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5 Ways to Use Big Data Effectively in Your Manufacturing Organization

Analytics tools could hold the key to finding areas of improvement in your factory.

Harnessing data is crucial: Two-thirds of companies participating in a 2012 MIT Sloan survey said using analytics gave them a competitive edge. Most factories could use the boost.

Manufacturing activity slowed throughout North America in August. According to the Institute for Supply Management (ISM), U.S. manufacturing activity fell to 51.1 from 52.7 the month prior. The reading is the worst in two years. In Canada, the RBC Purchasing Managers Index (PMI) fell to 49.4 from 50.8 in July. A score below 50 signals industrywide contraction.

In each market, factory managers are under renewed pressure to optimize processes and lower costs. Analytics tools could hold the key to finding areas of improvement. Here are five metrics that may be worth studying as you implement Big Data in your factory:

1. Line speed by product. Big Data tools are best for capturing machine-level information. Use that to your advantage. Take note of when and how often your line manufactures certain types of products and then use tools to track the time and effort required to generate meaningful output for each. That way, you'll have a better handle on what mix would produce the greatest profit.

2. Granular utilization data. When does your factory produce its greatest output? What days? What hours? And at what mix and with who on the floor? Again, the idea here is to study the conditions that lead to the very best outcomes and then seek to reproduce those outcomes on a regular basis.

3. Error rates correlated by product and employee. Avoiding mistakes is every bit as important as optimizing your mix and hours on the floor. Use Big Data and analytics tools to study error rates and then correlate the results by product and employee. You may find that some workers do particularly well with a certain brand of product and not so well with others, allowing you to optimize your mix and make smarter decisions when it comes to training and employee incentives.

4. Assembly speed by product and employee. Careful and error-free production matters most. But speed is also key, especially for facilities that deal in high volume. Use Big Data and analytics tools to segment production to better understand what products are easier to produce and then ask your floor leaders why. What is special about those products in particular? Would your factory earn higher profits by writing contracts to increase volume? Be ruthless in your quest to understand the benefits of changing your mix.

5. Relative cost to prototype. Sometimes you'll have the benefit of testing a product before it goes into full-scale production. Take note of these projects. Measure the relative time and cost it takes to develop a working prototype and then put the data into a benchmark. That way, you'll have a better sense of what makes for a profitable project before starting the bidding process.

Of course, Big Data is only a tool and what matters is what you do with it. Track line and assembly speed, utilization data, error rates and prototyping costs to find out what drives efficiency and profit at your factory. Then, recognize and reward the behaviors and processes that produce them so that everyone wins -- including the workers who stand to lose the most during this most current downturn.

John Mills is executive vice president of business development at Rideau Recognition Solutions, a global leader in employee rewards and recognition programs designed to motivate and increase engagement and productivity across the workforce.

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