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    Manufacturing Analytics: What it is, Top Use Cases, and Benefits

    With the arrival of Industry 4.0 and the Industrial Internet of Things (IIoT), a digital transformation is currently underway. The manufacturing industry is beginning to use analytics driven by real-time production data, not only to make better, faster decisions, but also to enable automation across the organization.

    Equipment connected through sensors and edge devices feeds massive volumes of data to cloud-based analytics platforms that can analyze and understand data faster than human perception. This data can then be used to drive real-time decision-making and significant process improvement throughout the company.

    This article will explain what manufacturing analytics is and list use cases for consideration. It will also explain the benefits and goals of manufacturing analytics applied in any shop floor or factory.

    What is Manufacturing Analytics?

    Manufacturing analytics is the use of machine, operational, and system data to manage and optimize production, including key functions such as maintenance, quality, and planning. With accurate and real-time data, manufacturers can make better, faster decisions.

    Manufacturers have used data to improve efficiency and advance their market share for many years. But the most significant change today is how data is collected. Many companies still use fragmented, traditional methods for data capture, with staff manually checking and recording factors, filling forms, and writing down operation and maintenance histories for the machines on the floor. Unfortunately, these methods are highly inaccurate due to human errors. They are also time-consuming, open to bias, and do not generate the quality of analysis required for accurate decision-making.

    But with digital transformation underway across manufacturing, connected devices can reduce labor associated with manual data collection and documentation. And because this technology and software utilize advanced analytics and algorithms, the insights derived are real-time and much more actionable.

    Automated machine data collection is driving the next generation of manufacturing analytics, unlocking a myriad of advanced use cases that range from simple monitoring and diagnosis to predictive maintenance and process automation.

    In manufacturing analytics, data capture that records events can be leveraged to increase equipment utilization, reduce cost, drive process improvement, reduce human-based errors, and do so at a depth that reveals accurate machine conditions and trends in production.

    Examining Data on Shop Floor Monitor.


    Manufacturing Analytics Use Cases

    Real-time production data is changing the manufacturing industry dramatically. Let's consider several manufacturing analytics use cases that real-time machine connectivity has made possible in manufacturing:

    Fault Prediction and Preventive Maintenance

    Preventive maintenance programs have been around manufacturing for decades. The idea is that through use-based or time-based programs, unplanned breakdowns are less likely to occur. By applying analytics, real-time data can be leveraged to do more than prevent breakdowns.

    It can predict with high accuracy the likelihood of a breakdown and the moment it will occur. This reduces costs by allowing technicians to perform repairs at the machine's optimal time and stage parts. This reduces overall downtime and increases productivity.

    Learn more about predictive analytics in manufacturing.

    Downtime Event Triggered

    Demand Forecasting and Inventory Management

    Demand forecasting is critical for modern manufacturers and having complete control of the supply chain allows better inventory control.

    But demand planning can be complex. With the addition of data science methods, end-to-end control of the supply chain can be used in conjunction with real-time shop floor data to better manage purchasing, inventory control, and transportation. Highly accurate demand plans can be generated that identify trends that would otherwise go unnoticed.

    With a better understanding of how long it takes to make parts, how long job runs will take, and the expected costs and profit of a given job, manufacturers can better estimate their need for material to improve planning.

    Price Optimization

    Cycle times play a major role in pricing. And knowing precise times for part creation and the associated costs allows for accurate cost models and optimized pricing strategies. Setting them too low reduces profitability while setting them too high may impact demand. An advanced analytics platform for manufacturing can bring this data forward to ensure prices are set appropriately. MachineMetrics can help manufacturers optimize their job standards to ensure accurate cycle times.

    Warranty Analysis

    For many manufacturers, warranty support can be a drain. Often, warranties consist of a "one-size-fits-all" approach that’s more general. This allows uncertainty and unexpected problems into the equation.

    By applying data science and capturing information from active warranties in the field, products can be improved or changed to reduce failure and therefore cost. It can also lead to more informed iterations for new lines of products to proactively avoid field complaints.


    The evolution of AI and advanced machine learning algorithms have made the rise of robotics all but inevitable. And as these robots improve, the data they supply in the execution of their duties will increase.

    By including this data within a powerful cloud-based manufacturing analytics platform, quality can be controlled at the micro-level. Robotics evolution will also lead to improved machine construction from OEM machine builders.

    Product Development

    One costly process in manufacturing is product development. To stay competitive, companies must pay for R&D to create new product lines, improve existing models, and develop new value-added services.

