The effects of the pandemic have hit the manufacturing industry in ways no one could have predicted. During the last 18 months, a new term has come up frequently in the news and in conversation: the supply chain crisis. Manufacturers have been disrupted in almost every facet of their business, and they have been put to the test as to whether they can weather these challenges or not.
Manufacturing businesses that began a digital transformation prior to the current global crisis have been more agile in handling the disruptions. That is because manufacturers using data analytics and cloud technology can be flexible in adopting the capabilities they need for important business goals, be able to identify inefficiencies more quickly and be equipped to adopt a hybrid workforce to make sure production doesn’t stall.
The pandemic has identified and accelerated the need for manufacturers to digitize and harness the power of modern technology. Real-time data and analytics are fundamental to the manufacturing industry because they create the contextual awareness that is crucial for optimizing products and processes. This is especially important during the supply chain crisis, but this goes beyond the scope of the pandemic. Manufacturers will want to, despite the external circumstances, automate for quicker and smarter decisions in order to remain competitive and have a positive impact on the bottom line.
In this article, we’ll identify the use cases and benefits of manufacturing analytics, which can be applied in any situation at any time.
What is Manufacturing Analytics?
Manufacturing analytics is used to capture, process, and analyze machine, operational, and system data in order to manage and optimize production. It is used in critical functions – such as planning, quality, and maintenance – because it has the ability to predict future use, avoid failures, forecast maintenance requirements, and identify other areas for improvement.
To improve efficiency and remain competitive in today’s market, manufacturing companies need to undergo a digital transformation to change the way their data is collected. Traditionally, manufacturers capture data in a fragmented manner: their staff manually check and record factors, fill forms, and note operation and maintenance histories for machines on the floor. These practices are susceptible to human error, and as a result, risk being highly inaccurate. Moreover, these manual processes are extremely time-consuming and open to biases.
Manufacturing analytics solves these common issues. It collects data from connected devices, which reduces the need for manual data collection and, thereby, cuts down the labor associated with traditional documentation tasks. Additionally, its computational power removes the potential errors and biases that traditional methods are prone to.
Because manufacturing equipment collects massive volumes of data via sensors and edge devices, the most efficient and effective way to process this data is to feed the data to a cloud-based manufacturing analytics platform. Without the power of cloud computing, manufacturers are generating huge amounts of data, but losing out on potential intelligence they have gathered.
Cloud-based services provide a significant opportunity for manufacturers to maximize their data collection. The cloud provides manufacturers access to more affordable computational power and more advanced analytics. This enables manufacturing organizations to gather information from multiple sources, utilize machine learning models, and ultimately discover new methods to optimize their processes from beginning to end.
Additionally, manufacturing analytics uses advanced models and algorithms to generate insights that are near-real-time and much more actionable. Manufacturing analytics powered by automated machine data collection unlocks powerful use cases for manufacturers that range from monitoring and diagnosis to predictive maintenance and process automation.
Use Cases for Cloud-Based Manufacturing Analytics
The ultimate goal of cloud-based analytics is to transition from having descriptive to predictive practices. Rather than just simply collecting data, manufacturers want to be able to leverage their data in near-real-time to get ahead of issues with equipment and processes and to reduce costs. Below are some business use cases for automated manufacturing analytics and how they help enterprises achieve predictive power:
Demand Forecasting and Inventory Management
Manufacturers need to have complete control of their supply chain in order to better manage inventory. However, demand planning is complex. Manufacturing analytics makes this process simpler by providing near-real-time floor data to support supply chain control, which leads to improved purchase management, inventory control, and transportation. The data provides insight into the time and costs needed to build parts and run a given job, which gives manufacturers the power to more accurately estimate their needs for material to improve planning.
Managing Supply Chains
For end-to-end visibility in the supply chain, data can be captured from materials in transit and sent straight from external vendor equipment to the manufacturing analytics platform. Manufacturers can then manage their supply chains from a central hub of data collection that organizes and distributes the data to all stakeholders. This enables manufacturing companies to direct and redirect resources to speed up or down.
