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Every industry wishes they had a crystal ball to help them make better predictions about when equipment will fail, which markets to enter (and which to avoid at all costs), and which products will turn into bestsellers.    While there’s no cure-all to alleviate the risks of doing business, Internet of Things (IoT) devices have proven highly valuable in enhancing preventive maintenance and allowing businesses to predict equipment failure with near-perfect accuracy.    IoT sensors collect machine data (eg: operating temperature, supply voltage, vibration, etc.) and wirelessly transmit the data to a cloud-based, centralized data storage platform in real-time. Artificial intelligence automatically analyzes the data to detect anomalistic patterns that might portend imminent machine failure. If an anomaly is detected, the platform automatically generates a work order request, which is sent to your CMMS in order to be assigned to an available technician.    IoT in predictive maintenance: What is it about? Unlike reactive maintenance, the main objective of predictive maintenance is to plan ahead to avoid unexpected equipment failure. The costs of unscheduled downtime vary widely by industry. Automotive manufacturers lose $22,000 per minute of downtime. According to Gartner, the average cost of IT downtime is $5,600 per minute. In fact, 59% of Fortune 500s experience at least 1.6 hours of downtime per week, which costs them $4.6 million per year in lost productivity.   While preventive maintenance is typically based on a calendar or usage schedule, most equipment failure (82%) occurs randomly, according to a study by the ARC Group. In fact, only 18% of equipment fails due to age. Preventive maintenance draws on historical failure metrics and OEM recommendations to schedule maintenance at an optimal frequency, but it ignores real-world conditions.  IoT-assisted predictive maintenance allows maintenance supervisors to collect machine data to monitor the operating condition of high-value assets in real-time. The system automatically collects data on availability, reliability, and metrics such as Mean Time Between Failures (MTBF) and Mean Time To Repair (MTTR). Machine learning models learn from this data over time to make better predictions regarding machine failure.    Real-time equipment monitoring also enables facilities to respond faster to emergencies—especially when a work order request is automatically triggered at the first sign of equipment breakdown.     How does it work?  IoT devices communicate using Machine to Machine (M2M)—technologies that allow machines to automatically exchange data. M2M enables networked devices to exchange information and perform actions without the manual assistance of humans.    For example, if an IoT sensor detects irregular vibration patterns in an asset, it can send a work order request to your CMMS to schedule preventive maintenance and circumvent asset failure.   The main components of an M2M system include sensors, RFID, a WiFi network, and autonomic computing software that is programmed to help a network device interpret data and make decisions.    M2M technology was first adopted in manufacturing and industrial settings where other technologies such as SCADA and remote monitoring helped remotely manage and control data from equipment. It is now used in a variety of industries including healthcare, business, and insurance.     For example, ECG units already exist that transmit information on the blood pressure, pulse, or blood sugar level of the patient to doctors in the nearest hospital while the patient is being transported in the ambulance. This technology enables medical professionals to prepare for the patient’s arrival and start treatment quicker—which can save lives.    What are the benefits of IoT asset tracking? Every business has valuable assets, from manufacturing equipment to vehicle fleets and even livestock. IoT asset tracking extends the useful life of assets by automatically generating work order requests and maintenance schedules based on real-world conditions.    Outside of maintenance management, IoT asset tracking provides a myriad of benefits including better inventory management, theft prevention, and cost savings.    Implement predictive maintenance - Transition from preventive maintenance (time-based or usage-based maintenance) to predictive maintenance, in which maintenance is scheduled strictly when needed. This allows organizations to cut down on the costs of over- or under-maintaining an asset.  Gain real-time data insights - Tracking equipment conditions and whereabouts in real-time lets you make better business decisions. For example, tracking fleet whereabouts allows you to optimize routes according to traffic conditions, weather, etc.  Improve inventory management - Replenish inventory at the right time in just the right amount. Having access to real-time inventory data enables organizations to implement just-in-time inventory management and cut down on warehousing costs and avoid losing revenue due to understocking.  Locate and identify lost equipment - Identify lost or stolen items to improve theft prevention and recovery Reduce costly problems - Monitor alerts and take immediate action to reduce negative impacts from malfunctions/equipment failure or asset theft. Image credits: 1. Check Time by Smashing Stocks from NounProject.com 2. Lan Network by VectorsLab from NounProject.com  