    Previously, this was done through excessive iterative modeling to arrive at the best product. But now, data science and advanced manufacturing analytics make it possible for much of this process to be simulated. Using "digital twins" and other modeling methods, real-world conditions can be generated virtually to predict performance and reduce R&D costs.

    Computer Vision Applications

    Automated quality control has come a long way. It’s evolved from trip sensors, drop wires, and other mechanical devices to a highly sophisticated collection of advanced optical devices. By tying these devices into data collection, sensors can add data to the stream through optics, temperature, and advanced vision applications such as thermal and infrared detection to accurately control stops. This also allows for higher speeds, lower labor, and the holy grail of any factory – "lights out" manufacturing.

    A Digital Twin Graphic of a CNC Machine.

    Managing Supply Chain Risk

    Like the data coming from production machines, data can also be captured from materials in transit and transmitted directly from vendor equipment to the software platform to help provide end-to-end visibility in the supply chain.

    Using manufacturing analytics, companies can manage their supply chains in a "control tower" format, directing and redirecting resources to speed up or slow down. They can also order backup supplies and buffer stocks when new demand is sensed and trigger secondary vendors when disruption occurs.

    Benefits of Manufacturing Analytics

    Contextual awareness is critical for advanced manufacturing systems. Manufacturing analytics provides that awareness in real-time. This makes companies more competitive as cost, quality, product development, and customer satisfaction are optimized. Manufacturing analytics empowers companies to improve productivity and profitability by leveraging the massive data stream generated by their production equipment. With intuitive visualization tools, dashboards, machine learning algorithms, and advanced analytics, actionable insights are available to managers and decision-makers across the company.

    Production Data at the Machine and Operator Giving a Thumbs Up.

    We believe the benefits of manufacturing analytics fall into three distinct categories:

    Reduced Costs

    Because processes can be optimized with the insights revealed in analytics, the cost can be significantly reduced. And the growth of robotics, as well as autonomous or semi-autonomous machine decision-making, reduces labor. The same is true of predictive and prescriptive maintenance programs proven to reduce cost and increase productivity by lowering downtime and managing parts inventories better.

    Increased Revenue

    With real-time insights available in production, inventory management, and demand and supply planning, manufacturers can respond quickly to changes in demand. Suppose the data tells them that they’re nearing max capacity. In that case, they can add overtime, add capacity, alter processes, or adjust other aspects of production to respond and maintain delivery times.

    Miscellaneous Benefits

    With the increase in capabilities provided by manufacturing analytics, there are miscellaneous benefits as well. These include reduced energy consumption, safer environmental protocols, reduced compliance errors, and increased customer satisfaction.

    Goals of Manufacturing Analytics

    Traditional data collection within manufacturing was fragmented and error-prone. It was also challenging to translate data into meaningful action and decision-making. Either the information was delayed, incomplete, or it contained unintentional human bias (such as rounding part counts or downtimes). Manufacturing analytics seeks to unsilo data, analyze it in real-time, and use it for enable better, faster decisions across the enterprise, or even automate those decisions altogether.

    By detecting issues before they occur, production processes can be optimized, and overall equipment utilization can be significantly improved. It also helps streamline supply chains and create transparency within them. Because manufacturing analytics uses advanced machine learning algorithms, it can help identify opportunities and optimize processes.

    As seen in the manufacturing analytics use cases above, product usage can be included in the new development of products. And alongside digital twin technology and incoming warranty information, it can drive new, better products with reduced failure rates and lower production costs. These same use case examples can drive increased throughput by detecting and alerting staff quickly to problems at the machine level. This reduces downtime and scrap rates.

    Selecting the Right Solution

    The area of manufacturing is undergoing considerable changes due to the development of technologies that can collect production data and leverage it to make better decisions on a daily basis.

    However, when making a decision on the right solution to enable analytics on the shop floor, manufacturers should consider a few key-value props. Here are some questions to ask when considering a solution:

    • Can the solution collect production data in real-time?
    • What is the source of the production data? Equipment, people, or systems? All the above?
    • Is the data standardized into a model that can be easily propagated in out-of-the-box reports as well as consumed by other management systems (MES, ERP, CMMS)?
    • How quickly can the software get you to value? What problems will it solve?

    We would like to introduce you to MachineMetrics Manufacturing Analytics Software. Our platform enables the real-time, autonomous collection of machine data for accurate production reporting. Stakeholders at various levels of the operation can easily consume out-of-the-box reports and visualizations to reduce downtimes, identify production bottlenecks, increase capacity, track your most important KPIs, and enable complete visibility and control of the shop floor.

    Learn more about our solution by booking a demo with our team today.


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