Data has always been important. “You can’t manage what you can’t measure,” my first boss told me long ago. At that time, data consisted of what I wrote down on my clipboard – linear footage readings, tally information and labor hours. They were used in calculating recovery rates, trim loss figures and a few productivity and profitability measures.
By today’s standards, we didn’t have a whole lot of data to measure 20-30 years ago. Needless to say, management involved a lot of intuition and last-minute heroics in filling material voids or scrambling to put out rush orders.
But the amount of data has grown exponentially so that companies are now using the term Big Data. According to Wikipedia, “Big Data is a term for data sets that are so large or complex that traditional data processing applications are inadequate.” It describes Big Data analytics as “the process of examining large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information.” The innovation of solutions such as parallel processing and the necessary software (Hadoop, Llamasoft) have made analytics possible.
With machine and sensor generated data we are not only accumulating a great quantity of data, we are also now using different types of source information. Researchers categorize data as being structured, semi-structured or unstructured. The structured data is the area that businesses have traditionally used and which IT and accounting departments have typically tracked. Semi-structured information starts to get a little fuzzier. It might include content such as the results of customer satisfaction surveys or competitor pricing information. Unstructured data includes a variety of sources such as GPS telematics, twitter feeds, emails or blog posts.
In case you are wondering how large consumer goods companies can efficiently mine useful insights from their huge social media campaigns, Big Data analytics is the answer. Big Data is also behind understanding your interests. That is how Amazon.com can accurately recommend other books you should be reading, or how ads for vacations to Florida magically appear on your computer after you have been performing some related searches.
When it comes to the potential connection between Big Data and pallets in the supply chain, the connection has long been the RFID chip. The idea was that RFID could provide information about the pallet or unit load, date stamp it at particular locations, or through additional sensors, provide information about environmental exposures such as temperature or vibration. This was the premise behind RFID-tagged solutions offered by iGPS and many others. They believed their information could create value for the customer in their supply chain operations that would far outweigh any incremental cost of installing tags or readers. The market has been slow to embrace this proposition. Instead, optimization efforts have focused on other data gathering opportunities provided by transport vehicle telematics, forklift tracking or barcode scan data.
There is still optimism among some players that sensor-equipped pallets will find a following. Given the emergence of increasingly more data savvy waves of managers and tools such as Llamasoft which provide easier analysis of large data sets, perhaps pallets still might find a role as important sources of supply chain data collection.
Consider that earlier this year, Brambles announced the launch of BXB Digital. It is based in Silicon Valley, under the direction of Prasad Srinivasamurthy, most recently SAP’s senior vice-president of customer innovation and Internet of Things. Also noteworthy, Surgere, a third party management company for reusable packaging and pallets in the automotive sector, has been very bullish on the Big Data opportunity for using pallets and containers for data collection.
“I would submit that we are about to go through a change in supply chain that will be more important than anything else in automotive,” William Wappler, Surgere’s president and CEO, stated at an automotive logistics conference this May. He was talking about the impact of Big Data. “Automotive isn’t quite ready,’ he added, “but it is coming fast. My prediction is that it (the automotive supply chain) is going to be fully visible and it is going to be optimized.” He believes that the change is now starting, saying that his company had begun projects with four automotive OEMs and eight Tier One suppliers since the beginning of 2016.
Another small RFID-tagged pallet pool continues to grow. Axios Mobile Logistics rents its RFID-tagged composite pallets, and has been successful landing many leading U.S. egg producers as customers. (Full disclosure, I am on the Axios Advisory Board).
There is no question that the role of data is much more important than it used to be for pallet providers. Software such as Salesforce has become indispensable for many pallet companies in helping them organize and track customer relationships. Automated time and attendance systems help automatically capture employee attendance records and streamline payrolls. Preventative maintenance software is gaining traction and helping a number of companies save money. Some larger companies employ transport routing optimization software to shave time and miles from deliveries. The use of barcode systems has also provided impressive benefits for users of such systems. Then there is role of powerful pallet design software such as the Pallet Design System™ and Best Pallet™. The list goes on and on.
While data is increasingly important, I am not yet sure how Big Data will aid the average pallet company, but like a lot of other cloud-based solutions, everything eventually seems to become scalable to smaller businesses. Perhaps at the association level, programs such as Nature’s Packaging will benefit from analyzing public perception about pallets in social media.
Getting back to tagging and tracking, one idea that I find particularly intriguing is in better understanding how many trips that recycled pallets last, and the impact of this information on sustainability reporting. LCA studies typically assume that 48×40-inch non-pool pallets last only a few trips, and therefore they perform poorly compared to other alternatives when it comes to LCA assessments. I have a hunch that on average, these ‘one-way’ or limited-use pallets last for more trips than anticipated. By better tracking pallet life, we could get a clearer picture. The results just might be surprising.
