What Is Manufacturing Analytics?

Michael Hickins | Content Strategist | Nov 3, 2023

Manufacturers of every stripe—as varied as aluminum and steel producers and makers of electronic components, aircraft engines, and chemicals—use data analytics to help their factories run more smoothly, track supplier performance, increase the rate of perfect orders, identify supply chain bottlenecks, improve employee productivity, reduce product recalls, and ultimately cut costs and boost profits.

What Is Manufacturing Analytics?

Manufacturers use data analytics to reduce unscheduled downtime, track key performance indicators, and improve factory efficiency and customer satisfaction. The broader trend is called Industry 4.0 or smart manufacturing. This involves aggregating data collected from conventional IT systems as well as industrial equipment and running analytics applications to make more informed decisions. Analytics also helps manufacturers identify the root causes of production errors and predict bottlenecks across manufacturing and supply chain processes that could disrupt order fulfillment.

Key Takeaways

  • Manufacturers help keep their plant equipment running during production runs by analyzing sensor data to recognize when such equipment will likely fail.
  • Manufacturers considering a transition to more service-oriented business models use analytics to identify revenue streams that production inefficiencies directly affect.
  • Analytics helps manufacturers continuously monitor their supply chains, giving them visibility into the movement of raw materials or parts in transit from suppliers as well as materials located at various plants.
  • Manufacturers use analytics to reduce the number and scope of product recalls by identifying specific machines or production lines where quality issues occurred. This lets manufacturers recall only specific product batches rather than entire shipments.
  • Manufacturers use analytics to stay on track of crucial key performance indicators to ensure they reach their perfect order targets.

Manufacturing Analytics Explained

Most manufacturers use sensors to collect data from their plant and equipment, known as operational data, and from the IT systems that run applications to manage their manufacturing, financial, supply chain, and HR processes. Manufacturing analytics helps business leaders make decisions based on that amalgamated data.

For example, analytics systems let business leaders track key performance indicators (KPIs) to identify which suppliers consistently deliver on schedule, identify supply chain bottlenecks, and limit the scope of product recalls. Analytics systems also interpret inventory and work order data from the ERP system and data generated by machines on the factory floor, and alert managers of the potential to miss a key delivery window because of insufficient output or machine downtime. This type of analytics helps manufacturers improve their perfect order rate—a KPI that reflects a company’s ability to deliver the right number of goods, without loss or damage, in the correct packaging, and with invoices that accurately reflect stipulated pricing and the number of goods delivered.

How Does Manufacturing Analytics Work?

At most manufacturers, sensors connected to key pieces of equipment send constant streams of data, typically stored in a data warehouse, about every imaginable type of parameter—examples include the temperature at which the motor is running and the level of vibrations emitted by ball bearings—all of which can indicate a potential problem that must be addressed before the equipment fails and takes down a production line.

More sophisticated factories combine operational data with related IT to alert production units about a possible disruption and business leaders that a particular work order or production associated with that equipment is threatened. This type of analytics can also include inventory. Managers use applications to visualize where to find inventory—in different warehouses or in transit from a supplier—and apply analytics to make better, faster decisions about dealing with a potential inventory shortfall that could stop a production run if it isn’t addressed quickly.

Benefits of Manufacturing Analytics

Manufacturing analytics provides substantial benefits, the most important of which are outlined below.

