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6 Top Use Cases for Big Data in Manufacturing

With so much data being generated in real-time from smart sensors and IoT devices in manufacturing plants across the country, manufacturing companies are feeling the pressure to embrace big data analytics as part of their standard operating procedure. Thanks to the pandemic, the pace of technological advancement in manufacturing has become more of a quantum leap than a gradual trickle. In May 2020, Forbes predicted that due to COVID-19, manufacturing will experience five years of innovation in the next 18 months. What’s more, the big data analytics manufacturing industry, which was valued at $904.65 million in 2020, is expected to reach $4.55 billion by 2026. While there is enormous potential for big data analytics in manufacturing, most companies are still playing catchup. A study by IBM found that manufacturers are lagging behind their cross-industry peers in their ability to create a competitive advantage from analytics. Only 53% of industrial manufacturers report that the use of big data and analytics is creating a competitive advantage for their organizations compared with 64% of cross-industry respondents. Despite this setback, manufacturers are working hard to develop data maturity and generate gains from big data. The study also found that three-quarters of industrial manufacturing companies have either started developing a big data strategy (45%) or are piloting and implementing big data projects (32%) on par with their cross-industry peers. In this article, we’ll look at the types of data generated in the manufacturing industry, why big data matters to manufacturers, and the most common use cases for big data in manufacturing. So where does all this big data come from?👀 Data is constantly generated from assets like sensors, pumps, motors, compressors, and conveyors. Data can also come from outside partners, vendors, and customers (eg: customer feedback, supply chain and logistics data). In fact, IoT sensors enable manufacturers to track data points from non-computerized machines as well. This is known as condition monitoring—the process of monitoring the condition of an asset in real-time to anticipate its maintenance needs. These sensors enable global manufacturers to collect real-time shop floor data (business intelligence) that allows them to continuously adapt their processes. Collectively, this data feeds into dashboards, scorecards, and databases. The data is used to generate reports as well as real-time business intelligence. The sheer quantity of data generated by factories today requires modern storage and processing tools in order to mine the data. In many cases, manufacturing data is stored in data lakes via the cloud and is processed on GPU clusters rather than traditional CPU processors. Sounds good. But what can I do with all this data? 🤔 Big data matters because companies are increasingly competing on minute differentiators like speed, consistency, and customer service rather than competing on a product. In critical industries, the insights generated by data analytics can spell the difference between life and death. Automakers, high-precision parts suppliers, medical device manufacturers, and F&B companies know that maintaining high-quality standards is essential for safety and compliance. Manufacturers must be ready to apply AI and machine learning to discover patterns and build models to make predictions and continuously improve their business processes. Data can be used to look for signals such as defects, downtime, or yield, with dashboards and applications that can monitor key KPIs in real-time. Manufacturers can also build models to make advanced predictions regarding production volume, equipment failure, and product quality. Did you know? A McKinsey study found that the appropriate use of data-driven techniques by manufacturers “typically reduces machine downtime by 30 to 50 percent an increases machine life by 20 to 40 percent.” What are the top use cases for big data in manufacturing? 🤓 1. Predictive maintenance Manufacturing profits depend largely on maximizing asset yield, so performance increases from reduced asset breakdowns can lead to massive productivity increases. Preventive maintenance—performing maintenance on an asset ahead of anticipated failure—isn’t always optimal because it hinges on doing maintenance earlier than needed, which reduces Overall Equipment Efficiency (OEE). Maintenance engineers are increasingly moving towards predictive maintenance, which is more accurate. Maintenance tasks are scheduled only when warranted, which keeps costs down. Predictive maintenance uses historical data to determine an optimal maintenance schedule. It also involves using sensing equipment to collect data in real-time. If the software detects an anomaly in your operations or a potential equipment defect, a work order is automatically triggered. Predictive maintenance keeps the maintenance frequency as low as possible because a work order is only triggered under specific conditions. Over time, the predictions grow more accurate based on the data generated by the real-time monitoring of an asset (condition monitoring), work order data, and benchmarking MRO inventory usage. Predictive maintenance sensors can perform vibration analysis, oil analysis, thermal imaging, and equipment observation. Examples include: Monitoring the temperature of computers and machinery to prevent overheating or using smart HVAC units