  When companies take stock of their top competitive advantages, inventory management rarely comes to mind. After all, it’s a behind-the-scenes function— and a cost center, at that— but it underpins every successful operation, helping businesses deliver the right products in a timely manner while consistently meeting quality standards.    Too much stock results in unnecessary warehousing costs, while too little can lead to lost sales, so there’s a lot of money at stake. Data shows that ⅓ of businesses will miss a shipment deadline because they’ve sold an item that wasn’t actually in stock. This is typically due to low visibility in inventory management flows—sales, marketing, and fulfillment teams aren’t sharing real-time sales and inventory data.    Inventory management represents an important decision variable at all stages of product manufacturing, distribution, and sales, in addition to being a major portion of current assets. In fact, inventory often represents as much as 40% of total capital at industrial organizations.   If you neglect inventory management, you run the risk of production bottlenecks, which can result in irreparable reputation loss for companies operating in competitive industries.    Here’s how you can optimize your inventory management to kick butt.  What is stock control?  Stock control means ordering enough stock of a product that sells well. To achieve this, you must have high-quality data for tracking item cost, sales forecasts, and sales figures.   1. Data quality is everything Good inventory management comes from accurate demand forecasting. Reliable forecasts are needed for decisions around assortments, purchasing volume, and safety stocks (extra stock which is stored in the warehouse to prevent an out-of-stock situation). Use historical data together with knowledge about inventory turnover, current order levels, and expectations for future sales.    Remember that inventory management doesn’t apply only to the raw materials you use to produce goods. It’s equally important to stock the right tools and replacement parts to perform planned maintenance tasks— especially preventive maintenance. Consequently, having robust maintenance operations data is also an important part of inventory management.    Here are the most important data points you need to know at all times: Which items need to be stocked How much of each item is needed to stock to avoid stockouts and lost sales Reconciliations of inventory balances  Inventory lead time/vendor lead time Actual and projected inventory status  Sales rate/demand forecast     2. Create processes and procedures to avoid confusion While this might sound like needless paper-pushing, you’ll finally understand the importance of having procedures in place the next time a huge shipment arrives and you’re out of warehouse space because there’s a pile of deadstock nobody knew what to do with. Standardizing processes helps you run a tight ship.    Here are some things to look out for:   Replenishment techniques. Determine minimum and safety stock levels for each product in your lineup. This can be used to trigger automated warnings when inventory falls below ideal stock quantities. Make accurate entries on every stock receipt.  Guidelines for controlling excess inventory or dead stock. Form agreements regarding the handling of excess inventory. Some suppliers allow you to return items for a refund or credit. Alternatively, you can sell excess inventory to a liquidator or divert the inventory to a different product or plant. Similarly, deadstock can be offered as a clearance sale, donated, offered to customers as a free gift, or bundled with other products.  Create an organized record system in a centralized database. Set up a database structure of showroom locations, distribution centers, delivery trucks, warehouses, and web inventory so you know where inventory resides in every part of your supply chain.  Assign product classification to all items. Product classifications include product category, group, collection, vendor, and brand. Your inventory should also designate stock items, custom orders, and merchandise you plan to sell as-is.  Maintain accurate product information. If products are sold online, make sure the product descriptions, dimensions, prices, and other product data are managed centrally so the information is consistent wherever the product is sold (online or at a brick-and-mortar location).  Audits. Perform an annual full physical inventory to determine a true inventory valuation against your financial records and determine your annual shrink (loss of inventory attributed to damage, administrative errors, etc.). Implement a regular cadence of cycle counts and pick random areas of your warehouse to spot check weekly to keep tabs on your inventory throughout the year.    3. Be buddies with your suppliers   Having a close relationship with your suppliers enables you to accurately understand their capabilities and limitations. The goal is to source a reputable supplier who can produce a quality product and turn around large orders on short notice. Purchasing cheap parts or raw materials will only lead to defects, reworks, and scrap parts. When comparison-shopping suppliers, ask about fulfillment times, minimum order value, and comparative pricing.    4. Keep tabs (literally) on your inventory using RFID chips and IoT sensors  RFID chips enable businesses to track inventory whereabouts in real-time. RFID for inventory management requires a scanner that uses radio waves to communicate with an RFID tag. The tag contains a microchip that allowers the reader to scan data and write data to the tag for real-time tracking.    These tags help to automate and expedite inventory checking as there is no manual data entry. Also, RFID tags do not require “line of sight” scan like barcodes; it is possible to read them at a distance for fast inventory processing. However, note that unlike barcodes, which can be read by a mobile app, RFID tags can only be scanned using RFID readers.    5. Use a CMMS Inventory management software provides quick and easy access to detailed inventory and ordering information. When you use a CMMS, you can also keep track of inventory data for your maintenance operations— including parts, tools, and other equipment needed to repair or replace major assets. Remember, preventive maintenance hinges on proper inventory management: you need to have the right parts on hand ahead of an asset’s scheduled maintenance.   Poor inventory management will only derail preventive maintenance, create a maintenance backlog as technicians wait for parts to arrive, and lead to more reactive maintenance down the road. This is why inventory management is a crucial aspect of reducing unscheduled downtime. 

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.)

