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.
Demand for Big Data Analytics in Manufacturing
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.
How Project Managers Can Play A Role
Planning and Delivery
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.
Productivity and Efficiency
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 probabilities of delays, for example, due to disruption by severe weather conditions.
Analytics findings on weather patterns can help companies develop contingency plans and identify back up suppliers, etc. to minimize the risk of production being interrupted. Identifying risks and managing them on an ongoing basis is a core part of the project management team’s role, and data analytics will increasingly become a valuable tool for them in maintaining effective risk management within the manufacturing process.
In business terms, the era of big data analytics may just be dawning. However, the technology is already proving to be a critical tool for bringing about improvements across many business processes, particularly in manufacturing, where process complexity, process variability, and capacity restraints present challenges. Those companies that strengthen their capabilities for detailed analysis and assessment of their operations will make themselves more competitive and ultimately more profitable.
How Project Managers Can Become Big Data Savvy
In this age of digital transformation, project managers are increasingly aware of where the intersections lie between emerging technologies, sectors like manufacturing, and their own role.
They understand the impact that big data analytics can have for manufacturers. They have a key role to play in helping manufacturers select the right technology systems that will enable them to maximize their use of this data.
Project managers may need to acquire new skills and learn how to adapt to the needs of big data projects, and there are many training programs available that can help with that.
Leading a big data-driven project team can be quite different to leading more traditional software development teams, so here the project manager can draw on cross-disciplinary skills from other areas within the business, for example, from operations and business analysis.
By leveraging emerging technologies such as big data analytics the project management professional remains relevant and able to deliver real business value in sectors like manufacturing, where demand for these skills are in the greatest demand.