Any company that’s doing business in the twenty-first century has some understanding of the potential of big data to improve their businesses. The problem is that it’s much more common for manufacturers to have an incomplete idea of how data can make them more profitable.
A data strategy that’s not implemented correctly or that has the wrong goals can divert resources into inefficient channels, and incorrect analysis of data can send a manufacturer down an unprofitable path.
How Not to Use Data
A study conducted by Warwick Analytics found that more than 40% of businesses had a plan to develop a data analytics strategy but were only in the initial, experimental stage of their analytics implementation. Half of the study’s participants admitted that they had an incomplete understanding of both the fundamentals of analytics and the potential for analytics to improve their businesses.
Going into an analytics strategy with such an incomplete understanding is a recipe for disaster. At best, the company will realize little benefit from its analytics processes, and at worst, the inaccurate analysis could end up costing more than it delivers.
Advantages Data Can Deliver
For any business, data analysis has the potential to aid in decision-making processes and improve the way the company does business, both internally and externally.
For manufacturers, effective data analysis has the potential to deliver big cost savings that can dramatically improve the company’s bottom line, especially with parts sales within the aftermarket.
Further, data analysis can improve the production process directly. Machine-level analytics can spot inefficiencies in the production process that, when corrected, can increase throughputs and yields. Data can also be used for quality assessments that can spot problems and lead to process improvements.
Data can also improve the efficiency of processes surrounding the production process. Analysis of supply chain data can help a manufacturer streamline supply chain operations and reduce costs. Detailed analysis of operations can also help the manufacturer to anticipate maintenance needs and head off costly repairs and downtime.
Challenges to Using Data
When asked to identify the barriers to the effective implementation of data analytics, participants in the Warwick study cited both problems with their company’s culture as it impacted the implementation of data processes and the trustworthiness of the data itself.
Perhaps the most significant challenge in implementing a comprehensive analytics strategy lies in the processes for collecting the data. Often the data is collected from a multitude of sources, using a variety of technologies spread out through many business units. The processes, technologies, and units are not designed to work together, and integrating data from various sources is costly and time-consuming.
The inefficiencies of data-collection processes can also lead to distrust of the data on the part of the people who need to analyze it. The data coming in may not be clean, or there may be no way to tell whether or not it is clean, which is enough to make it unreliable. Ensuring the collection of clean data, and the processes needed to clean messy data are steps that cost time and money.
The answer to these problems is a comprehensive data-collection strategy that allocates sufficient resources to integrate data and make it available to all the business units that need it. The strategy must be over-arching so that its goals are clearly addressed throughout the process, not just as a collection of competing goals isolated within individual units and systems. Data audits, too, must be a priority so that data is always clean and reliable.
Analytics, Not Just Data
The technologies that allow manufacturers to collect huge amounts of data on their processes are ubiquitous, and most companies are utilizing them. Fewer companies, however, have effective strategies for using that data to improve business processes. Without effective analytics, data is just numbers that will do nothing to improve the bottom line. The value of data collection comes from the insights that it can deliver upon analysis; there is no inherent value in merely compiling data.
React to Data and Predict the Future
When manufacturers collect data, they have a tendency to use it to look back at the history of their processes. They use data as a tracking and recording system, and if anyone asks them what they’ve done in the past, they have vast amounts of data to tell the story.
What manufacturers are less good at is using predictive analytics to tell the future. With predictive analytics, a manufacturer can identify past inefficiencies in supply chains and production processes and then use their new understanding of the old processes to develop new, more efficient processes. It’s a seemingly simple step, but it requires a significant investment of resources in analytics. The need for that investment turns it into a step that many companies don’t take.
Predictive analytics also have immense power to take a manufacturer into the future of the market. By analyzing all the product and customer data a manufacturer has access to, that manufacturer can learn from current product offerings to develop new products that are even better at meeting customers’ needs.
Integration and Investment
The key to making data work for the bottom line is to understand that it can’t stand by itself. The numbers don’t increase profits—an understanding of what the numbers mean increases profits. That understanding only comes from an analytics strategy that’s integrated across the organization and that is supported by a total commitment to the value of data analysis.