Big data is the lifeblood of manufacturing. It’s big data that can reveal the glitches in a company’s business operations, and it’s big data that when analyzed opens a window of opportunity for manufacturers to identify and fix problems before they get worse.
As manufacturers approach their big data projects, they’ll want to consider an enterprise wide data management plan that applies analytics tools to the numerous business segments of their operations. A recent study by Accenture highlights the far-reaching benefits of applying analytics to day-to-day supply chain operations.
The study, which relied on interviews with 1,000 senior executives at large global companies, found that organizations that applied big data analytics using an enterprise-wide strategy were far more likely to generate a range of important supply chain benefits versus those companies that implemented a process-focused strategy.
According to the survey, those adopting an enterprise-wide strategy realized shortened order-to-delivery cycle times of 63 percent versus 12 percent for those adopting a process-focused strategy. Other results were: improvement in demand-driven operations (58 percent versus 15 percent), better customer and supplier relationships (52 percent versus 19 percent), more effective sales and operations planning and decision making (51 percent versus 13 percent), and faster and more effective reaction time to supply chain issues (47 percent versus 18 percent).
If you’re a manufacturer dealing with information overload, you’ll have to consider targeting each segment of your business that generates critical data. Once you’ve identified those areas of your business that need improvement, you’ll need to apply analytics tools to identify the problem and find a solution.
To do this, you’ll need to create end-to-end visibility that offers a single version of the truth to your network participants. Only then can you drive business efficiency into your supply chain and cut costs across your supply network.
An effective big data solution for manufacturers, therefore, will create visibility and transparency across multiple areas of your business operations. These areas include:
- The procurement and tracking of component parts
- warehouse arrivals, shipments and inventory levels
- customer orders
- product quality and defect tracking
- Testing and simulation of new manufacturing processes
- Output forecasting
As you think through the best way to use big data to help you gain insights into your manufacturing operations, here are 3 key benefits that you should consider as you develop your big data strategy:
1. Cloud computing offers visibility and cost savings
Cloud computing is a business model that addresses many of the challenges manufacturers face as they sort and manage their data. For a start, manufacturers that outsource their data management operations to a cloud service provider experience the benefits that come with low up-front and maintenance costs, on-demand scalability and shared data from different segments of their supply chain. Cloud computing raises the level of collaboration among supply chain partners, and gives manufacturers real-time access to critical data.
2. Analytics tools can change a manufacturers business.
For example, a biopharmaceutical manufacturer applied advanced analytics to significantly increase its yield in vaccine production at no additional cost. In this example, documented by McKinsey & Company, a project team segmented its entire process into clusters of closely related production activities, and for each cluster it collected and entered data into a central database that tracked process steps and materials used. Through statistical analysis, the data revealed interdependencies among the different process parameters and their impact on yield. Nine parameters proved to be most influential, especially relating to when to inoculate cells and conductivity measures associated with one of the chromatography steps. Based on this, the manufacturer made targeted process changes that increased its vaccine yield by more than 50 percent—worth between $5 million and $10 million annually.
3. Through big data analytics manufacturers can improve customer satisfaction.
Car manufacturer BMW Group uses big data to analyze information generated from dealerships and manufacturing outlets around the world. BMW’s prototype cars generate approximately 15,000 data points from the engine and transmission to the suspension and brakes. Before new cars go into full production, BMW test its prototype cars and uses big data analytics to find faults that are fixed. The result – BMW Group manufactures high quality cars that are safer and require less time and less money for repairs.
With so many manufacturers working toward the goal of getting the right quantity of products to the right market at the right time and at the right price, manufacturers have no choice but to consider harnessing big data to gain insights and make decisions that achieve tangible results.
In a very competitive environment, it’s good to know that manufacturers can turn to big data to improve their business operations, raise the quality of their products and bolster their bottom line.