Companies in pursuit of more cost-effective and secure computing resources are increasingly opting for cloud storage solutions because of their enhanced security and reliability. What’s more, the normalization of remote work necessitates robust IT systems that enable employees to securely access company data from anywhere. By 2022, Gartner predicts that 75% of all databases will be in the cloud. A 2020 survey found that 41% of enterprise workloads would be run on public cloud platforms by the end of the year, with another 22% using a hybrid option (a mix of on-premise servers and cloud storage platforms).    Why all the fuss? Migrating to the cloud gives you access to virtually limitless computing resources, including servers, storage, databases, analytics, and intelligence. If you deal with large volumes of data and use a variety of software applications, cloud computing might be for you.    According to researcher IDC, spending on cloud infrastructure across dedicated and shared environments increased 6.6% year on year to $18.6 billion in Q3 2021.  What is the difference between cloud storage and on-premise servers? While on-premise servers constitute onsite hardware that is controlled, administered, and maintained by your company’s in-house IT team, cloud storage outsources the task of server administration to the cloud service provider. This eliminates the costs of procuring and updating hardware and employing a dedicated IT professional to oversee the server. The cloud provider installs and maintains all hardware, software, and other supporting infrastructure in its data centers.  Cloud platforms run on a pay-as-you-go business model. Users pay a subscription fee to the cloud service provider that is commensurate with the amount of storage needed and/or cloud services used. Need to add users or increase your storage capacity? Simply upgrade your plan in a few clicks. Cloud platforms also provide access to advanced computing resources, such as dynamic loading (used to achieve better service provisioning), auto-scaling (automatically scale cloud services up or down based on defined situations), and serverless computing capabilities.   Some organizations opt for hybrid cloud solutions— using different types of IT deployment models including on-premise servers, private cloud, or public cloud. What types of applications should be migrated to the cloud? Before you scramble to migrate all your data to the cloud pronto, take note that not all processes benefit from migrating to the cloud. For example, legacy on-premise applications may be difficult to migrate, and botched migrations can prove costly. Legacy software has special attributes that may require application rearchitecting (breaking down applications and rebuilding them in a more modern, scalable design).    However, businesses that run legacy software are at increased risk of data breaches. What’s more, these systems are often not as efficient or easily integrated with other software applications. They may no longer be supported by the original vendor, meaning the vendor is no longer issuing bug fixes or security patches. Aging servers can also slow down a company’s business processes.  Cloud storage vs. on-premise servers: Pros and cons Cloud storage Best for companies that are growing quickly, and those that have a distributed workforce and global operations.    Pros Cons Less capital investment and lower maintenance costs: Companies only pay for the cloud services they use, and there is no need to install any hardware.  Less labor-intensive: There is no need for dedicated IT support staff to maintain cloud storage as server maintenance and software upgrades are outsourced to the cloud service provider.  Easy file sharing and collaboration: Distributed teams can collaborate and share data easily. Multiple users can collaborate on one common file.  Enables remote work: Cloud storage enables employees to access data from any device with an internet connection. Data is synchronized across all devices.  Easy to scale: If your current storage plan is not enough, simply purchase a higher-tier subscription.  Reduced risk of permanent data loss: All data is backed up and stored across thousands of data centers, so even in the event of a disaster, the data should remain intact.  The costs add up quickly: Cloud service providers charge according to usage, storage capacity, or a combination of factors. Costs can balloon unexpectedly for companies experiencing rapid growth. There may be additional costs for uploading and downloading files from the cloud.  Internet dependency: You cannot access files without an internet connection. Slow internet speeds can hamper access.  Requires additional security measures: Some cloud storage vendors lack adequate data security. You must take additional steps to secure your data in the cloud. Less privacy: Your data is managed by a third party and is visible to the cloud provider.   Less customizability: Cloud operators provide limited customization options, whereas on-premise servers can be customized almost limitlessly.  Fixed contracts: Beware of entering into fixed contracts that don’t respond to your changing storage needs. It may be best to opt for pay-as-you-go.   On-premise servers Best for companies that store sensitive data (eg: patient records or credit card data), large companies that require flexible/customized storage solutions, or those that don’t have access to a high-speed internet connection.  Pros Cons Greater privacy: No third party has access to your critical data. On-premise may be the preferred option for companies that handle sensitive data.  Access does not require an internet connection: You can access files and applications quickly even if you have a slow or unreliable internet connection. You can also keep your internet costs low since you don’t need to pay for a high-speed connection.  Maintain physical control over your servers: Companies can modify or upgrade servers autonomously without having to go through a cloud service provider. May offer greater flexibility and customization for their storage needs.  Requires IT support - Servers must be managed and maintained by dedicated staff. In enterprise organizations with massive datasets, this is a full-time job.  Increased maintenance costs: Companies must buy hardware, software, and licenses to upgrade or repair servers.  