to control building temperature and save energy. Monitoring pressure in a water system to predict when a pipe could fail. Monitoring oil particles in construction or fleet vehicles Tip: Predictive maintenance hinges on processing multiple datasets associated with different sensors and other maintenance detection devices. However, combining data from multiple sensors requires additional investments in data processing tools. For example, you might need to integrate data stored in a dedicated sensor database with data stored in your CMMS. 2. Anomaly detection Anomaly detection means identifying data points that lie outside of the norm. There are three types of anomalies: Point anomalies - a single datapoint that deviates from the rest of the material Contextual anomalies - abnormalities in a specific context (eg: time delay due to environmental influences) Collective anomalies - a collection of data points is anomalous relative to the rest of the data. Manufacturers can use anomaly detention to determine where and when abnormal behavior has occurred. Isolating the anomalous data points helps with performing a root cause analysis to determine why a particular asset failed or why product quality did not pass muster. 3. Supply chain management Supply chain analytics uses data to improve decision-making across the entire supply chain. It expands the dataset for analysis beyond the internal data held on ERP (Enterprise Resource Planning) and SCM (supply chain management) systems to include point-of-sale (POS) data, inventory data, and production data. Aggregating data points from different junctures of the supply chain gives managers insights into every facet of real-time operations. Amazon, for example, has patented an “anticipatory shipping” process in which orders are packaged and pushed to the delivery network before customers place an order. Other use cases: Optimizing delivery systems Delivery routes must account for variables like changing fuel prices, road closures, and changing weather conditions. Sensors on delivery trucks, weather data, road maintenance data, fleet maintenance schedules can all be integrated into a system that looks at historical trends and makes recommendations accordingly Cold chain monitoring Cold chain monitoring technology supports temperature-sensitive product logistics through data logging. In industries like F&B, pharmaceuticals, and chemical processing, even a slight change of a few degrees in product temperature can render the product unusable. Monitoring technology allows logistics professionals to track temperature situations in real-time and adjust heating and cooling remotely. 4. Demand forecasting Demand forecasting is the process of making predictions about future customer demand based on historical trends. Doing so helps businesses make informed decisions about pricing, business growth strategies, and market potential. Here are some other wins you can achieve with demand forecasting: Optimize inventory management and reduce holding costs Forecast upcoming cash flow for more accurate budgeting Improve production lead times (the time between an order being placed and the manufacturer completing the order) Most businesses forecast demand by performing a time series analysis to identify seasonal fluctuations in demand and key sales trends. If you don’t have a lot of historical data on hand, you can use qualitative data—expert opinions, market research, competitor analyses— for demand forecasting until you gather enough data to make reasonable predictions. 5. Product life cycle management (PLM) PLM is the process of managing a product from inception to retirement. Businesses use PLM software to track and share data long the product value chain, from design to manufacturing and sales. Research from MIT shows that the introduction stage— when you first launch a new item on the market— is where 70-90% of product lifecycle costs accumulate. Sales are slow as you work to build product awareness. At the same time, your organization is spending a lot of money on marketing the product. This is where data insights prove most useful: finding opportunities to reduce waste and choosing marketing channels with the highest ROI. Market data shows you which kinds of products generate the biggest ROI and what kind of pricing structure is necessary to turn a profit. As you move through the product life cycle, you’ll start collecting more data about customer preferences, which you can use in your decision-making strategy. For example, you might discover that customers are willing to buy at a slightly higher price point, or that the ideal demographic for your product isn’t the audience you’ve targeted thus far. 6. Quality assessment Quality assessment is the process of collecting and analyzing data to determine the degree to which the final product conforms to predetermined standards. This is key to ensuring customers receive quality products devoid of defects. If the quality is unsatisfactory, then you must perform a root cause analysis to determine why. Manufacturers can reduce variability using standard operating procedures (SOPs) and keeping equipment in good condition via an effective maintenance strategy. Data enables manufacturers to track the most important quality assurance KPIs, including: Specification compliance Low percentage rate of defects On-time shipping Shipping results in delivery without damage to the product or packaging Speed of response from customer service (response times, first-call resolution, etc.)

