There is no escaping the big data revolution that is sweeping across all sectors of industry. Companies that embrace this revolution are on the road to achieving greater business efficiencies and higher profitability.
Any organization with the ability to assimilate data to provide crucial insights into their operations can benefit. Sectors like financial services and healthcare have already embraced big data analytics to remarkable effect.
Now, manufacturing is getting up-to-speed as companies recognize the value in the vast amounts of data that they create and hold. Manufacturers across a range of industries now have the capability to take previously isolated data sets, aggregate and analyze them to reveal important insights.
However, what many of them lack is a clear understanding of how to use the new technology, or even which big data analytics tools they need to apply to their huge volumes of real-time shop-floor data.
For project managers with big data skills and knowledge, this offers an opportunity to gain a competitive edge in the manufacturing sector.
Over the past couple of decades, manufacturers have made progress in tackling some of their sector’s biggest challenges, including waste and variability in production processes. By implementing Lean processes and programs, many have achieved significant improvements in product quality and output.
Nevertheless, in some processing environments, pharmaceuticals and biochemistry for example, Lean methods have not been as effective in curbing processing variability swings, largely because the production activities that influence output in these industries tend to be complex and numerous.
In biopharmaceutical production, it is not unusual for companies to be monitoring more than 150 variables to ensure the purity and compliance of their product. This has created a need for a more granular approach to identifying and resolving errors in these and other industry production processes. And, that’s where data analytics can make a difference.
In manufacturing, planning and delivery is often a heavily documented area. It is also an area where big data is shaping project management. The application of data analytics can produce insights that can help to redefine manufacturing planning processes and parameters.
A second area where project managers have already deployed big data technology is in the analysis of quality management data.
Because producing consistently high-quality products is key to remaining competitive, many manufacturers are now looking to big data as a way of improving their quality assurance.
One example of where this has been done successfully is computer chip manufacturer Intel, which uses predictive analytics to deliver quality assurance on its products. Prior to the development of big data technology, the firm would subject every chip to a battery of tests to ensure that it reached the quality standard.
Using big data for predictive analytics, historical data collected during the manufacturing process was analyzed, enabling the company to reduce test time. Instead of running every single chip through thousands of tests, Intel was able to focus tests on specific chips, bolstering its operational efficiencies and its bottom line.
In this fairly typical manufacturing scenario, a project manager can play a strategic role in bringing quality management and compliance systems out of their traditional silos and helping organisations find better ways of operating.
Speeding up the production process is key to driving profitability in manufacturing. But doing ramping production without sacrificing quality can be a challenge, particularly in manufacturing sectors such as pharmaceuticals, where multiple factors play a role in the manufacturing process.
Improving accuracy during the production process while increasing output is another task for the project manager with access to big data analytics systems and skills, which can be used to effectively segment their production and identify the fastest stages of the process.
With this insight, manufacturers can focus their efforts on those areas for maximum production and efficiency. In the case of the more complex pharma manufacturing process, big data can analyse these factors effectively and with ease. Segmentation of the process highlights areas with the highest error rates, which when addressed, allow the company to increase production and boost profitability.
Risk to any stage of the manufacturing process is a threat to output. For example, many manufacturers are reliant on the delivery of raw materials, and need to reduce risk in this area. Predictive analytics can be used to calculate the proba...
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