Requires significant capital investment: High upfront costs of purchasing servers and hardware, and installation is a time-consuming process.  Limited scalability: On-premise servers are difficult to scale quickly in the event that your organization needs more storage. Scaling requires the installation of new hardware, which is expensive and time-consuming. Increased risk of data loss: All data is stored on an internal server, which poses risks unless you have an offsite backup service.    Transitioning to the cloud: How does it work? Cloud migration is the process of transferring databases, applications, and IT processes into the cloud. Usually, it’s not a simple plug-and-play process, and requires lots of advance planning.  There are various ways to do a cloud migration, from a procedure as simple as a “lift and shift” (migrating your application to the cloud with little or no changes) to a complete application re-architecture. The most complex, time-consuming step during a cloud migration is migrating data— especially when it involves a large amount of data. In most cases, data can be transferred over the internet (simply upload your databases to the cloud service provider’s website), but for massive databases that would take too long to transfer over the internet, some providers offer physical data transfer methods, such as loading data onto a hard drive and then shipping the device to the provider. Before you migrate to the cloud: a checklist  1. Determine how you’d like to migrate your data There are two ways to perform a cloud migration: shallow cloud integration or deep cloud integration.    Shallow cloud integration (AKA “lift and shift,” “rehosting,” or the “forklift approach”) entails moving the on-premise application while making limited or no changes to the cloud servers or the application code, except whatever is required to run the application in the new environment. In other words, ite means moving the application as is.    Benefits of shallow integration Cost-effective Tends to be a less costly, labor-intensive migration that can be performed relatively quickly.  Fewer security problems  Does not pose additional security risks, so long as the application is secure, up-to-date, and patched before the migration.  An easy way to get started in cloud computing Moving mission-critical IT infrastructure from on-premise servers to the cloud constitutes a major commitment of time and money, although these costs are justified in the long term. A shallow cloud integration is a good starting point for complex cloud integrations in the future.    Deep cloud integration involves modifying the application so you can use advanced cloud services. This is usually a necessary step when migrating legacy software to the cloud. You can even opt to upgrade legacy software to a cloud-native framework for better overall performance, efficiency, and scalability—currently one of the biggest trends in the software industry.   Benefits of deep cloud integration: Faster deployment You can deploy your apps and services faster and scale them more quickly.  Enables edge computing Certain applications that require low latency (no delays) can only be enabled after a deep cloud integration.  Facilitates remote work  Authorized employees can access applications over the internet from any location. Enables businesses to operate in distributed work environments.  2. Determine which applications to move to the cloud Some of your applications may already be optimized for on-premise servers and don’t need to be migrated to the cloud. You may wish to keep applications that hold sensitive data (eg: medical records or credit card numbers) stored on on-premise servers. Certain industries like finance and healthcare require businesses to use on-premise servers for security reasons.  3. Establish KPIs for cloud migration  As with any new technology adoption, organizations should establish KPIs to evaluate the success of a cloud migration. More importantly, KPIs reveal unexpected problems and help you determine when the migration is complete.  For each KPI, set a baseline metric so you can compare the pre-migration performance of your application to its post-migration performance.  Sample KPIs for cloud migration include: Response time Page load time Error rates CPU usage % Memory usage    4. Set your budget and choose a cloud service provider Before you set a budget and start searching for vendors, make sure you’re clear on your specific business needs. Create a checklist of requirements (technical, security, data governance, service management) and minimum expectations to reference while shopping around. At minimum, choose a provider who can help you optimize your budget, manage your cloud infrastructure, and offer 24/7 support.  Most organizations underestimate the actual costs of cloud migration, so do your homework. According to a ‘State of the Cloud 2020’ report by Flexera, “organizations are over budget for cloud spend by an average of 23% and expect cloud spend to increase by 47% next year.”  A pay-as-you-go model might work well for an organization that doesn’t have a lot of data and plans on slow but steady growth, but not such a good idea for a large enterprise with reams of data.  After you migrate to the cloud...   Establish cloud security Shut down and remove any redundant systems, and sever any superfluous network connectivity. Migrations provide the opportunity to review your security measures. While your cloud provider maintains your databases for you, security is a shared responsibility between you and your cloud service provider.  Monitor the cloud  Your cloud provider will monitor systems for basic health and availability, but you need to do your own monitoring. Monitor virtual machines, operating system resources, and application availability. Monitor system and application performance and ask your employees for feedback so you can make any necessary adjustments.  Determine if the migration was successful Keep an eye on the KPIs that were defined as part of the audit you did right before carrying out the migration. It may also help to review the business case that was formed during the project initiation phase. Interested in learning more about our next-generation cloud platform? MicroMain Global offers a user-friendly mobile app and the ability to work from anywhere.  

