Statistics and tracking

The evolution of workflows and processes, especially concerning technological developments, often has unintended consequences. Chief among these is the raising of questions that had not been previously conceivable or even possible. If the library are able to provide a shared discovery layer that allows a patron their own identification of items, what are the implications for the borrowing processes or the identification of items compared to some objective number or record? What results from these consequences is again the evolution in resource sharing. That is the development of new technologies and new types of resources that had not been previously considered. One of these new technologies will be an increased integration with consortial partners in the form of statistics and tracking. If the consortia is serving as the sole processor and routes the request via the shared discovery layer, then there will be an increased need to track and report statistics of the behavior for the system as a whole. The increased integration will come with it a necessity to understand the different relationship within the consortia. The best way to track these relationships is statistical measuring of turnaround times and processing speeds. This is necessary for not only determining which members are effective and which are not, but also this data can be utilized for more advanced processes we will talk about later.

From that, there will be a natural discussion about the location of the items in process. While the patrons can, as technology has developed, come to expect items quickly, there is also an understanding that with clear expectations shipping an item takes time. This is seen with Amazon and other shopping sites. While the request is made seamless through a website for near infinite items a person could want, there is an understanding with the requestor that for a physical item there is shipping involved. So going hand in hand with the increased need for statistical tracking to understand the relationship between the partners within consortia, so to will there be an increase in being able to track items in transit. Understanding the relationship of where items are in transit can help inform the systems that make the requests, thus allowing for expectations to be set at the moment of request. For example, when a person orders from Amazon there is a display of the expected ship time. Not many will complain that their item took too long if they are told upfront the time it will take for shipment. Now, that is not to say that a patron may choose another option for fulfillment if the time for shipment is long, but if they accept the shipment time and make the request if there is an explicit acceptance of the time for shipment. It is this type of tracking that patrons have come to expect from their resourcesharing experience currently. Each of these types of innovation naturally led to the most radical notion provided in this chapter so far.

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