This past week, I had a chance to spend a few hours with the folks at a large global food manufacturer discussing the evolution of their mobile strategy. The company has been using GPS-enabled mobile technology with its thousands of sales and merchandising resources for over five years. What started as a strategy to track its field resources to automate the payroll process, has turned into a large-scale program to improve productivity and performance. In my view, this manufacturer has reached the second wave of value in mobile technology - understanding what its resources are doing and using that information to recalibrate its planning processes and management strategies. The second wave returns have been as compelling, if not even more compelling, than the benefits achieved during the initial deployment.
Unless you are tracking your field resources with GPS-based activity statuses, you have no idea what they are really doing. Field workers are largely unsupervised and, unfortunately, there are a few "bad apples" in most organizations who aren't entirely honest about reporting their activities. "Stretching" is particularly prevalent, with hours worked and/or miles driven "rounded up" in an overly optimistic manner. If you want to know where you are losing 2-3% of your productivity, it could be right there. On the other side of the coin, without accurate tracking, it can be difficult to identify and reward high performers among those largely unsupervised field workers.
Which brings me to the second point - mobile data is big data. This manufacturer captures billions of data points every year. Every GPS ping tells a story; one that you wouldn't get from doing the monthly ride with one of your field resources. A lot of mobile data is transactional (pickup, delivery, arrived, depart, etc.), but provides a wealth of operational performance information because GPS provides time and location context. "Slicing and dicing" that information over a period of time can paint the true picture of good and bad performance and allow you to break down field performance into its fundamental components. If that doesn't sound like a job for big data, I don't know what does.
Getting the fundamental view of what is happening in the field provides a tremendous opportunity to improve the planning processes, management strategies, and human resource policies. Planning without operation data is an open loop process that runs off too many assumptions. This particular manufacturer was able to use the operational data to determine which factors really impacted its planning processes. The company actually simplified its planning models and can now more effectively deploy it resources to drive sales. Management is now focused on exception management and spotting workforce trends.
An interesting and counterintuitive point that occurred over time was the simplification and streamlining of the company's mobile application and data collection process. While the natural inclination is to capture as much data as possible, it became apparent that consistency was more important than breadth of data collected. It isn't realistic to expect field resources to get it right every time without workflow management and automation. However, making the workflow convoluted to capture all possible cases makes field workers unproductive and requires a lot of support. With a fair amount of turnover and part-time employees, ease of understanding became as important as determining what data they wanted to capture.
There were two key takeaways for me from this conversation. First, if you aren't using GPS-enabled mobile devices with your field resources your company is experiencing higher costs and inconsistent customer service - you just don't know it. Second, all that transactional data you are collecting as part of your mobile strategy to go paperless or simply track your resources can be turned into a tremendously powerful continuous improvement program.