  • Prevent unscheduled downtime. Manufacturers use analytics to interpret sensor data that can indicate a piece of equipment likely will fail soon. For example, sensors can detect that the ball bearings in a gear shaft are vibrating at an unusual frequency, indicating they will soon seize. Using that data, manufacturers can do preventative maintenance to keep the machine and production line running on schedule.
  • Improve productivity. Using analytics, manufacturers can increase the productivity of their equipment and employees and boost profit margins by up to 10%, according to McKinsey. The consultancy cited the example of a global chemicals company that cut costs by several million euros per year, partly by reducing its dependence on third-party suppliers for certain product lines and identifying opportunities to expand capacity by increasing the throughput of some crucial production assets. The company also increased sales by raising the production capacity for other product categories. The manufacturer used an analytics model that crunched over 500 variables, more than 3,000 constraints, and hundreds of production steps.
  • Support new business models. Many manufacturers are experimenting with new business models predicated on delivering services and simply selling finished products, known in some circles as product as a service. Examples include airplane engine manufacturers charging airlines fees based on the number of hours an engine flies without needing repairs, and a medical equipment manufacturer charging hospitals on a usage basis, guaranteeing equipment uptime in exchange for ongoing service fees. Analytics makes these services possible as manufacturers analyze data collected from their systems to identify when preventive maintenance is required. In addition to letting manufacturers build a differentiated recurring revenue stream, the data they collect and analyze helps them improve future products, and the model helps them build longer-term customer relationships.
  • Optimize costs. Manufacturers can better understand their overall costs, including labor, materials, overhead, and anomalous expenses, such as ordering too much safety stock for a raw material that leads to excess carrying costs. Such use of analytics can lead to improved margins.
  • Stay on top of key performance indicators (KPIs). Business leaders use analytics to help them flag potential problems that could affect key aspects of the business, both in their plants and across their supply chains. No single KPI can indicate how a plant or manufacturing company performs. Moreover, some KPIs, such as on-time delivery, reflect not just the performance of one plant but of an entire supply chain. Leading manufacturers use analytics to help managers understand the issues underlying each of these KPIs as well as how they relate with one another.

    The most common KPIs include:
    • Perfect order rate, which, as discussed earlier, is a composite of various KPIs that reflect how a manufacturer delivers finished goods without errors, including shipping the right number of goods, packaging them correctly, and ensuring they’re accompanied by documentation that matches the actual quantity shipped and invoiced according to stipulated prices.
    • Yield, which measures the efficiency with which goods are produced, by calculating the number of units that are produced to standard specifications as a percentage of the total number of units produced.
    • Overall equipment effectiveness (OEE), which measures the percentage of time a plant is productive, taking into account product quality, equipment availability, and performance. By analyzing OEE at any given time, manufacturers can predict potential equipment failures and plan maintenance accordingly.
    • On-time delivery, which measures the percentage of units delivered within a specific time frame as promised to the customer. This analysis helps understand potential delays in order fulfillment and pinpoint their exact cause, whether they’re related to supplier delivery issues or order management bottlenecks.
    • Throughput, which calculates the efficiency of a given plant or manufacturer, based on the total number of goods produced over a given timeframe. With continuous monitoring of this type of data, manufacturers can identify potential equipment inefficiencies, manage resource backlogs, and adjust production plans to meet their targets.
    • Cycle time, which is a way of calculating the capability of a manufacturer’s facilities to meet demand, measured by the amount of goods a plant produces from the moment a customer places an order to when the customer receives the goods.
    • Production volume, which measures the total number of units produced over a given timeframe.
    • Capacity utilization, which measures how well a manufacturer matches capacity to demand, calculated by dividing total capacity used in a given timeframe by total available production capacity, multiplied by 100 to get a percentage.
    • Scrap rate, which measures the amount of material that must be scrapped after a job ends. The lower the rate, the better.
  • Track supplier performance. Manufacturers use analytics to identify suppliers that consistently deliver parts or raw materials on time. They also use it to monitor the quality of suppliers’ products, their prices relative to competitors, and the extent to which they’re complying with labor and environmental standards.
  • Gain supply chain visibility. Manufacturers use analytics to produce reports about their inventory levels of raw materials or parts. They can visualize which parts are still in transit and where among their various plants they have inventory that can be moved around to backfill a shortfall in another location. This is especially important for large manufacturers with thousands of suppliers filling hundreds of orders at a time.
  • Prioritize work orders. Analytics makes it easier for manufacturing teams to determine which projects and production runs to prioritize—based on factors such as when a product has been promised, whether there are current supply chain disruptions, and whether the teams have on hand the specific inventory needed for each order. Analytics lets supervisors compare work orders, sales orders, and inventory on hand, and it lets production superintendents see how various production runs fit into an overall manufacturing plan. For example, a plant manager might decide to run a more recent work order for a premium or high-volume customer that needs to be filled quickly and to deprioritize an earlier order from a less steady customer that won’t take as long to fill.
  • Improve employee productivity. Analytics can help reduce unscheduled downtime, as stated above, so that production workers are rarely idle. But it can also help staff schedule maintenance activities for times when equipment isn’t in use, which can be challenging to do manually when several work orders are in progress across more than a single facility. This, in turn, helps ensure that maintenance crews aren’t standing around waiting to service machines—a not uncommon occurrence. Indeed, maintenance employees spend only about a quarter of their time doing productive work, according to estimates. These same types of analytics can be used to adjust other processes, such as shift start and end times to coincide with materials delivery windows or other external factors.
  • Limit the scope of product recalls. Analytics uses detailed reporting from individual pieces of equipment, including real-time production data and quality control reports, to help manufacturers identify exactly when a quality problem arose, on which production line, and at which piece of equipment. That helps limit the scope of product recalls, lowering costs and increasing customer satisfaction.
  • Get more detailed data. Manufacturers manage their operations using KPIs with data that’s generally at the plant level. That data can also be tied to individual production lines and even machines, letting manufacturers improve throughput, cycle times, and other KPIs at a granular level.
  • Reduce employee attrition. Analytics can help manufacturers identify and rectify safety hazards, difficult working conditions, overly long work shifts, and underutilized employees, thereby helping improve morale, safety, and tenure. Manufacturers also use analytics to help identify employees with skills other than ones they use for a given position, letting them reassign employees to different areas of the business and advance their careers.
  • Produce consistent financial data. Companies still using spreadsheets and other manual, disconnected means of managing financial data often end up with data that’s inconsistent. This can be the result of reporting errors or because managers try to put the best possible face on a given situation gone awry. Analytics applied to data extracted from both financial applications and equipment on the factory floor can produce automated and accurate reports free of human error or manipulation.

