Increase effectiveness and efficiency by turning disorganized processes into streamlined digital solutions.
One of our partners brought us in to rethink the way store managers and retail associates worked together. There was a disconnect between the two groups — especially when it came to ownership of work and progress tracking. Managers were writing daily tasks on a legal pad and handing them to employees to complete. This paper-based system was inefficient and led to lost productivity.
We built an intuitive platform that allows managers to delegate work, communicate the assignment of tasks, and track progress in real time for employees. This platform has been rolled out to the full organization, to five countries, with nearly 70% daily active engagement. This task management platform has dramatically improved employee productivity across the board. Resolving a store issue or task often took over a day before the platform, but now takes on average four hours. That means a better quality store experience for customers.
One client came to us to help reinvent their regulation reporting workflows. At the time, a workflow could not be submitted by a facility without using a desktop in a back office. This resulted in security issues and incomplete information due to rushed completion. On top of that, 70% of the reports were miscategoorized, and duplicated information had to be filled out for each submission due to the clunky system. Needless to say, the inefficient system was wasting huge amounts of time.
We launched a workflow management platform that’s reduced submission time from 15 minutes to two — all while reaching 100% adoption within the U.S. The drag-and-drop builder allows users to easily manage workflows that are business critical. An intelligent detection pulls demographic information which reduces repetitive information from needing to be filled out for each report, saving 1600 hours per year alone.
A major food processor came to us to assist with processes that were severely impacting their efficiency and bottom line. The company was losing millions of dollars annually on lost inventory due to mislabeling with inefficient processes in place. The variable here was a human’s ability to accurately identify and label a product at a reasonable pace.
Working with the team, we co-created a machine learning model that enables production-plant workers to more efficiently and accurately label products for processing. Through small tests and iterations, we were able to get the model to 90% accuracy. This technology helps employees identify products more accurately which in turn reduces significant product inventory loss.
