Proof of Delivery (POD) app is a mobile application, for in-house logistics customers, for delivery drivers to manage sales order deliveries (including Transfer Orders) and capture proof of delivery as well as perform pick ups relating to item returns (RMA’s) on their return trip. I have designed the taskflow models, information architecture, wireframes and mockups, supported developers by providing handoffs and necessary direction.
July 2018 - 3 weeks
Pronto - GGK Tech
Proof of delivery is where goods dispatched are delivered and the actual delivery date/time is recorded. i.e. establish and prove that the intended recipient has received the goods. Currently in Pronto a sales order or transfer order can be processed up to time of dispatch. However, once dispatched the fate of the goods is assumed, no evidence of what actually occurred is stored.
In the beginning phase, I with my team brainstormed areas that seemed intriguing to explore and asked: What does the driver want? We then created affinity diagrams as a way to find common themes and identified the red routes based on user stories we received. Thus, I narrowed my focus on understanding what is the major thing stakeholders want as well as the usability of the users. We decided to invest further with a question: What are the ways to get proof of delivery?
We took stakeholder interviews for initial understanding of the requirements, later we requested them if they can arrange formative user research since they were located in Australia. As resources were minimal and limited, we asked us to manage with secondary research.
I was in a dilemma, how can we continue designing a product without being user centric. And how can we be user centric without users?
I came with a solution, I noticed lot of delivery drivers of Amazon, Myntra and various other courier services lingering outside my office, using an application while delivering the orders. I decided to look into it and talk to them. As master of user research Steve Krug has rightly said:
“If you are building new product, test the competitor’s or comparable products with the personas who are almost closely related. It doesn’t have to exact.”
I created a script and a questionnaire for the few of the delivery guys from Amazon, Myntra, Flipkart and Big Basket. I interviewed, conducted an informal contextual inquiry and taped the whole process to later show it to the clients. We gathered some valuable insights out of the interviews which resulted in massive change in the user flow later.
After consolidating all the interview data I created an affinity diagram to see common groups of users, how they are currently behaving when go out for delivering and loading shipment, and also what value this app could offer them. I also found the most frequently mentioned goals which allowed me to see initial red routes and help develop user stories and personas.
We always consider this part the gentle synthesis of research and actual product design. Although technically we’re not designing the product yet, it’s only natural to think about these flows and questions in a more tangible way. We find (if we have time) creating detailed user flows can focus my mind on function and possible user decision-making factors at these points. We find it relaxing and therapeutic also!
By now we have a good idea of where to start, the red routes told us what our users are most commonly telling us: We want to operate the app in both online (full functions) and offline mode, we want to see a listing of manifests with manifest ID, packages, orders, time, volume, weight, delivery date, I want to visualise all deliveries within the manifest plotted on a map within the app. By keeping these close to the design at all times, I can focus my UX and build other functions around it.
Having concluded our testing and design for the previous stage, it was time to create a more viable design based on many well-thumbed sketches. Although testing will reveal further amends and iterations, we feel happy we’ve ironed out most of the bugs and have something on paper that will spring to life once designed and prototyped. Using the Axure RP, Adobe XD and trying out the libraries function for the first time, I designed the screen flows and complete prototype.
They had allotted on an average $458 per user as training cost of the tool, intuitive UX of the app helped them save that cost. Delivery drivers could easily adopt the app to their process and they didn't need have to undergo training. It saved lot of money for the company and resources that were supposed to deploy for the training.