This device is one of the key products sold by Pall that plays no small part in ensuring the factories of their customers are able to continually operate multiple daily shifts, every day of the week. In providing this product to their customer, Pall has a number of moving pieces to support, including the factory machines themselves, along with machine operators, and factory managers.
Pall’s key role in supporting this fine-tuned industrial ecosystem: providing a filtration system that ensures the health of oil and other liquids necessary to keep factory machines operating.
The goal for Pall was to improve on their existing filtration systems by providing a connected, smart filter to their customers.
The challenge was to ensure that the connected filter was, in fact, smart in how it solved these problems. We’ve seen it before, where connected devices which gather and disseminate data do not provide added intelligence or streamline processes. Too easily, the smart filter software interface could have been a one-size-fits-all notification system and performance data dump. This would have meant a convoluted interface for machine operators, distracting them from their mission critical responsibilities, while anyone with access to the system would get all the notifications, regardless of whether or not they could act on them.
From a practical point of view, we needed to ensure that the filter and related software served the day-to-day responsibilities of factory staff, either by providing them with insight specific to their roles, streamlining process, or reducing workload. To do so, we needed to intimately understand factory systems and the roles of staff who ensured continual operations.
The journey mapping exercise for Pall involved focusing on the user group for industrial filters. There were two key people or personas to cover: the machine operator and the factory manager. Both groups were dependent on one another to ensure the factory continued to operate, so both journeys were essential to understanding the factory system.
Responsibilities varied, but there were identifiable stages and sets of responsibilities, beginning with daily routine requirements, periodic maintenance, and condition-based requirements, such as a machine performing below an acceptable threshold.
With insight and specificity on each person’s responsibilities and relationships between the groups, we were able to identify key factors and behavioural requirements that helped shape both the smart filter itself, and the behaviour of the software interface that provided data and notifications.
For example, we identified the following:
Factory managers were not as mobile as machine operators. They would benefit more from a desktop-based web portal which included detailed, historical performance data and updates on notifications which may have been disseminated to a variety of machine operators.
Machine operators were necessarily mobile, as they often were responsible for multiple machines or even multiple factory locations. This meant a smartphone interface—with location intelligence built into the filter software—would help each operator stay on task and ensure they only received notifications that were pertinent to them.
The resulting smart interface fed by a smart filter meant that the factories would operate even more efficiently, giving our client more capability to up sell customers to the smart filter system, and charge accordingly.