9 Best Practices for Manufacturing Analytics

Successful analytics projects share several key characteristics, outlined in the best practices below.

1. Make it a business project

Involve business stakeholders, all the way up to the C-suite, in developing analytics projects. Ensure that the projects yield early, meaningful results (see KPIs section) so they’re not seen as just another bunch of IT projects. For example, demonstrate that combining IT and operational data can help analyze connected metrics, such as the impact of on-time delivery on customer satisfaction or the impact of machine downtime on the perfect order rate.

2. Start small

To prove the value of analytics, start with data collected from a small number of machines, ones that are bottlenecks or are particularly crucial to a production line, rather than trying to create an enterprise-scale project. This approach is less expensive than a big bang one, is more likely to show immediate results, and often leads to greater demand for more wide-scale analytics projects.

3. Inventory your data

Engage in a full-scale discovery of the different data types available from different systems used by various departments. This assessment should include applications used by acquired companies; accounts payable, payroll, and other back-office applications added over time; and even that one-off application that a developer created for someone a decade ago and is still running on a server under someone’s desk.

4. Include operational data

Include data collected from factory equipment or other operations alongside data collected in applications that manage manufacturing processes to get the most accurate analysis. For example, analyzing work order data from an ERP application with operational data about a production line’s cycle time can indicate whether a given order will be filled on time, a finding that directly affects customer satisfaction and revenues.

5. Create a single data repository

Aggregate data from different data warehouses into a single, cloud-based data warehouse or data lake. This is especially crucial after an acquisition because different companies often use different data management systems that don’t integrate well with one another.

6. Measure what needs to be managed

Scope analytics projects so that the appropriate types of data are collected and analyzed. If one goal of the project is to reduce downtime, make sure sensor data is being collected for the equipment that needs to be maintained in working order. If a goal is to improve throughput, ensure you can record volume and collect time series data so you can measure how much is being produced in a given timeframe.

7. Embrace AI and machine learning (ML)

By leveraging no-code ML within analytics, anyone in your manufacturing organization can uncover hidden patterns based on historical data, such as identifying backlog trends in inventory, predicting machine downtime, analyzing resource underutilization, and correlating the impact of production shortfalls to key business metrics, such as revenue and margins.

