Category:

At DoubleBlaze, our BlueYonder consultants started out deploying their pricing solutions over 12 years ago and are the BlueYonder premier partner for implementing their pricing solutions. We frequently work with retailers to help solve pricing problems, offering advice, building case studies, and architecting the best path forward. While retail price optimization and price list management software are strong suits in our practice, we also focus on their other solutions for retailers including demand planning, category management, and warehouse management.

BlueYonder Demand Planning

BlueYonder (formerly JDA) Demand Planning is a solid tool for managing retail forecasts. BlueYonder has two flavors of demand planning currently, the traditional planning tool based on their Supply Chain Planning and Optimization (SCPO) platform and the newer Luminate tools based on machine learning. A little bit of history is that a few years ago, JDA recognized they were falling behind in the use of machine learning tools so they acquired a small company out of Germany called BlueYonder that was strong in machine learning. They liked the name so much they adopted it for themselves and are now known as BlueYonder. The legacy BlueYonder company from Germany had a long history in machine learning which allowed JDA to leap forward in their use of the technology. At DoubleBlaze, we service both the traditional BlueYonder Demand & Fulfill as well as Luminate Demand Edge.

Category Management Consulting

The BlueYonder Category Management platform that we in the community call Cat Man handles all category management functions including space planning, planograms, and assortment optimization. The solution allows you to deliver better assortments, create better floor plans and planograms, and ensure you have the right products at the right stores in the right place to increase your margins. The system also helps maximize inventory and reduce carrying costs. At DoubleBlaze, we help retailers and CPG customers with new implementations, train employees on the solution, and update or enhance their current implementation.

BlueYonder Warehouse Management

The BlueYonder Warehouse Management System (WMS) is the third pillar in our extended BlueYonder retail planning focus. The BlueYonder solution handles the base use cases such as picking, packing, shipping, receiving, etc. as do most of the solutions on the market today. But it also handles more complex use cases such as slotting, yard management, and real time inbound/outbound processing. As with the other solutions, DoubleBlaze can handle new implementations, go lives, or enhancements.

New Projects or Enhancements

Whether you are starting a new project or need simple enhancements, our BlueYonder consultants can help. We can provide turnkey implementation services or staff augmentation with project managers, solution architects, functional and technical consultants. Schedule a meeting with us if you have an opportunity.

No comments yet

Lead Time Calculation is Hard

What’s in a delivery date? Turns out a lot in today’s complex supply chains making lead time calculation very difficult!  As discussed in our previous blog article Intersection of Price Product and Availability, customers want to know what product they’re getting, how much it costs, and when they can get it.  Each of these questions are answered in increasingly complex ways as manufacturers, distributors, and retailers optimize their operations, supply chains, and diversify their customer touchpoints.

Specifically here, we’ll touch on delivery date or lead time calculation and available-to-promise (ATP).  Delivery date comes into play before the order has been promised when a customer is trying to finalize an order.  As supply chains optimize, there aren’t as many finished goods in the system so determining delivery dates is more complex than simply checking inventory.  Also, in times of limited supply, goods may be tied up with contractual obligations on service levels and allocations.  Throw in build to order or mix options, and the myriad of permutations is even harder to predict.  Finally, there are transportation issues that could delay or complicate the answer. 

When we previously did this for a computer manufacturer, the permutations were so complex, we simply took a statistical average for lead time and used that, then when they couldn’t deliver on time, they’d notify the customer and take the customer satisfaction hit or make them happy by delivering early.  But when you’re a manufacturer selling to assemblers or selling through eTailers like Amazon or Ebay, they take delivery dates more seriously and if you miss your date there could be severe consequences.

Accurate Delivery Dates

So how do you limit your inventory exposure and also get an accurate delivery date back to customers?  Here are some of the ways we approach it:

• Standard Lead Time – As mentioned above, a standard lead time is the easiest to simply put on a product.  This is not always the most accurate or desirable method because it means you may have to keep more inventory than needed to maintain service levels.  In this case, we typically estimate the lead time by looking at past deliveries and then tweak that on products that are quicker to deliver or take longer using rules.
• Available to Promise – Available to promise is a complex mix that combines current orders with delivery dates, service levels, on hand inventory, and future deliveries.  Taking all these factors into consideration is difficult when you have a complex supply chain.  There are a couple products we use including BlueYonder’s Order Promiser and SAP IBP (formerly APO) that can accurately model all these moving parts and provide a good date.  With these systems, you also have the ability to move orders or delay them given service levels or a customer’s willingness to wait.
• Allocated Available to Promise – Building on ATP, in industries like semiconductor, there are also allocations to consider and limited supply.  These commitments complicate the delivery date answer because you may have firm orders further out that you’ve reserved inventory for, but need to satisfy an order now.  The AATP engines can see that you have replenishment orders pending and allow you to consume current inventory expecting that you will be able to satisfy the future orders with new inventory.
• Build to Order – Build to order needs a special mention because it complicates the process even more.  With build to order or configure to order we typically model the most constrained parts and don’t worry about the other parts that have more availability.  We have used the standard lead time method on the constrained parts in the past, such as processors for computer manufacturers, but have also used an ATP engine with the constrained parts.  Both methods work and it depends on the situation as to what we would recommend.