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4 Business Problems You Can Solve with Better Data Reporting

During the coronavirus pandemic, companies have downsized, restructured and gone through all manner of structural changes. Despite the turmoil, some departments like maintenance and operations have continued largely with business-as-usual. Why? They are deemed essential. Robust data reporting helps you prove your team’s impact on the bottom line, secure budgets for new projects and gain credibility—and therefore, leverage— with executive management. You wouldn’t hire a mechanic to fix your car until you’ve read reviews and compared prices—or, in other words, until you’ve analyzed the data to make sure you’re making the best choice. The same goes with business decision-making, which is far more high-stakes, where data should be the basis of every decision you make. Data reporting is the process of collecting and formatting raw data and translating it into a digestible format to measure the ongoing performance of your organization. Different industries use it for different reasons. Healthcare providers use it to optimize patient outcomes and deliver personalized care. Energy companies use it to achieve things like lower energy consumption by monitoring streaming data to make real-time adjustments in energy use and production. Using reports, dashboard widgets and live views, you can organize your data into informational summaries that monitor how different areas of the business are doing. Here are four big things you can achieve with better data reporting. 1. Improve your customer service Customer data has become an invaluable asset, used in everything from ad targeting to sentiment analysis and ecommerce personalization. But even if you’re not a major retailer, you still need insight into how your customers feel about your product or service, what pain points they’re encountering during purchase or after-sales care, and what factors lead to churn. Data reporting helps you piece together why customers are calling to complain, how much value a certain customer has brought to your business and whether that dollar amount has changed over time, as well as monitor how certain customer segments respond to various marketing or sales initiatives. Meanwhile, maintenance management data provided by a CMMS helps reduce unscheduled downtime. Delays in production can create a poor customer experience, strain relationships with suppliers and upset the logistics supply chain. 2. Control operational costs Using data to make more judicious budget allocations has obvious benefits in terms of cost control, but it can help you reduce wasteful expenditures. For example, proactive maintenance is an approach that uses historical data to predict when an asset will fail, and performing preemptive maintenance to avoid the massive costs of unplanned downtime (in the auto industry, this can be as high as $22,000 per minute). According to a study by Kimberlite, organizations that use a data-driven proactive maintenance approach see their downtime reduced by 36% compared with those who rely on reactive maintenance. Extracting insights from historical data also helps capital-intensive businesses use data to reduce their physical inventory and unsold stock. When it comes to a massive power plant or manufacturing plant, having the right replacement parts in stock can mean the difference between hours—or even days— of unscheduled downtime (and lost revenue) while waiting for a part to arrive or a quick and easy fix. 3. Secure budgets for projects Each department is responsible for demonstrating how their teams’ activities impact the organization’s bottom line. Data reporting provides teams with the ability to show tangible results, such as time saved by using a new project management tool or how much customer churn decreased since hiring a new customer success manager. Tracking data also helps you know where to allocate budgets to specific activities within marketing or sales. Are your display ads generating qualified leads, or are you better off using an account-based marketing strategy to target your most high-value prospects? Maintenance teams starting with a preventive maintenance strategy for the first time can use data reporting to show return on investment and cost savings from prolonging asset life cycle. Finally, it can be hard to secure budgets for untested initiatives, but data reporting helps you establish a track record of results, thereby making it easier for you to make the case to executive management. 4. Make better hiring decisions People analytics is the practice of using statistical insights from employee data to make talent management decisions. Over 70% of companies now say they consider people analytics a high priority. Some years ago, Google began distributing laptop stickers to new hires in the people analytics department with the slogan: “We have charts and graphs to back us up. So f**k off.” The main purpose of people analytics is to determine the root cause of HR problems like a talent shortage, high turnover or an excessively long hiring process, plan interventions and prepare for future staffing needs. For example, if the organization needs to cut down on workforce costs, you can identify where you are losing money. Perhaps your technicians’ wrench time is abysmally low (for most organizations, this hovers around 25-35%), which could mean poor work order management or even an incompetent manager. Digging deeper into the data helps you understand whether technicians are simply not working at full productivity, or if they’re simply not being assigned enough work because of an outdated maintenance management system. Access to workforce data also helps you determine the characteristics of high-performing employees so you can find similar candidates in the future, or create a training and development plan for your less able employees.

