Data is everywhere, and yet only 26.5% of organizations report having established a data-driven organization (Bean, 2022).
Decisions made on the plant floor and in the corporate office can be the difference between a business that is thriving or waning. As commodities and operational costs increase, it is essential to steer a company in a direction where initiatives are propelled, objectives are met, and employees are empowered.
In a manufacturing environment, there is often a disconnect between plant operations and management regarding a direction to pursue. A rich dataset can be the bridge between the two parties’ initiatives.
With new emerging technologies from Industry 4.0, leveraging data from stations, operators, and supply chains enable manufacturers to meet business objectives and adapt to the economic uncertainties of the modern world.
What are the barriers? And what are the benefits?
However, with proper change management of the advanced technology transition, individuals will see the true value of high-quality data and its utilization; a powerful tool rather than a burden. Senior management and manufacturing staff can quickly identify accomplishments and areas for improvement.
A plant manager may have a new idea on how to improve the efficiency of their lines, and further quantitative research can PINpoint the key areas for improvement in the plant. With a strong historical dataset, past information may present novel ways to cut costs and increase production. It doesn’t stop there – sales, operations planning, and distribution optimization will also be more efficient when data-driven.
The visibility of data ensures shop floor and operations leaders have a deeper insight into performance across the whole organization, leading to data-driven decisions and actions rooted in knowledge.
Enhanced visibility: Ensuring that leaders have a stronger understanding of a plant’s performance and uncovering hidden insights from products, process steps, stations, and operator data leads to quick identification of waste such as bottlenecks and poorly performed process steps.
For instance, PINpoint’s 5-bucket model generates insights into how every second of production time is spent by categorizing it into distinct groups to understand where waste is occurring in the line. This unearths unseen opportunities which will enhance the scope of continuous improvements in a plant and further drive-up productivity. Future decision-making regarding continuous improvements and eliminating waste will be improved from the quick and actionable insights gathered from a rich dataset.
Automation: Various standard and routine decisions have to be made on a daily basis. A rich and reliable dataset will allow such decisions to be automated and employees to make better decisions where human input is necessary. For instance, figuring out how to rebalance work on the line may be a lengthy and labor intensive process. Multiple time studies, meetings, and calls may be required. However, with an automated collection of station and process step data, areas with excess capacity can be instantly outlined and the burden can be removed from the bottleneck stations.
Want to see how data-driven decision making can transform your operation? Our MES software enhances the visibility of your performance by uncovering the ‘hidden factory. From insights such as operator performance, process capability, and production time breakdown, all the way to data analytics services, we are here to strengthen your decision making and increase quality and efficiency.
Doesn’t an MES sound interesting? We hope so! Learn more about what an MES does and how it can help your facility become more data-driven. Visit our V5 Software Page to learn more about our MES software and its capabilities.
Bean, R. (2022, February 24). Harvard University. Retrieved from Harvard Business Review: https://hbr.org/2022/02/why-becoming-a-data-driven-organization-is-so-hard#:~:text=Barriers%20to%20Becoming%20Data%2DDriven&text=Second%2C%20becoming%20data%2Ddriven%20requires,It%20is%20a%20people%20challenge.