Customer Service and Lead Time

In all cases, customers and customer service people need the ability to query the ATP engine for availability or delivery dates when either taking orders, changing orders, or checking order status.  Customer service people also need the ability to make decisions like shifting availability when possible or pushing orders out when a customer allows it.  In large enterprises, these decisions must also happen in real time so that customers can make their own decisions.  As supply chains get more efficient and customers get more demanding, order promising quickly outgrows a customer service reps ability to simply check inventory and these ATP engines can fill that need.

What Next?

If calculating lead time and delivery dates is a problem for you, we can help. Whether you are using SAP, Oracle, some other ERP, or planning tool, we can extract the data and model your supply chain. For more information or to discuss how we can help, please schedule a call!

No comments yet

The goal of demand forecasting is typically to get a more accurate forecast and drive down inventory cost. A better forecast will lower costs for retailers by controlling inventory levels, lend insights to space planning, help back room inventory space, lower carrying costs, and limit excess inventory. When working with retailers, these are some of the areas we focus on.

Inventory Levels. A better forecast helps control inventory levels at stores as well as DCs and reduces the cost of carrying aged inventory, meaning less inventory dollars are aged out and can reduce shrink.

Space Planning. Forecasting allows for better space planning in stores with more accurate forecasts. For example, knowing that an item that sells 20 annually at any given location may not require 10 facings at every store, whereas a more popular item from a forecast perspective, may benefit from having more facings.

Storage. A better forecast aids in overstock/understock issues and saves “back room” inventory space due to just in time (JIT) replenishment. For example, as the old stock is about to run out, the new stock is being brought in and displayed resulting in less customer disappointment when items are out of stock.

Carrying Costs. Accurate forecasts allow for better allocation of purchasing dollars. For example, if an item is forecasting 1000 sales annually but is budgeted for 3000 items, the extra budget can be freed to put towards other items that may need more support.

Optimized Inventory. In retail, it is key to have the right amount of the right product at the right place at the right time which allows for money to be more intelligently spent on tactical decisions rather than excess inventory. A better forecast also allows for seasonality to be intelligently accounted for and included in the demand/inventory planning process.

These are a few of the areas we focus on when determining how a better forecast can benefit a retailer. Each area has a significant impact on cost. When we do a benefit analysis, we ask what the current forecast accuracy is and then calculate what impact a 3% or better improvement would mean across different categories. As more and better tools become available with machine learning, achieving a 3% improvement becomes much more feasible. We would be happy to do a high level assessment to help you justify a project.

No comments yet

A warehouse is a vital part of every supply chain.  From retailers to grocers and distributors to manufacturers, the warehouse makes the supply chain work.  At its core, the warehouse is a central location where goods are bought, stored, and distributed for additional processing.  Making the warehouse as efficient as possible drives down cost, order delays, and errors which is critical in today’s competitive environment.

Small or large warehouse operations can require inspection, procurement, acceptance, put-away to picking, packing, order assembly and shipping.  The Warehouse Management System (WMS) directs every step in the process and captures accurate records of where the inventory is and where it is going.

As companies adapt to changing consumer purchasing patterns, the WMS has to keep up.  Whether you are handling individual orders from eCommerce or bulk orders from retailers, the WMS needs to perform operations efficiently and be adaptable to new technologies.

In our experience, the WMS must have:

• Seamless integration to ERP
• Real-time inbound and outbound processing
• Unified yard management
• Intelligent inventory management
• Resource orchestration
• Intuitive and configurable user experience

There are many different WMS solutions on the market of which for the most basic functionality, they all check the box.  Differences are more pronounced as the complexity of the facility increases with the more advanced systems incorporating more automation, usability, and are beginning to use machine learning to help make processes more efficient.

DoubleBlaze can help you whether you are implementing a new WMS, managing go lives, need changes to your existing systems, or migrating to cloud.

No comments yet