8. Expand analytics capabilities progressively

Identify key areas where data isn’t being collected and add sensors or other capabilities to let that occur. Expand both the scope and complexity of analytics projects accordingly. For example, manufacturers can start by measuring the quantity of units produced and the percentage of time that equipment is operating at full capacity, subsequently adding quality measures, such as the number of units accepted as a percentage of total units produced.

9. Adjust the manufacturing plan

Manufacturers can use analytics-driven insights from data amalgamated from integrated inventory as well as fulfilment, customer experience, sales, production, and third-party sources to make quick decisions and adjust production plans as needed.

Business Use Cases for Manufacturing Analytics

Manufacturers use data analytics to improve the overall efficiency of their floor operations and supply chains and to gain better insights into KPIs, such as overall equipment efficiency, equipment uptime, and yield throughput. Consider the following examples.

  • HarbisonWalker International. Large multinational manufacturers can use analytics to improve forecast accuracy and on-time order delivery. For example, HarbisonWalker International, a company more than 150 years old that makes refractory products (products that can withstand high degrees of heat, pressure, or chemical attacks), has dozens of facilities scattered across three continents. The combination of acquisitions and myriad applications patched together during the last 20 years made data collection and analysis difficult. By consolidating data and applications onto a single cloud ERP system, HarbisonWalker has analyzed manufacturing and financial data enterprisewide to improve forecast accuracy, reduce worker overtime, fine-tune inventory levels, and improve on-time delivery to more than 90%.
  • Western Digital. Analytics helps large businesses make data-driven decisions more quickly. For example, data reporting workflows at data storage company Western Digital were slowed by several factors, including its acquisitions of Hitachi Global Storage Technologies and SanDisk, which each used different data and workflow platforms. The three companies combined had more than 2,000 applications to manage, and it took more than eight hours for IT to refresh the data warehouse. This setup left business users without access to business intelligence and analytics during the workday, and when reports became available, the data was 24 to 48 hours old. By standardizing data and workflows on a new cloud-based system with preconfigured reporting, Western Digital gave its business leaders access to analytical data within about 20 minutes. In addition, the consolidation of data and platforms has let the company streamline workflows and ensure that all managers and executives work from the same data sets and reports.
  • Bitron. Manufacturers use analytics to reduce the time that executives spend searching for data, making it easier for them to make decisions on an empirical rather than gut-level basis. Bitron, an Italian manufacturer of a range of mechanical and electronic components for a variety of industries, including energy, automotive, and HVAC, uses cloud technology to eliminate data silos. Self-service analytics tools let managers create the reports they need. Typically, users need to export data from various sources and run analytics separately using point analytics tools, which leads to flawed insights. However, using Oracle Analytics Cloud, which includes data preparation and enrichment capabilities, users can more easily aggregate data and produce KPIs to help them manage manufacturing processes.
  • Bonnell Aluminum. Analytics gives manufacturers more visibility into their supply chains and operations, letting them better meet customer demands. Bonnell Aluminum, a manufacturer of custom fabricated and finished aluminum extrusions, tried to use data from HR, finance, and plant operations systems, including data from five manufacturing plants that was in noninteroperable data warehouses. Plant managers combined onsite data from spreadsheets with a homegrown ERP reporting system, which led to inconsistent data and poorly founded decisions. A lack of reliable data made it impossible to identify or correlate material shortages worldwide, identify underperforming suppliers, and prioritize customer orders. This lack of clarity became untenable with 80% of Bonnell’s business in custom manufacturing, which requires it to deliver goods made according to given specifications at a specific time. Thanks to a new cloud-based ERP and analytics platform, the company has made better purchasing and inventory decisions. By connecting data across the enterprise, including data from suppliers, Bonnell can better understand which products are in highest demand, identify process bottlenecks (such as supplier delays and related inventory issues), and make necessary changes (such as reallocating labor and spending) to meet those custom demands.

