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We are living and working in a new frontier known as “The Digital Universe.” From emails, texts, and videos to the billions of sensors embedded in everything from shipping containers to shoes, our increasingly digital lives are generating nearly inconceivable amounts of data. And yet, this is just the beginning.

In a recent study, IDC forecasts the digital universe is doubling in size every two years, growing to 44 trillion gigabytes by 2020. Interestingly, while the amount of information is growing by leaps and bounds, a surprisingly small percentage is actually being extracted, analyzed, and compiled by businesses into something actionable. As plant managers and other executives struggle to understand how to operate in this new environment, one thing is clear: manufacturing organizations must have the right tools, technologies, and knowledge in place if they are to extract nuggets of meaningful information from this data-intensive world.

Data must be meaningful or it is irrelevant. Last year less than 5% of what businesses might consider useful data was analyzed. This is because many organizations lack the information technology solutions necessary to process in real-time the high volume and wide variety of information generated from the Internet of Things and internal business data.

Of course, even a small percentage of insightful data has been enough to fuel meaningful change within industrial manufacturing. In fact, manufacturers that can extract value from the digital universe are developing exciting new operational processes, which are leading to greater efficiencies, new revenue streams, and improved profitability.

Real data-driven applications for manufacturing facilities

Much of the buzz about Big Data is focused on connected equipment: machines transmitting environmental or performance data to other systems that alert operators to take action such as replace a fan or change the oil. Yet, companies that are able to use data for decisions making are finding tangible benefits throughout their entire manufacturing operations.

Here are 10 real-life examples of how newly available information is being used to change the business of industrial manufacturing.

  1. Predictive maintenance: Using data to predict when a product will need maintenance and then taking proactive action to  over  downtime is the most commonly mentioned application. The benefits of predictive maintenance can include fewer costly, unplanned outages; reduced repair and maintenance costs; extended longevity of machinery; and improved productivity.
  2. More clearly defined customer requirements: Companies that combine customer and social data and analyze it for positive and negative trends, find it is possible to create more accurate customer requirements from the beginning, thereby reducing product development time and increasing the quality of service.
  3. Matching demand for parts with supply in real time: When maintenance is needed, data analytics can help ensure technicians have access to the right part at the right location. Using data for improved inventory visibility helps reduce planning costs across a global network and allows for faster and more precise execution.
  4. Scenario analysis: Visionary organizations are mitigating and managing uncertainty, complexity, and risk within their sales and operations planning process by using data to create and assess possible scenarios.
  5. Inventory planning: Data are being analyzed and used to determine inventory targets and safety stocks according to historical needs and required service levels across the global multi-echelon supply chain.
  6. Real-time MRP run: Reduce frozen horizons by using data to run materials requirements planning multiple times a day and review the effects of changing demand and reducing out-of-stock situations; decreasing inventory and safety stocks; and limiting the bullwhip effect.
  7. Adaptive inbound logistics based on intelligent objects: Integrating data across manufacturing systems allows companies to automate movements of material and make them more transparent, faster, and less prone to error.
  8. Transport process analysis and optimization through simulation runs: Running data-based simulations of a manufacturer’s transport processes helps identify areas for reducing costs and supports the creation of sustainability processes such as CO2 reporting.
  9. Geo-located shipping: Sensors embedded in shipping containers transmit geo-location data that increases supply chain visibility, allowing manufacturers to prepare or even to react upfront if disruptions occur. This can be especially important when there are long distances between the source and destination.
  10. Global supply chain visibility and alerting: Industrial manufacturers are using data to manage the end-to-end supply chain and react to changes in real time, thereby increasing efficiencies and reducing costs.

While these 10 examples illustrate how information is improving the industrial manufacturing process today, it is exciting to realize that companies are just scratching the surface on the possible benefits afforded by an increasingly data-intensive business environment. With the right technology solutions in place, the opportunities are limited only by one’s ability to take the available information and put it in context to refine existing manufacturing processes.

David Parrish is senior global marketing director of industrial machinery & components for SAP