So you bought a shiny new software solution (yay!), trained your employees on how to use it, and now you’re pumped to overshoot all your business goals this fiscal year. Often, when companies purchase new software, a pervading sense of 'what now?' sets in after they sign on the dotted line. Software implementation is one thing—getting your new system up and running and integrating it into your existing workflows—but now the pressure is on to, well, “do better”—whatever that means.    Setting metrics-focused maintenance goals is a great way to ensure software adoption goes according to plan. For example, if unscheduled downtime totaled 17 days last year— the average amount of downtime across all industries— what is your target for this year? Which of your high-value assets should be on a preventive maintenance plan by Q2? How will you measure the ROI of your preventive maintenance efforts?       A CMMS can go a long way towards improving productivity—you can automate maintenance scheduling, make data-informed inventory forecasts, and receive maintenance requests from a website request form—but there’s a lot of behind-the-scenes legwork needed to achieve the results you’re looking for. Surveys show that 74% of CMMS users believe that this tool improves productivity, while 58% consider it cost-effective in general.   If you’ve recently purchased a CMMS or you’re wondering how to get more out of your software, here’s a roadmap to help you get the most out of your software as quickly as possible.  1. Track employee performance and crack down on the slackers (Just kidding! Sort of.)  A CMMS gives you a big-picture view of employee performance that tells you whether or not a) You’ve hired the right employees; b) You’re assigning the right tasks to the right workers; and, c) Your workers are adequately trained and equipped with the right resources to do their jobs.    Granular data allows you to drill down into each technician’s wrench time (the percentage of an employee’s shift that is spent on actual maintenance tasks), the number of work orders handled in one scheduling, and time spent on specific tasks (also known as Mean Time to Repair). If an employee spends too much time on one task, maybe they need additional training or don’t have the right tools to get the job done.    Many companies will train employees as part of their initial onboarding and then stop there. However, workers need guidance on how to use and maintain their work tools, periodic refreshers on workplace safety recommendations, and procedural guidance overall. They also need to learn incident management best practices—how to intervene in the event of an emergency or equipment malfunction.    A CMMS enables you to store documentation, instructions, and OEM recommendations in a centralized location, so employees should have access to the information they need at all times.   If you’re unsure about why you’re seeing certain patterns in the data, such as a swelling maintenance order backlog or unusually high Mean Time To Repair (MTTR), ask your employees about what’s getting in their way. Training may be the issue.    In a recent study of over 3000 businesses by the National Center on Education Quality of the Workforce, those that increased employee development/training saw an average increase in productivity of 8.6%. The study also found that companies that invest the most in workplace learning yielded higher net sales and higher gross profits per employee. 2. Practice good data hygiene because dirty data sucks  Your CMMS is, first and foremost, a data repository for all things maintenance management. Treat your database like a temple. Poor data hygiene makes it harder to make accurate predictions using your data, which can lead to major errors when it comes to making data-driven business decisions, like how to allocate the maintenance budget this year or determining if you have the bandwidth to sign a new client.  While the initial data entry to get your software up and running was most likely handled by your CMMS provider, you need to police the day-to-day data entry done by your employees to ensure that it fits certain standards. From inputting equipment data such as model number, serial number, purchase data, installation data, and so on to inventory parts information and labor information, there are many opportunities to “corrupt” the data. Here are some things you can do to avoid the problem of “too many cooks in the kitchen.”   Have your technicians enter the data themselves - Technicians should have direct access to the CMMS so they can enter relevant data as soon as they accept a work order. This is a better alternative to having technicians fill out a paper form and then pass it on to a data entry clerk or administrative assistant. The information may be illegible or the paper may get lost, and it takes more time to document the data.  Make rule-based forms for data entry - Each field in your data entry forms should have rules regarding acceptable inputs. This helps guard against inaccurate data entry from careless mistakes or negligence. For example, technicians shouldn’t be able to input letters in a numbers-only field. Certain fields should have acceptable ranges (eg: a percentage field should be 0-100), units of measurement ($, feet, lbs), or specific formatting rules (eg: social security numbers must be formatted XXX-XX-XXXX). Where possible, add a dropdown menu so technicians can select from a menu of options, rather than typing into a text field. Misspellings, missing data, duplication, or incorrect units of measurement can result in dirty, unusable data.  Restrict user permissions - Restrict user access to relevant parties. Say you operate several distilleries. Technicians from one distillery should be able to view the data from another distillery but not edit it.  Regularly review your CMMS hierarchies - Large enterprises typically have inventory hierarchies set up within their CMMS to distinguish between different manufacturing plants or worksites, each with a corresponding hierarchy that shows where assets are located and sorted into specifics such as floor, aisle, shelf, or bin. When circumstances change—say you opened a new plant or moved a number of items into a different storage unit—you need to update these changes in your CMMS so you can keep an accurate inventory count.  3. Tighten up your inventory management (no more stealing pens from the office!)  Done right, just-in-time inventory management can be a major competitive advantage. Using historical data on asset failure and automated alerts when inventory runs low, you can forecast when parts are about to fail and preemptively order replacements ahead of time. This means keeping inventory costs low while never being caught off guard without a crucial replacement part. A CMMS tracks data over time so you’ll know when to order parts, how many spare parts to keep in stock, and which parts need to be replaced with better-quality ones.    CMMS provides cloud-based inventory management so that data on accurate stock levels is available to every user. By tracking data over time, you can find patterns. You can identify which parts technicians use frequently or infrequently and match parts with pieces of equipment on which they are used.    