The manufacturing industry consists of production processes working in conjunction with each other to produce products that meet customer demand. Due to how these processes interconnect, finding ways to increase productivity, lower costs, and improve product quality have led manufacturers to seek out automation solutions.
The manufacturing industry consists of production processes working in conjunction with each other to produce products that meet customer demand. Due to how these processes interconnect, finding ways to increase productivity, lower costs, and improve product quality have led manufacturers to seek out automation solutions.
A 2017 McKinsey Global Institute Executive Summary found that 60% of occupations can have nearly 30% of their work activities automated, as the manufacturing sector in the United States is one of the prevalent industries that could potentially recover trillions paid to workers to complete these tasks.
To understand which processes could benefit from these growing technologies, we’ll look at how data analytics provide various methods into improving business outcomes.
Manufacturing analytics refers to the data that is produced from machines, operations, and systems throughout the entire company. When using this data, manufacturers may better manage and improve various key functions, from design planning and production to maintenance and quality assurance. According to the McKinsey report, roughly 60% of processes in the manufacturing sector could potentially be automated, including system, machine, and operational data collection.
With automated manufacturing analytics, real-time data that records events in processes is instantly captured to generate reports that provide actionable insights. Then a manufacturer may use the insights to proactively eliminate errors, minimize waste, optimize equipment functions, predict future operational issues, reduce costs, and perform a myriad of other actions.
There are several technologies that are integral to performing manufacturing analytics methods. These technologies consist of machine learning, Internet of Things (IoT), edge computing, predictive analytics software, and other technologies.
The processes where analytics may be applied to varies based on the manufacturer's specific operations, the equipment they use, the products they manufacture, and the company's short-term as well as long-term goals. The methods are designed to optimize performance, streamline processes, and locate cost savings without impacting manufacturing quality. Some benefits that a manufacturer may find in analytics for manufacturing may include the following.
Supply chains are a fundamental part of manufacturing, from procuring raw materials to shipping products to customers. Major supply chain challenges cause bottlenecks that could create various problems such as predicting customer demand and balancing inventory levels. In 2020, roughly 52% of supply chain professionals claimed hiring/retaining workers was the top issue followed by forecasting (48%), customer demand for lower costs/response times (47% respectively), rising customer service expectations (43%), and supply chain disruptions (39%), according to Statista.
Dealing with product returns may cause supply chain disruptions and dissatisfied customers. Manufacturing analytics targets quality assurance issues to allow manufacturers to maintain quality and please their customers. Several areas where analytics may be applied include:
Some manufacturers also perform field service and support for products that are installed, repaired, and maintained at a customers' location. Knowing when to perform field service and support can be improved by using analytics.
Seeking out continuous improvements on the production floor allows companies to use workers and equipment at the right times and for the right projects without raising costs. Efficiency may be obtained at varying levels by gathering key performance data to create benchmarks and then setting goals to boost efficiency. Ways to use analytics to pursue these endeavors may involve the following:
The technologies and innovations that are becoming available to manufacturers to perform data analysis is vast. In essence, a company can gather copious amounts of big data from practically every area of operations. Yet pursuing data analytics methods means more than collecting as much data as possible from systems and operations. A manufacturer wants to pull the right data at the right time that creates actionable results. Here are some industry-wide innovations bridging the gap between data analytics and the manufacturing sector.
Discrete event simulation involves creating virtual models of the shop floor and characterizes how processes function based off of gathered data. Companies can use these tools to evaluate performance or to test out new production processes before these processes are officially deployed in the actual work environment. Discrete event simulation can be used in various applications including scheduling, resource allocation, scenario analysis, and many others.
One example of this technology would be to determine how a new piece of equipment will function within the assembly line, as scenarios are run to perform risk analysis to determine if the equipment will function as it should and will boost production levels.
Product analytics involves using data analytics to take the attributes of a product and make improvements to its qualities. This technology allows a manufacturer to perform quality checks on products to instantly look for defects and issues that may cause product recalls or hazards. Workers may provide better troubleshooting procedures to quickly identify the root causes of poor product quality and inform the entire production line of their findings so all employees can take the appropriate quality assurance actions.
Product quality analytics may be used on equipment to automatically make adjustments to how it performs functions. When a piece of equipment falls out of calibration due to worn components, the data is captured and transmitted to workers, who may make the required equipment repairs and recalibration to bring the machines back to expected parameters.
Productivity analytics is a type of data analytics innovation used to maintain the productivity of operations. It can be used to prevent unexpected downtimes, identify poor resource management with deployed equipment, or allow workers to realize how equipment is used inefficiently to perform work tasks. Optimization, simulation, and demand planning tools may be used for productivity analytics to enhance productivity that will generate increased revenue.
If equipment must undergo constant changeovers to perform certain tasks or to create new product lines, productivity analytics may be used to perform simulations on how these changeovers may be done. Then workers can use these findings to efficiently use equipment while avoiding redundant or nonproductive tasks.
Throughput signifies the amount of goods that can be produced in a specific amount of time. Knowing the throughput of operations permits a manufacturer to schedule product orders for customer deadlines while making the appropriate quantities. Data analytics helps optimize throughput by identifying problems that are creating bottlenecks which lead to production inefficiency. By having this data, workers may spot where the bottlenecks are occurring and the root causes. Then they may take the appropriate actions to bring throughput levels to desired performance parameters.
A manufacturer strives to use as few processes as allowable to perform the desired production output. Manufacturing yield represents the fraction of inputs that are used to make good quality products. Understanding how every single production parameter impacts manufacturing yield may assist a company in identifying ways to make improvements that will optimize overall productivity. Manufacturing yield optimization tools and resources are designed to improve these areas by suggesting the types of production variables that will have the most impact on manufacturing yield.
Manufacturers use production scheduling to define processes and the sequences used on equipment and materials to make products. Data analytics may be used to enhance production scheduling. Data may be collected from equipment, suppliers, supply chains, inventory, and workers' knowledge of processes to define how schedule tasks are performed. In this manner, equipment and workers are used in the best possible times and ways for efficient processes while eliminating redundancies and waste.
To get started with manufacturing analytics, there are key stages to implement to turn the gathered data into actionable insights. These steps ensure that a manufacturer is not only able to gather the data but that it is the right data to improve business operations.
The process begins by defining the business use cases. Business use cases are basically the objectives that the company wants to pursue, whether it is to eliminate product waste, improve product quality, or ensure products reach customers. Understanding the business use case helps the manufacturer to determine which necessary data will need to be gathered and where it will come from in processes.
Once an objective is chosen, a manufacturer figures out the business use case by performing a sequence of actions to make a desirable result. These sequences may be done several times to gather observable data of every machine, system, and worker output used to reach the desired result. To gather this data, a range of tools, sensors, or systems put into place at every production step is used. The data is collected from connected systems and technology as it is categorized, merged, and filtered so only the right observable data is available for further analysis and reporting.
A manufacturer should keep in mind that there are always signals within the data that give indications of production performance and output, such as product defects, warranty claims, downtimes, or yields. The challenge lies in using the right tools to automate the data collection process so that it does not become lost. There are also advanced analytics applications that allow for deeper dives into the data for simulation and modeling projects.
Having a clear objective and the automated tools in place for data collection helps to streamline the manufacturing analytics to further identify where the data may be found and how to effectively use it to make processes efficient while reducing waste.
Amper makes it easy to get started with manufacturing and operational analytics. Our system pulls data from any type of machine and connects it to the internet in minutes–not months. So you can start accessing your operational data quickly. Reach out to us today to learn more.
.png)
.png)
.png)
