Big Data Doesn't Have to be a Big Problem

Big Data Doesn't Have to be a Big Problem

If you consider the formula Data + Analysis + Action = Gold, you can capitalize on all that Big Data has to offer.

In manufacturing today, there’s a lot of buzz about Big Data and the potential opportunity it represents. But how can Big Data help your organization? One answer is to integrate important financial and operational information with your existing labor data. The result is a comprehensive Big Data strategy that can turn seemingly innocuous data about your workforce into significant business value. Even better: such a scenario may already be within your reach. With the right methods for collecting, analyzing, and acting on all of this data, you can transform everyday information into a new competitive advantage.

Admittedly Big Data is a big concept, and one that can seem to present more questions than answers. How can you access the right data? How can you then make it actionable? How can you share it in a meaningful way across the entire organization?

It may seem intimidating, but if you consider the formula “Data + Analysis + Action = Gold” you can simplify the process and truly capitalize on all that Big Data has to offer.

A Winning Formula

Data

Clearly, it all starts with data. Yet many manufacturers find it difficult to access data that resides in different systems, like workforce management information such as wages, incentive pay, or individual productivity; financial data such as sales, cost of goods sold, or gross margin; or operational information including uptime, throughput, or cycle time.

Worse, even if they can get to it, the effort to compile it and make sense of it all is usually a time-consuming, manual effort that could take days or weeks to complete.

The right data collection techniques and methods can help you meet these challenges. From data collection terminals on the floor to scanned bar codes and proximity cards, there is a wide variety of methods for collecting labor data.

For example, the mobile device a FedEx driver uses to scan a package is similar to the technology that workers may carry in manufacturing plants. Supported by standardized applications, such devices enable employers to collect data that is more useful, and centralize it for easy retrieval and analysis.

Analysis

In today’s Big Data world, the overall volume, velocity, and variety of data are all increasing at exponential rates. So being able to understand what all of this data really means—and the impact it can have on your business—is vitally important.

As a result, determining the right level of precision—or the granularity of the data you collect and how frequently you collect it—depends on the nature of the data and its ultimate use. For example, if your data is being used for statistical measurement, you may need less precision and can use sampling strategies to minimize data collection efforts.

Action

The third part of the equation is “action,” which represents the idea that great analysis is meaningless unless it can be put into action.

In this phase, it is critical to provide decision makers—everyone from production managers to senior executives—with real-time access to the information they need to make the decisions possible. For example, a small change in materials may detract from product quality, leading to increased overtime and changes in how union agreements affect labor costs in future years. Big Data can help you connect the dots, so that insights are backed with fact, made relevant to the bottom line, and create the opportunities and the motivation for action.

Gold

Finally, gold represents whatever specific business goal you are trying to achieve. For example, gold can be cost reduction, improved productivity and throughput, operating margin improvement, or something else. The point is that an effective Big Data strategy can help you focus on the most critical business objective and achieve better results.

One tip: start small! For example, you can identify areas for cost savings by examining the relationship between unplanned absenteeism and unscheduled overtime. Then, as you attempt to look at larger issues, you’ll discover that the real value of labor comes by combining workforce data with financial and operational data. You can collect data on what your employees are doing and link it to important information, such as work orders, customer orders, or projects.

Better Insight for Better Business Decisions

With the right Big Data strategy, manufacturing executives—including operational and financial leaders—can gain the complete insight they need to make more informed decisions. For example, by analyzing the correlation between labor, financial and operational data, you can now identify your most efficient and cost-effective production facilities, or conversely, your least profitable products. You can also make more informed decisions related to global manufacturing, whether you should move production, or even discontinue production for a particular product or line.

Such Big Data analysis can even help drive product mix and pricing decisions, and help executives make the best decisions to maximize the use of resources to drive top-line revenue and strengthen bottom-line profitability.

Take a Small Step toward Big Data

Manufacturers no longer need to be trapped by data silos and wasted resources. By combining existing labor, financial and operations data, applying Big Data analysis, and acting on this increased insight, you can now make more informed decisions to lower labor costs, improve productivity, boost profitability and increase your overall competitive advantage.

Kylene Zenk-Batsford is senior manager, manufacturing practice group, with Kronos, a provider of workforce management solutions.

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