Tracking inventory location is another powerful feature of a CMMS. Use real-time inventory tracking to keep tabs on every piece of company inventory, mapping it not just to a specific facility but an aisle, shelf, or bin. When inventory managers need a specific item, they can just look it up in the CMMS instead of calling different locations to track down the item.  4. Start tracking MTTF—because your cheap, replaceable assets need TLC, too Those “cheap” ball bearings, fan belts, and light bulbs that keep the lights on (no pun intended) at your manufacturing plant can lead to massive downtime and lost revenue if they fail unexpectedly. Mean Time to Failure (MTTF) is a crucial failure metric that measures the average amount of time in hours that a non-repairable asset operates before it fails.    Since this metric applies to assets that cannot be repaired, MTTF can also be thought of as the asset’s average lifespan. Here, “failure” refers to any disruption significant enough to result in unscheduled downtime or prevent an asset from functioning as intended. Generally, technical teams aim to extend MTTF as long as possible.    The longer the interval between part failure, the less frequently parts need to be replaced, the less time teams spend replacing parts, and the less money the organization spends replacing physical components. Additionally, a longer MTTF means teams are less likely to be caught by surprise when a machine fails.   Here’s what you can do with MTTF: Know when to stock replacement parts and how many of them to keep in stock  Make sure you’re getting the highest quality parts at the most competitive prices (and switch suppliers or parts if you need to) Schedule preventive maintenance tasks more accurately based on the MTTF of your low-value assets.    For your high-value assets, pay close attention to MTBF (Mean Time Between Failures), which measures the average amount of time in hours that a repairable asset operates before needing repairs. MTTF and MTBF are closely related because the inexpensive, non-repairable assets are what keep your expensive, repairable assets running.  5. Get started on preventive maintenance yesterday CMMS providers are always harping on about preventive maintenance—for good reason. Preventive maintenance refers to regular, routine maintenance to keep equipment in good operating condition. The point is to prevent unplanned downtime stemming from unexpected equipment failure. An effective PM plan requires careful planning and scheduling of maintenance tasks based on historical failure metrics such as MTTF and MTBF. In 2020, 76% of companies in the manufacturing industry worldwide prioritized preventive maintenance.      Preventive maintenance tasks include inspections, cleaning, lubrication, oil changes, adjustments, repairs, or replacing parts. The exact type of preventive maintenance required will vary based on operation and type of equipment. PM is typically reserved for high-value assets because of its high upfront cost.    A CMMS enables you to coordinate preventive maintenance tasks easily. The software stores the organization's maintenance data in the cloud so technicians can keep track of inspections, repairs, and replacements, and receive automatic work orders. The system can plan and prioritize maintenance tasks based on production schedules and other ongoing maintenance work, thereby minimizing disruptions. 6. Bonus tip: Consider implementing Total Productive Maintenance If you’re ready to overhaul your maintenance operations and really kick things up a notch (or ten), Total Productive Maintenance is a philosophy that entails using maintenance management as a competitive advantage. TPM strives for total perfection—no downtime, no accidents, no lost revenue—by doing several things:   Requiring employees to undertake autonomous maintenance. Training employees who operate machinery on how to inspect and repair equipment. Employees are expected to do routine maintenance tasks like cleaning, lubrication, and assume ownership over their work and their workspaces.  Eliminating waste. Any process, task, or item deemed redundant (i.e. it does not add value to the customer) must be eliminated.  Sharing maintenance responsibilities throughout the organization - Forming small, multidisciplinary teams to do preventive maintenance and autonomous maintenance (operators maintain their own equipment). Standardizing work processes to minimize error. Engaging in continuous process improvement to ensure tasks are being done in the most efficient way by using data insights to guide the approach.    Total Productive Maintenance was invented by Seiichi Nakajima of Japan between 1950 and 1970 and was first implemented at Nippon Denso (now Denso) a company that makes parts for Toyota. Implementing a TPM plan can greatly increase your overall equipment effectiveness (OEE) over time. While TPM allocates jobs normally done by maintenance technicians to all plant personnel, it does not eliminate the need for a dedicated maintenance team.  CTA: See something you like and want to implement it at your organization but aren’t sure where to start? Check out our upcoming events or sign up for our newsletter to receive ongoing maintenance management advice. 

As businesses increasingly turn to big data and automation to streamline their operations, companies are now competing on what were once considered “minor” details—auxiliary business processes like inventory control, maintenance management, and people management.    So what if your competitor achieves 15% less downtime than your organization? Well, not only are they saving hundreds of thousands of dollars in maintenance costs each year, but they’re producing more of your product and most likely worming their way into your market share. Remember: if you’re not keeping up, you’re falling behind.    Even if New Year’s resolutions went out of vogue during the pandemic, 2022’s arrival is still a welcome opportunity to reassess what you’ve been doing and prepare for the year ahead. Here’s what you need to know about the latest, most exciting trends in maintenance management to keep your business on the cutting edge.    1. Sensor technology will give everyone superpowers IoT sensors have changed the game, enabling maintenance teams to remotely monitor machinery. By automating the process of collecting maintenance data, such as mean time between failures (MTBF) and asset life cycle, facilities can establish preventive maintenance and predictive maintenance programs that grow more accurate over time as the system accumulates more data. While many forms of industrial sensing exist— including pressure, position, temperature, and speed—vibration sensing is still the most common. Vibration analysis is used in rotating machinery to detect loose or worn bearings, equipment misalignment, or low fluid levels, all of which manifest as changes in vibration (normal vibration occurs at frequencies between 6-10 kHz). Using sensors to anticipate machine failure can save companies millions of dollars. Parts that cost a few dollars can cost manufacturers many times that in repairs and lost revenue when they fail.     