance staff costs, and replacement part costs. After all, every hour of unplanned downtime in the automotive industry costs an average of €50,000 according to McKinsey.
Method: 1. Understand that there is no such thing as a ready-made predictive maintenance programme. Instead, the process requires the data to be of a high enough standard to allow the best models to be built.
Solving consistent breakdowns. One of the unexpected consequences of machinery breaking down is that it can lead to severe supply-line problems. If you operate a plant that makes engines, for example, then your delay doesn’t just affect you but also the next manufacturer in the supply line. If the engines aren’t ready on time, you’ll be responsible for holding up the next manufacturer’s timeline – and many contracts now have severe penalties for this sort of delay.
2. Identify the team of people in your organisation who will be responsible for assembling the key data points.
2. Narrow your goals to specific tasks, for example: Minimising the exits on call. Optimising the workforce schedule. Lowering the mean-time-to-recovery. Reducing the discomfort for the companies the factory sells to.
With so much data available, the priority is in choosing the core points. For example, General Electric's latest trains have more than 250 sensors measuring 150,000 data points per minute but the core points are location, weight, speed, and fuel usage to optimise for fuel, planning, and trip speeds. 3. Begin gathering your data. Depending on the amount of data you have and your end goals, this task may involve: Data blending: Blending multiple data sources. Data cleaning: Reconciliation, missing data handling, denoising and outlier detection.
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Data transformation: Prepare the data for mathematical language, with subsampling and features extraction Predictive maintenance algorithms require collecting relevant data from external sources, for example prior maintenance and baseline information on original equipment manufacturers. 4. Undergo data processing and analysis on existing machines to determine the useful life of components. 5. Determine your forecasting: Defined actions should be triggered based on rules integrated into the predictive model.
THE TEAM AROUND YOU (THE EMULSIFIER)
Assembling your team is a core part of making your predictive maintenance project a success.
1. Domain experts: The in-house professionals who know everything about the processes with which the machines are operating, i.e. process engineers. 2. End-users: The person who will use the information provided by the predictive maintenance tool for practical purposes, i.e. maintenance operators. 3. Technical experts: Data scientists and analysts who will dive into the data. If you don't have technical experts in-house, hiring an analytics team isn’t an efficient solution as it requires a lot of time and money. Instead, look towards a third-part data expert to guide you on the journey.
Without a well-selected team, your project won't succeed. Like eggs in most bakes, they are critical to success.
For an efficient predictive programme, each project team should be involved in every step of the project. While you might be tempted to focus on the technical experts, your end-users should always be front and centre.
Method: A predictive maintenance project requires three kinds of professionals:
After all, it’s not about finding the fanciest algorithm; it’s about finding an effective solution that can be easily used in your company!
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THE COST OF PREDICTIVE MAINTENANCE “We always advise companies
looking into predictive maintenance to be careful with providers who promise results before diving into your data. Likewise, avoid providers who require a large fee before you start your project. “Predictive maintenance (and all
data-science projects, in general) is a step-by-step project – which should be matched with relevant, step-by-step economic estimates. “For instance, at Statwolf, we
usually quote the feasibility study first (the phase that has to assess if a prediction is doable with the available data). We then quote the deployment phase after we’ve completed the feasibility study. “Our customers appreciate this
approach, as they aren’t expected to pay a fee for a project that can’t be done. This economic structure ensures that predictive maintenance isn’t just applicable to be big giants, but for smaller companies too.”
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A winning recipe BRINGING IT ALL TOGETHER FOR STATISTICAL SUCCESS. With the rise of Industry 4.0, smart factories, and increasingly efficient technology, predictive maintenance could be a game-changer to manufactures – and key to the recipe to success. However, there are three key takeaways to keep in mind:
Companies who invest in predictive maintenance experience cost reductions of 3.6%.” 10 | STATWOLF.COM
1. The early adopter’s advantage. PWC estimates that companies which invest in predictive maintenance experience an annual cost reduction of 3.6 percent combined with a 2.9 percent increase in annual revenue. 2. If it seems to good to be true. It probably is. An old adage, but accurate still, especially with predictive maintenance where providers may promise results before examining the data. 3. Data analysis is always a win. Even if you discover that you’re not yet ready for a predictive maintenance programme, your organisation will learn a lot about your processes and you’ll glean useful information for moving forward. Useful as data analysis is, companies are being hampered by knowledge gaps and the sheer amounts of data available to them.
A CUSTOM SOLUTION: Statwolf’s data science solutions are a simple answer to complex problems. If you’re responsible for highly complex problem solving with a need for strict monitoring and issue prevention, our advanced online data visualisation and analysis solutions make it easy. The requirements of every data science project are different. Whether you’re implementing a predictive maintenance programme for a manufacturer, creating a fraud detection system for a retailer, or designing a machine learning algorithm, our data scientists can help. The value of data isn’t in collecting it – it’s in taking actionable steps from the insights it gives you. Statwolf offers fully customised demos and data solutions specifically catered to your needs.
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