Data-Driven Decision Making with Process Quantity Analysis
by Scott Walton and Patrick White
Become Data Driven
- Review current data and reports generated from the data.
- Ensure the reports are driving behaviors that benefit the business.
- Eliminate reports not being used.
- Build on the data and reports already being collected based on areas for improvement.
- Identify an internal analyst with some operations understanding to support data analytics.
- Develop a series of Process Quantity analysis.
- Extract a story from the data.
- Seek feedback and input internally and externally to act on the data story.
Nearly every manufacturing company collects data; however, all too often, these data either are not analyzed to the fullest extent or are reviewed in silos. The truth is, your facilitys data actually tells a story. The key is looking hard enough to uncover the right information that will lead to improvement.
To effectively use data to make sound business decisions, companies need to leverage tools and strategies proven to be best practices in manufacturing. Regardless of a companys size, tactics can be leveraged to assist in collecting and analyzing the right data. Our recommendation is to start by building a master Process Quantity (PQ) Analysis file.
PQ Analysis is a lean manufacturing tool designed to identify issues throughout a manufacturing facility. It utilizes existing data to conduct specific analysis and determine gaps and opportunities. By clearly understanding the problem and using data to validate the issue, a company can determine the correct course of action to improve the business.
To implement a PQ Analysis, shop leadership must start by collecting raw data of part-by-part performance. The specific type of data included in the analysis will vary by company, but the foundation remains the same. The first set of data points focuses on quantity. This is the information that will be reviewed (summed) and will uncover performance successes or improvement opportunities. Examples of quantity-side analysis includes part volumes, sales dollars, operating margins and scrap, among others. Once the data and analysis are conducted on the quantity side, the results will inform what should be analyzed on the process side. This includes how a company groups jobs and parts and is frequently organized by categories such as industry, commodity and press tonnage.
For example, during a PQ Analysis, a company that looks at its hit rate data across one of its key industries automotive could quickly identify those customers that award limited business and hence are potentially using the shop as only a lever for negotiations. This challenge then can be addressed with a strategic solution of more selective quote filters.
A common mistake companies make is collecting and using data that drive the wrong behavior across the organization. For example, a company might exclusively measure sales by revenue per customer. This metric would indicate customers that are generating the highest revenue and encourage salespeople to continue to pursue those customers delivering significant revenue dollars year over year. However, this metric alone does not look at profitability of a customer. So, although a customer might generate high revenue, the projects may be underperforming and even unprofitable. By not measuring profitability alongside revenue, the company does not have all the information needed to improve profitability and, worse yet, risks negatively impacting the bottom line.
Other pitfalls include overanalyzing or finding flaws in the data and, therefore, not acting on or believing the story the data are telling. Companies frequently collect data and then spend a significant amount of time analyzing it to determine accuracy, which oftentimes means working hard to disprove the data. If a company has a sound data collection strategy, the data will be directionally correct, meaning accurate enough to drive action. In short, overanalyzing data can paralyze continuous improvement efforts.
Utilizing data-driven decision making is not easy. It takes a great deal of discipline across the leadership team, as well as a strong understanding of the end goal. Taking the time needed to put the correct data collection and analysis process in place is critical for success. As the saying goes, the squeaky wheel gets the grease so it can be difficult to be committed to the process as opposed to addressing an issue that might not provide the biggest return or improve efficiency. The best-in-class data-driven facilities understand how collecting the right data and acting quickly on the critical issues not only drive performance but also allow strong organizational and strategic alignment.
Becoming a company that leverages data and analysis within the decision-making process will more quickly and efficiently identify gaps and issues, allowing rapid implementation of change and improvements. Many leading-edge companies have, in fact, adopted a data-driven culture to quantify continuous improvement initiatives. By always questioning status quo and forcing a bit of discomfort, manufacturers will be able to improve business performance and establish a true competitive advantage.
Scott Walton is chief operating officer of Harbour Results, Inc. With more than 25 years of experience in strategic planning, operations management, lean manufacturing and supply chain management, he has assisted companies worldwide. Patrick White is a consultant for Harbour Results and is responsible for a wide variety of Harbour Results data analytics and development, as well as developing custom databases for clients. Combining operational and financial advisory expertise with industry analysis and thought leadership, Harbour Results delivers results that impact the bottom line. The company specializes in manufacturing, production operations and asset-intensive industries, as well as a number of manufacturing processes, including stamping, tooling, precision machining and plastics.
More information: www.harbourresults.com