Data analytics systems are an indispensable asset for every industry. Businesses today collect, store, and process more data than ever and apply that to business intelligence processes. While the manufacturing industry has been relatively slow to embrace digitalization compared to the retail or financial services sectors, it has realized the need for digital transformation to stay relevant in a highly competitive market.
A report by Mordor Intelligence states that digital transformation in the manufacturing sector was valued at $263.93 billion in 2020, and it is expected to increase to $767.82 billion by 2026. This indicates that businesses in the manufacturing sector are looking to go digital, and implementing data analytics will be one of the essential steps in this process.
The Role of Data Analytics in the Manufacturing Industry
Data analytics in the manufacturing industry involves the collection and analysis of operations and events data generated by machines and workers to enhance performance and yield, improve the quality of services and products, reduce costs, optimize operations, reduce downtime, and visually represent events.
This analysis can be broad, such as a comparison between procurement costs and revenue, or specific, for example, anomalies in the vibrations of a machine part captured by a sensor.
The level of data capture and analysis depends entirely on the business’s requirements and the solution implemented.
The purpose of data analytics in manufacturing is to have an explanation for every event that occurs. Accurate insights lead to better decisions. Technologies such as big data, predictive analytics, IoT, and artificial intelligence, are part of the equation.
The Benefits of Data Analytics in the Manufacturing Industry
1. Enhance the Supply Chain
Data from every process in the supply chain—from inventory management to transportation—can be collected and analyzed. This gives the business complete insights into every function in the supply chain and helps them find areas for improvement.
Analytics features such as demand forecasting, vendor performance tracking, and delivery time tracking help enhance the supply chain.
2. Increase Uptime
Sensors can monitor equipment and machine health in real time and that information can be sent to an analytics system for immediate insights. This allows the business to stay on top of every device and its performance and take proactive action to prevent breakdowns. In a recent article, Forbes stated that, on average, an automotive manufacturer loses $22,000 every minute their production line is down. Unplanned downtimes can be significantly reduced—if not eliminated—by using real-time and predictive analytics.
If downtime occurs, the analytics system helps identify the root cause. This allows the business put in place preventive measures for the future.
3. Improve Field Operations
A significant challenge in the manufacturing sector is how to draw conclusions from data generated outside the plant, vendors and suppliers, fleet vehicles, inbound material, and products. Using data from an ERP or an equivalent system, a data analytics solution can give the business valuable insights into the inventory movement, supplier performance, transportation, and delivery time.
The business can further improve field operations to enhance the supply chain with these insights.
4. Optimize Day-to-Day processes
Almost every manufacturing process can be improved by applying insights from data.
IoT systems can help track the performance of workers and vehicles; sensors help track machines; and artificial intelligence can automate procurement using forecasting.
This results in optimized operational processes that save time, improve quality, and reduce costs.
Five Use Cases of Data Analytics in the Manufacturing Industry
1. Preventive Maintenance
Businesses can use fault detection systems to flag anomalies and predict breakdowns so teams can take proactive action.
2. Demand Forecasting and Inventory Management
Predictive analytics helps forecast demand so the business can plan procurement and inventory management. This enables them to mitigate both underselling and overselling.
3. Price Optimization
Businesses can manage activities and operations such as procurement and assembly to have lean cycle times using insights generated by a data analytics system. This allows them to improve the earnings-to-cost ratio.
4. Process Automation
Insights from a data analytics system can be used to automate both repetitive and sporadic tasks. Procuring raw materials, for example, can be automated by using a system that generates a purchase order every time the analytics system predicts more supply is needed to meet the demand.
5. Better Risk Management
Breakdowns and downtime are common issues in the manufacturing industry. A business can attempt to minimize these issues but cannot eliminate them. A data analytics system can help enterprises to use data from these issues to identify potential risks and plan for them. For example, the system can analyze previous breakdowns to find patterns and recurring issues in case of a breakdown.
Get Started With the Right Data Analytics Solution
There are many challenges when it comes to data in the manufacturing sector.
- Data is captured from dispersed sources such as machines, operators, vendors, and vehicles.
- Data is stored in silos, making it difficult to use.
- Existing legacy systems are incapable of utilizing the collected data properly.
- The volume of data collected is high, making the entire process of setting up a business intelligence system complex, and so on.
Identifying the right data analytics partner for your manufacturing business is as important as implementing the system. Fortunately, PreludeSys has many experts with deep experience in data analytics. We work closely with each business partner to build a custom data analytics solution suitable to their needs. Our services are comprehensive; we manage everything from the data storage system to the front-end visualization dashboard.
Visit our service offerings page and get a quote for your business here -> Business intelligence for the manufacturing sector.