Blue Yonder (formerly JDA Software) is a trusted retail solution provider with functionally rich applications, but no system can meet every retailer’s needs all the time. That is why RPE technical and functional solution experts have created a collection of  utilities that make Blue Yonder retail solutions even more effective and efficient. RPE has worked with several retailers to identify the most pressing challenges to increasing productivity and reducing disruptions and has developed utilities that address many common challenges.

Challenge: Allowing Planners to Remain in Their Plans During Submits/Approvals
Application: Enterprise Planning Stand-Alone

The basic Blue Yonder Enterprise Planning Stand-Alone toolkit requires top down and bottom up planners to be out of their plans when approving data to TDPP or BUPP. This utility allows the top down or bottom up planners to remain in their plans while the other planner submits/approvals and they can then load into their plans the other version submitted/approved when they want to see it reflected in their plan. It is similar to how Enterprise Planning EKB works for these versions.

Challenge: Uploading Custom Data to Allocation
Application: Allocation

The possibilities for data analysis in Blue Yonder Allocation are nearly limitless. The challenge for any retailer is how to make the data available to the tool in a timely manner, without requiring a communication trail between the user community and the IS department. With this in mind, RPE has developed the User Managed Data Upload tool, also known as UMDU. The UMDU utility gives the allocation team the ability to maintain Grades, Models, Locations and more using a seamless, one-way interface between the users and the Allocation server.

Challenge: Store Eligibility Enhancement
Application: Allocation

Blue Yonder Allocation determines need based on sales, inventory and other factors. What happens when your organization wants to institute controls on a specific buy of merchandise to keep certain styles in only certain doors? RPE’s Store Eligibility tool ties specific stores to assortments to ensure that users do not have to manually pull out stores from an allocation. By configuring the rules for the allocation up front, each need calculation will only send the product where you want it to go.

Challenge: Case Quantity/Pallet Rounded Allocations
Application: AllocationMen at desk with charts showing improvements from JDA Utilities

When using Blue Yonder Allocation to allocate bulk product from a DC or to generate purchase order quantities of palletized merchandise, it can be difficult to ensure that the overall quantities generated by Allocation meet pallet or full case requirements. RPE has created a utility which ensures that every allocation fulfills these requirements and provides cleanly rounded quantities, adding additional product only to the stores and skus that are most appropriate.

Challenge: Adding Attributes to the Worklist
Application: Allocation

RPE’s consultants are adept at creating calculated attributes within the Worklist. These attributes help drive decisions around managing slow and high velocity items. Adding calculated attributes to the Worklist gives the users the opportunity to filter the Worklist in a manner that quickly identifies opportunities and issues. For example, adding a weeks of supply metric to the Worklist will give the user visibility to items that are performing above or below expectations. Having this information helps the user identify which product should be addressed first. This assists them in prioritizing their workload; therefore, creating efficiencies.

Challenge: Auto Allocation Batch Processing
Application: Allocation

The Auto Allocation function within Blue Yonder Allocation streamlines the processing of hands-off allocations, sending these pick requests to the DC in batch form. For an organization with many skus or styles, this can result in an unmanageable number of disorganized requests. RPE has created a tool for the Auto Allocation function that groups styles by product hierarchy before being sent to the host. The result is pick tickets in fewer batches, organized in the most efficient manner for the picking team.