In 2020, Forrester predicted that companies that already used sensors on 25% of their machinery would increase their use by four-fold through 2023.    2. Decentralized repair teams? Yes, please! With sensing technology at their fingertips, companies are reconsidering whether to keep an onsite maintenance staff for each facility. Open networks of repair logs and real-time machine data enable managers to keep tabs on productivity in real-time, which enables them to quickly deploy remote maintenance teams to different facilities in the event of an emergency.    Some organizations are experimenting with decentralized maintenance management— that is, turning maintenance management into a shared responsibility among all personnel rather than the sole purview of the maintenance technicians. Instilling a culture of autonomous maintenance means involves charging machine operators with minor maintenance tasks such as cleaning equipment and checking the oil. This is done by empowering workers to take ownership over their workstations and the equipment they use.    Decentralized maintenance is ideal for large organizations that operate multiple facilities. Dispersing authority throughout the organization shortens the approval process and enables maintenance teams to execute faster.    3. You'll get a lot of s**t about downtime and inventory management if you don't do it right  With the mainstreaming of CMMS and other maintenance management software solutions, extensive unscheduled downtime and poor inventory control are no longer acceptable. A new report from Industrial IT predicts that the global CMMS software market will reach $1913.1 million by 2028, growing at a CAGR of 10% over the analysis period. Now that facilities can easily track parts inventory and availability using software, more manufacturers will focus on mitigating inefficiency in their current storeroom.    Companies know that reducing unplanned downtime yields major cost savings—thereby creating a competitive advantage. Estimates show that downtime costs industrial manufacturers $50 billion a year. Between 2015 and 2019, oil and gas companies involved in exploration and production spent an average of $80 billion a year on maintenance. With increased competition, businesses need to become more agile. Aside from technology use, practices like autonomous maintenance (training machine operators to perform minor maintenance tasks) will be key to reducing unscheduled downtime.   4. AR and VR are making a (real) impact It’s no longer just hype. Augmented reality (AR) has real capabilities as both a training and productivity tool for maintenance management. AR enhances the user’s environment by superimposing a layer of virtual information over their field of view. VR, on the other hand, places the viewer in a fully immersive virtual environment. This technology enables maintenance workers to practice complex or infrequent jobs in a safe environment. Maintenance managers can also use AR to provide remote training for technicians.  While VR is still useful for training purposes, unlike AR it cannot be used to perform maintenance tasks in real-time.   Picture this: while repairing a hydraulic pump on an assembly line, the technician can see step-by-step instructions on how to perform the repair virtually overlaid on a smartphone or while wearing AR glasses. Used in conjunction with IoT machine sensors, the AR platform can provide real-time information on pump pressure, temperatures, and other critical data.    A recent report from the Industrial Data Corporation (IDC) predicts that by 2023, the commercial use cases of AR/VR that are forecast to receive the two largest investments are training ($8.5 billion) and industrial maintenance ($4.3 billion).   5. AI is taking over the world! (Kind of, not really) AI-powered CMMS solutions can automate repetitive jobs and maintenance planning. It can identify maintenance requirements, prioritize and adjust schedules to ensure the right person is assigned to the right task. A study by Manufacturing Business Technology stated that predictive maintenance using AI can save companies over $630 billion in costs over the next 15 years. Why? Because the default M.O. doesn’t work. A Boeing study suggests that 85% of equipment fails unexpectedly despite calendar-based maintenance.    AI uses data to do continuous monitoring, which involves both the failure system and the anomaly system. The failure system reads data patterns that indicate and predict operation failure so that the system learns the symptoms and indications of failure over time. On the other hand, an anomaly system reads data as deviations from the normal routine operations. Unlike failure systems, it picks up variations from normal patterns. Combined, this data gives us a fairly accurate readout on operational processes.   

Becoming a data whiz doesn't happen overnight.   A lot of our customers tell us they want to use advanced maintenance strategies like preventive maintenance and predictive maintenance—you know, those fancy, high-ROI maintenance strategies that use analytics, historical data, and some type of SaaS wizardry to fix assets before they break down, costing your company gazillions of dollars in unplanned downtime.  But a lot of people don’t know where to start. For one thing, you need historical data to make predictive maintenance work. So we’re introducing a few software updates to make it easier for you to learn from your data, create inspection points, and keep a running checklist of things your technicians need to do to perform maintenance on each asset.  What is an inspection point? It depends on the asset. Basically, an inspection point is a key performance indicator that tells you when an asset is beginning to fail. For an HVAC, that might be runtime; for a vehicle, it’s a meter reading or observation from an oil analysis.  By monitoring your historical data, you’ll be able to identify inspection points (KPIs or conditions that tell you when an asset is most likely on the brink of breakdown), and set triggers for predictive maintenance.  Checklists tab update- Now you can add an inspection point (IP) or a checklist from the library template or create a brand new one. You can also create an inspection point from an existing asset condition or specification—this is what enables you to link specific IPs to maintenance triggers. You can even check off “Assign Work Order” and a Work Order will be automatically generated if the IP doesn’t pass. Automatic work orders mean less work for you! Checklists page added- We’ve added a page under Libraries so you can store templates of checklists and inspection points. That way, if you want to assign checklists and inspection points to multiple assets or similar types of assets, you don’t have to start over from scratch. You can organize these checklists and IPs into groups and give them a sort number.  Inspection Points are now available on mobile- You can now generate a work order from a failed IP (“Assign Work Order”), manually ‘Pass/Fail’ IPs, and edit a specification/condition value. Track your historical data using our new reading history - Okay, this is the important part. The reading history is a running table of past conditions and specifications values. By viewing your historical data, you can start to see trends. Each time you add a new value to an IP condition or specification, you’ll see a new entry under the reading history. Here are some other updates you might find useful:  OEM Part Number is now a column on the parts list  We removed Log ID from the part log and replaced the date field with a timestamp You can now require checklist/IP items to be completed before a task can be marked as ‘Completed’ Checklists and IPs can be assigned to groups and then filtered by group. Under the Checklist library, you can also manage groups by giving them a sort order IPs/Checklists have been added to the printable WO summary  

