Improving Quality Assurance Across Production Lines

The need for a high and consistent standard of production means that QA testing must be incredibly rigorous. In turn, this results in longer testing phases and sometimes redundant or overlapping tests in the interest of thoroughness. On the downside, this increases costs and could delay the time it takes to get products to market. However, big data can help find areas where tests may be unnecessary or repetitive, and help cut down on both time and money.

Case study- Intel, for instance, used this method, cutting down the number of tests it ran on each chip it produced—which were extensive—and focusing only on those tests that produced actionable and valuable information. The result was a $3 million reduction in manufacturing costs, and significant time saved.

Reducing Production Line Failures

With smart sensors that can accurately relay second-to-second updates on a variety of metrics related to production, factories have a clearer view on their floors than ever before. One of the best uses for this new data is in reducing the need for reactive maintenance, the likelihood of malfunctions and errors, and the improved ability to stay ahead of maintenance schedules.

Dubuat leverages this information to set up preventive and predictive maintenance programs. The former focuses on the expected lifetimes of products and is useful for general repairs while the latter is ideal for dealing with equipment conditions as they change. This can lower maintenance costs and reduce the likelihood of surprise and catastrophic malfunctions.

Improving Manufacturing Flows

Manufacturing companies are producing information from hundreds of sources every second. More importantly than producing it, however, is understanding how that data can be used to optimize your company’s business processes and operations. Even so, it’s not always immediately clear how you can leverage the massive amounts of data being produced from sales, production, supply chains, and more.Converting data from raw numbers into actionable insights. Nevertheless, it’s not about collecting mountains of data and parsing through every data point but creating the right analyses to make the most out of it.

Optimizing Supply Chain Management

A company using big data with their manufacturing processes can understand how their products are reaching their destination, and where costs could be cut and improved. It starts with warehousing information and tracking packages from the source to their final locations. Companies using big data analytics can understand the most efficient paths from factory to the shelf as well as determine the most effective way to package products to ensure they make the journey safely. More importantly, they can highlight changing trends in deliveries to allow companies to determine where to allocate their resources and efforts.

Using data analytics in various departments

Supply Chain Management Production / Factory Operations
Vendor Management Field Quality Engineering Plant Availability
Procurement & Spend Production Quality Plant Performance
Inventory Management DPMO/PPM analysis Plant Maintenance
Supplier Performance SPC Analysis Delivery Adherence
Finance Human Resources Warehouse Management
Revenue per employee Recruitment Inbound analysis
Manufacturing cost per unit Time & Attendance Warehouse analysis
Net Operating Profit Workforce Management Outbound analysis
EBITDA Performance Management Warehouse occupancy