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4 "Bonus" Features of a Modern CMMS You Can't Do Without

A “good” worker gets the job done, a remarkable one goes above and beyond. The same goes for a CMMS — or indeed, any AI-powered technological asset you invest in. Beyond core features like preventive maintenance and work order management — the basic flesh and bones of a CMMS — what other ancillary features should prudent buyers should look out for? Choosing a modern CMMS that goes above and beyond standard capabilities could mean the difference between getting your maintenance management under control or wasting money. Here are five must-have though less obvious features to look out for. 1. A mobile-first product Technicians who work in a large square footage facility or leave HQ for offsite work need to access the CMMS wherever they are. A mobile-first CMMS synchronizes between all mobile devices so technicians can remotely enter data, create work orders, and access information about assets and repairs from a tablet, smartphone or laptop. However, an adaptive software interface that flexes to whichever device you’re using isn’t enough. A truly mobile product is built around group collaboration, with features like an internal chat/messaging system and the ability to ‘@’ tag team members, make comments when updating a work order and annotate reports. You should be able to share dashboards, request status updates and view real-time data. MicroMain’s Mobile Technician App lets GLOBAL users out in the field complete tasks while offline. GLOBAL is cloud-based, so you can access it from any internet-connected device even if you’re not in the office. 2. Funding forecasting When a C-level executive greenlights a CMMS purchase, they do so with one objective in mind: cutting maintenance costs. It therefore goes without saying that any maintenance solution must have tools for financial forecasting to enable teams to track maintenance-related outlays, stay on budget and maximize their asset life cycle. Forecasting is an advanced feature that many companies don’t offer or charge extra for. Basic forecasting capabilities enable users to organize receipts and foresee upcoming expenses, but the best CMMS tools integrate these features with asset inventory. Equipment and asset management help minimize the chance of equipment failure by tracking performance data and scheduling preventive maintenance. The EAM software should provide insight into repair history, work orders, floor plan management, and associated costs for each. 3. Meaningful data reporting Data collection that doesn’t generate actionable insights is a futile endeavor. Your CMMS reporting tool should help you answer questions like: How much time did we spend on safety audits last month? Is it time to replace X piece of equipment? Is our facility understaffed or overstaffed? Go one step further beyond actionable insights (fixing what’s wrong) and you get proactive recommendations (preventing a breakdown), where the data shows you how to finetune scheduled maintenance beyond a manufacturer’s recommendations. Ideally, the CMMS should cross-reference work order data by assigned technician, asset type, time to complete and so on to generate meaningful reports. Prioritize CMMS data collection that simplifies complex metrics into charts, graphs and KPIs to aid decision-making. 4. Document storage While the purpose of a maintenance solution is to record maintenance activity, it should serve as a repository for equipment-related documentation. A CMMS should come with file storage where users can upload critical documentation, like O&M manuals, equipment warranties, receipts from work orders and so on. These documents should also be accessible via the mobile app as downloadable items. 5. Web request system With the help of a CMMS, maintenance teams will have their finger on most equipment breakdowns and maintenance needs before they happen. However, sometimes s**t happens. If you run a large hotel or apartment complex, or you oversee several major power grids all at once, the first person to notice a problem might be a customer or an employee outside the maintenance team who doesn’t have CMMS access privileges. A web request system allows non-licensed users to submit work requests through a simple web form. You can customize the form to include the information you need, such as task type, building area, room numbers and the requester’s contact information. How to Get Started With so many CMMS options available, you’ll want to find the right maintenance solution that pays for itself, makes life easier for your maintenance team, and helps you stay on task and on budget. Book a demo with one of our specialists today to discuss your business needs and to see if MicroMain is right for you.

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