In order to optimize pricing strategies and create accurate cost models, manufacturers need exact timelines and costs. Having an advanced manufacturing analytics platform can help manufacturers determine accurate cycle times to ensure prices are appropriately set.
To remain competitive, manufacturing organizations must invest in research and development (R&D) to build new product lines, improve existing models, and introduce new services. Manufacturing analytics makes it possible for this process to be simulated, rather than using traditional iterative modeling. This reduces R&D costs greatly because real-life conditions can be replicated virtually to predict performance.
Manufacturers are relying more on robotics. As these robots become more intelligent and independent, the data they collect while they execute their duties will increase. This valuable data can be used within a cloud-based manufacturing analytics platform to really control quality at the micro-level.
Computer Vision Applications
Modern automated quality control harnesses advanced optical devices. These devices can collect information via temperature, optics, and other advanced vision applications (like thermal detection) to precisely control stops.
Fault Prediction and Preventative Maintenance
Using near-real-time data, manufacturers can predict the likelihood of a breakdown – and when it may happen – with confidence. This is much more effective than traditional preventive maintenance programs that are use-based or time-based. Manufacturing analytics’s accuracy to predict when and how a machine will break down allows technicians to perform optimal repairs that reduce overall downtime and increase productivity.
It’s important to analyze information from failed products to understand how products are withstanding the test of time. With manufacturing analytics, products can be improved or changed to reduce failure and therefore costs. Collecting warranty data can also shed light on the use (and misuse) of products, increase product safety, improve repair procedures, reduce repair times, and improve warranty service.
Benefits of Manufacturing Analytics
In short, cloud-based manufacturing analytics provides awareness and learnings on a near-real-time basis. For manufacturers to be competitive, contextual awareness is crucial for optimizing product development, quality, and costs. Production equipment generates huge volumes of data, and manufacturing analytics allows manufacturers to leverage this data stream to improve productivity and profitability. Here are the tangible benefits and results of implementing manufacturing analytics:
Full Transparency and Understanding of the Supply Chain
In today’s environment, owning the supply chain has never been more critical. Data analytics can help mitigate the challenges that have cropped up with the current supply chain crisis. For manufacturing businesses, this means having the right number of resources. Data analytics allows manufacturers to remain as lean as possible, which is especially important in today’s global climate. Organizations need to use data analytics to ensure they have the right amount of material and optimize their supply chains during a time when resources are scarce and things are uncertain.
Manufacturing analytics reveals insights that can be used to optimize processes, which leads to cost savings. Predictive maintenance programs decrease downtime and manage parts inventories more intelligently, limiting costs and increasing productivity. Robotics and machine learning reduce labor and the associated costs.
Manufacturers must be dynamic in responding to demand fluctuations. Near-real-time manufacturing analytics allows companies to be responsive to ever-changing demands. At any given time, manufacturing companies have up-to-date insights into inventory, product, and supply chains, allowing them to adjust to demand accordingly in order to maintain delivery times.
Improved Efficiency Across the Board
The amount of information that product equipment collects enables manufacturers to increase efficiency in a variety of ways. This includes reducing energy consumption, mitigating compliance errors, and controlling the supply chain.
Greater Customer Satisfaction
At the end of the day, it is important to know what customers want. Data analytics is a crucial tool in collecting data from customer feedback, which can be applied to streamlining the process per the customer’s requirements. Manufacturers can analyze the data collected to determine how to personalize services for their consumers, thereby, increasing customer satisfaction.
The effects of COVID-19 have shaken up the manufacturing industry. Because of the pandemic’s disruptions, manufacturers are realizing the importance of robust tools – like cloud computing and data analytics – to remain agile, lean, and flexible regardless of external challenges. The benefits that organizations can reap from these technologies go far beyond the horizon of the current supply chain crisis. Leading manufacturers are using data from systems across the organization to increase efficiency, drive innovation, and improve overall performance in any environment.
2nd Watch’s experience managing and optimizing data means we understand industry-specific data and systems. Our manufacturing data analytics solutions and consultants can assist you in building and implementing a strategy that will help your organization modernize, innovate, and outperform the competition. Learn more about our manufacturing solutions and how we can help you gain deep insight into your manufacturing data!