Investing in a CMMS helps your company manage its maintenance better by staying on top of work orders, tracking labor productivity, and extending your asset’s life cycle with preventive care. The best CMMS software will significantly trim your maintenance costs while increasing productivity.    If your goal is to make your manufacturing business more profitable, a CMMS might be the right solution for you. Pitching a CMMS to your supervisor is a great way to add value to your role and save your company time and money.    We understand research is essential when choosing the right CMMS for your business. That’s why we’ve listed the top five things you need to know about before you purchase a CMMS.    1.  Save Time   Fewer workers in the manufacturing industry mean less time is spent on each task as more workers are forced into blended roles.    According to the  U.S. Bureau of Labor Statistics, 716,800 employees were lost between 2008-2018. The same report projects negative job growth of -0.5 % through 2028.  This downward trend is expected to continue at a -0.5 % annual rate through 2028, with another 640,700 jobs forecasted to be lost by 2028, the BLS reports.   Despite this, manufacturing companies are still struggling to fill positions. With fewer workers on the floor, more jobs are evolving into blended roles, which means each employee will have to work smarter and become more efficient.    A CMMS system can provide your company with time-saving features like predictive maintenance alerts that let your team know when a piece of equipment could need an update. As a result, your group can establish consistent practices to improve the performance and safety of your equipment by scheduling regular maintenance tasks instead of dealing with last-minute, time-consuming and urgent repairs.    Work order management is another time-saving feature you should look for in your CMMS. Seek out CMMS software that makes scheduling and tracking work orders simple. Assign and track labor, parts, tools, and other important information right from your iPad. You can also set priorities, due dates, and alerts to ensure all work is completed correctly and on time. This time-saving benefit can reduce hours spent on a single project and save your company hours of work order payroll.      2.  Cost-Effective Not all CMMS solutions are created equal. Having a scalable software system based on your budget and needs is essential to your company’s success.    When you’re pricing CMMS software solutions, look for tiered price levels. Some systems let you pay for what you need and charge based on the number of admins and technicians using the platform. It can also be helpful to find a CMMS that offers free software upgrades and built-in customer support services.   If your team downsizes, it’s vital to have the option to reduce the cost of your CMMS bill. For example, if you aren’t using all your logins, notify your provider and ask them to adjust your bill in the next billing cycle.    3.  User-friendly    The manufacturing industry represents workers of all ages, backgrounds, and technology skill levels. Therefore, it’s essential to have a software system that everyone can use and understand without extensive training and onboarding that takes up valuable time.    You’ll want a CMMS that is compatible with all levels of technology users. When you are comparing subscriptions, make sure you choose one with free technical support and software upgrades.    You’ll need to import your existing data into your new CMMS system. Importing your data is a fundamental process that determines how effective your CMMS will be. You’ll need a customer service team in place that can help convert your data and transfer it from a third-party system to your CMMS. Depending on your skill level, you can choose to do it yourself. Either way, it’s good to have both options when presenting a CMMS solution to your team.    Another feature to be on the look for is training. Choosing a CMMS that offers regional, on-site, and headquarters training proves that your CMMS provider is investing in your success.     4.  Customer Service When you need help with your CMMS software, it should be readily available. Choose a CMMS with excellent customer services and provides you with chat reps, email correspondence, training, and phone calls.    If you have questions about optimizing your software or just need to talk shop with an expert, you want to have outstanding customer service built into your subscription to maximize your experience.    Onsite training is a great way to get hands-on experience with a new tool. For example, an indicator of superb customer service could come from a three-day on-site training program with your CMMS expert.    You should expect to receive training based on your specific needs, when, and where you need it most. Your team deserves training tailored to your business which may include a detailed review of advanced topics in an interactive environment.    When your team completes a training session or a chat with a customer service agent, they should feel confident about the new fully implemented, functioning system.   5.  Presenting a CMMS Solution to Your Boss You’ve done your research and identified a problem within your company. A CMMS could solve the issues your team is experiencing. Now you want to impress your boss by taking the first step and present them with a solution.    MicroMain’s CMMS software is loaded with powerful features to help you better manage and optimize your maintenance operations. Our software offers work order management, preventative maintenance, predictive maintenance, asset management, inventory management, and more to keep your company organized.    MicroMain’s CMMS is the easiest-to-use CMMS software on the market. We’ve optimized our CMMS system to be as user-friendly as possible, with a clean, modern, easy-to-navigate user interface. In addition, every feature was designed with all users in mind, regardless of technical expertise.     Next Steps   To learn more about our features, visit our website or request a demo today. You can also download our printable guide when you’re ready to pitch a CMMS to your supervisor.    

  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.