How to Implement Data Analytics in Manufacturing

Most manufacturing companies use data analytics, but in many cases they have yet to implement a comprehensive strategy. That includes aggregating and cleansing data consistently, running analytical queries against that data, and systematizing responses to alerts or other information revealed by the data. Manufacturers should consider the following 10 implementation best practices.

  1. Create an inventory of the current state of your data repositories and document what you want the end state to be, including the metrics you would like to see produced (for the sake of preventive maintenance, quality improvement, worker safety, and so on).
  2. Inventory your data types. This includes unstructured data gathered from machines, devices, assets in transit, and other sources, as well as from manufacturing, finance, supply chain, sales, marketing, HR, and other applications, plus structured data organized in data warehouses or data lakes.
  3. Begin a data migration process, first by consolidating data into a single data warehouse or other repository, backed up to ensure business continuity. In addition to being a crucial first step in the analytics process, rationalizing the data in this manner also helps reduce storage costs, which is a good initial win.
  4. Build connectors or data feeds from disparate data sources into the central repository.
  5. Use data cleansing software to remove duplicate, contradictory, or otherwise inaccurate data collected from different systems, ensuring that the centralized data is clean and reliable.
  6. Start small, as mentioned earlier. Initially target one piece of production equipment identified as a bottleneck so that teams can apply analytics for the purpose of preventive maintenance and reducing downtime. Or identify a set of KPIs (cycle times, throughput, worker safety, etc.) to track and improve using analytics.
  7. Move the analytics to more crucial production lines or supply chain processes.
  8. Let business users create their own reports and dashboards at the intervals they choose, to reduce their reliance on the IT department.
  9. Configure reports so that they’re visually oriented (versus a tabular format), making it easier for people to make decisions based on data anomalies or other flags.
  10. Whenever possible, use prebuilt reports that are part of the analytics software package, yielding industry standard KPIs that help you benchmark your operations against those of competitors.
How to Implement Data Analytics in Manufacturing Image
Setting up a manufacturing analytics program is an iterative process that involves starting with a small project and slowly expanding the scope.

The Future of Manufacturing Analytics

While most manufacturers already use information technology and, to some degree, telematics or other instrumentation on their equipment, their use of IT and analytics in particular tends to be uneven. That’s because data resides in different silos, making it difficult to access and analyze.

Standardizing on cloud-based IT systems will help manufacturers consolidate all this data—both structured and unstructured data—letting them use analytics in a concerted, consistent manner to gain accurate and trusted insights to improve decision-making.

Finally, the introduction of low-code and no-code ML embedded within analytics will let business users create reports on their own, without needing to fill out a request ticket or otherwise get help from IT. This will lead to more frequent use of data and all the resulting benefits.

Future-Proof Your Manufacturing Processes with Oracle

Oracle Cloud Supply Chain & Manufacturing, part of Oracle Fusion Cloud ERP, helps manufacturers respond quickly to changing demand, supply, and market conditions. Manufacturers using this application suite can continuously monitor inventory patterns to mitigate the risks of work-order backlogs, determine if supplier performance could affect production goals, and much more.

Oracle Fusion Supply Chain & Manufacturing Analytics enables manufacturers to increase productivity with prebuilt insights, improve shop floor efficiency by quickly detecting anomalies, and optimize plan-to-produce processes with an integrated view of supply chain and manufacturing data.

Manufacturing Analytics FAQs

How does analytics help manufacturers?
Manufacturers use analytics for a variety of purposes, including to reduce unplanned downtime, track and improve supplier performance, prioritize work orders, boost employee productivity, and reduce product defects.

Which kinds of physical events can sensors detect?
Sensors can detect the presence of flames, gas leaks, and oil levels, and they can sense physical properties such as temperature, pressure, and radiation. They can also detect motion and proximity of objects to one another.

Where do manufacturers get the data they analyze?
Manufacturers correlate data from a variety of sources, including factory floor machines, back-office IT applications, suppliers, and third-party providers of data focused on markets; demographics; weather; regulations; patents; environmental, social, and governance practices; and other information categories.

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