New Feature: Future Pick Labor Planning 

While we were developing real-time visibility to let warehouse leaders see what’s happening in their warehouse today, they were already looking ahead. They started asking, “Can you also help us better plan what we need for future days and shifts? Who we need to bring in, and how we should staff future days based on everything you know about us and all our historical data?” 

That was a good question, and the answer is “yes!” 

We spent a year focusing on achieving real-time visibility to give the operations team insight into how to manage the work and the workers they have today. Leveraging that underlying data set, we created Pick Labor Planning, which can derive scheduled workers and forecast work over the course of the next 7 days.

Then, with both of those data sets, we’re now able to give users visibility to what the warehouse labor planning should be for future days: if they should be thinking about adding or subtracting people or changing their goals. With Future Pick Labor Planning, warehouse leaders can look up to 7 days out, planning shift by shift to achieve daily goals. 

This new feature allows planning:

• By day up to seven days out
• By Shift
• By Throughput target

Warehouse Throughput Targets: 

• Goals: Ensure you achieve the amount of work you target each shift
• Dropped Work: Ensure you ship everything that has been dropped to the facility for the given operating day
• Scheduled Workers: Optimize throughput with the staff you have planned 
• SLAs: Ensure you ship to meet specific service levels or cut-off times

Operations teams have different warehouse management needs, approaches, and goals. With this feature, we provide visibility and guidance into how best to manage the team you have scheduled to get the work you need done based on the goals you set for yourself.

With the Goals view, for example, we know the number of people you have scheduled to come in, who they are, and the number of picks you need that day. We leverage our insight into historical worker performance to show you how to assign workers to the job they’re best at based on where we forecast pick volume to be. 

This lets you maximize efficiency and minimize warehouse labor costs by getting work done with the fewest number of labor hours. We show you how many people you could cut for the day or just for specific hours and still achieve your goal by following our plan. 

With the Scheduled Workers view, for example, we show you how to optimize throughput by assigning specific workers you have scheduled to particular tasks and how many picks you could achieve if you follow our plan. We show you the capacity you have with those workers so you can decide if you want to change your staffing plan to meet your goal

Warehouse leaders can see this guidance in various views, including headcount, timeline and roster. 

CognitOps Align can deliver this deep guidance because we’re constantly gathering data and tracking activity. Our machine learning platform tracks every worker and what they do in the building, automatically analyzes activity by task, and applies that historical insight to predict productivity going forward. We collect historical data from existing systems to get started and then continue collecting and refining data over time. We can assign workers and worker schedules based on actual historical performance. 

We’re at a point where we can forecast dropped work for future days more accurately and with finer granularity than our customers can, which allows us to create a staffing plan that better matches workers by skill to the expected composition of work. 

This is a huge step forward from existing Labor Management Systems (LMS), which just report on worker performance without helping you manage it. 

New Feature: Automated Area Level Reporting

The second big update is that we’ve integrated Google Looker business intelligence into our application to enable automatic and consistent performance reporting. 

Historical reporting is typically done manually, warehouse by warehouse, which means there can be inconsistencies in the data by team or warehouse. It requires manual aggregation and pulling of data by multiple people to then report up to corporate.

Since CognitOps is collecting all this data, we created standardized dashboards and KPIs that are apples-to-apples across warehouses. When each warehouse leadership team reports to corporate, they’re reporting exactly what the other team is reporting to corporate.

This report provides the granularity to go from day to week to month for as long as we have data. Users can compare measurements against each other. They can go from a parent area level down to a zone level. They can look at the data any way they want to slice it. 

Installing CognitOps in each facility delivers consistent reporting across all of them. 

The customer who originally requested this feature is over the moon with the results. His finance lead and other executives outside the warehouse are excited to have this validated, low-friction reporting capability – taking manual work and uncertainty off their shoulders. 

Conclusion 

These features are great examples of the close and collaborative relationships we’ve built with our customers. They share their needs and aspirations for the solution and that gives us insight into our roadmap direction going forward. 

Next month, look forward to an update on the work we’re doing around Pick Labor Moves to help customers manage their teams to make sure they meet their cut-offs and SLAs.