Distribution Centre & Demand-Driven Supply Chain

Content By | RIS News

Omnichannel retail is well known for its disruptive forces and tremendous opportunities. On the disruptive side of things, the rise of e-commerce has driven huge changes in the way of retail products must be distributed to the end consumer – not just through stores anymore but also straight to the shopper’s home. The operational challenges to adapt and compete have been immense. However, that transformation has come with the opportunity for double-digit e-commerce sales growth for many brands and merchants, plus the chance to establish even closer consumer relationships.

Adjusting the supply chain to meet omnichannel demand is not easy, and there are unique complexities facing every part of the chain, including product development, merchandising, sourcing, and fulfillment. This post will discuss some challenges and opportunities specifically related to the final links in the chain, from receiving goods at the distribution center (DC) to responding to orders.

The Efficiency-Expediency Balancing Act

The modern retail DC is a hive of multifaceted activities, all centered on moving product inventory where it needs to go as quickly, accurately and cost-effectively as possible. Traditional wave-based fulfillment processes have emphasized efficiency when it comes to picking, packing and shipping large batches of the same items. All hands on deck, so to speak, would focus on moving the batch from one process to another until it went out the door and onto the truck. There were buffers between processes and if all of the hands, from pickers to packers to loaders, were full of work. the whole wave would need to slow down or stop for a while until they were caught up and could let more of the batch’s items into the flow. But the work usually got one in an efficient manner because the facility was built from the ground up to support this type of flow.

Today’s e-commerce orders complicate things. Getting an online order out the door requires more of an assembly mentality and process flow. So how does a retailer coordinate all of those same “hands” to do all of the same processes when the “batch” is now a single unit or maybe two to three units? “They may as well be from different ends of the earth.” That’s how one seasoned distribution executive summed up the vast difference between the efficiency needed to handle traditional retail store fulfillment and the expediency so critical to filling e-commerce orders. The former requires workforce skills, systems, machinery, processes and infrastructure to move large quantities of items, in bulk. For example, a DC might process orders of thousands of items each multiple times daily, sending them off in large truckloads to stores. On the other end of the spectrum, expedient fulfillment also requires specialized processes, equipment, and technology, but this time focused on picking, packing and shipping thousands of orders of one or two items, each heading to a different destination.

Polar opposites indeed. It’s no wonder many retailers initially opted for separate e-commerce fulfillment centers. However, it’s now widely considered to be best practice to co-locate fulfillment operations for stores (retail) and e-commerce (online), with all their yin-yang differences, into DCs that can literally do it all. But to do it well, warehouse technology hs to let inventory flow efficiently or expediently when need be.

Like many leading fashion retailers, Gap Inc. has been investing in its warehouse management and fulfillment infrastructure to evolve with today’s omnichannel demand. In particular, the retailer has gotten away from having silos of distribution for different channels. It’s distribution network services the entire Gap Inc. family of brands, across both online and in-store channels. “Like our customer, we think about the omnichannel experience,” says Kevin Releford, regional director, global supply chain and product operations, Gap Inc. “Previously, Gap Inc. operated retail and online distribution centers separate channel operations, but our capacity and workforce have allowed us to flex to the growing shift toward omnichannel shopping by combining both channels into single facilities. This increases our efficiency to deliver inventory to our customers by having a more effective regional network.”

Strategies for the Demand-Driven Supply Chain

Warehouse execution systems (WES) are designed to organize, sequence and synchronize a DC’s resources, including people and machines, to adjust to real-time demand conditions and reprioritize work accordingly. They serve as a sort of connective tissue between order/inventory systems of record (WMS and ERP) and warehouse control systems (WCS) that drive material-handling equipment (automatic storage and retrieval systems, conveyors, sorters, and, increasingly, robots).

Retailers can leverage WES automation to prioritize the day’s shipping volume with minimal staff intervention, freeing DC management and team members to focus on making sure everything gets packed properly and out the door on time. They do not need to be concerned with scheduling the next batch wave or determining when DC resources will be able to handle time-sensitive e-commerce orders. When it makes sense to do so, batches of orders can still flow through the facility, but these batched do not prevent other orders from being introduced into the flow. Work on each order starts and finishes independently from the work on all other orders. In this way, the WES enables a blend of wave and waveless processing to provide more dynamic order fulfillment and to prioritize the most important orders at a given time.

Sometimes, this means identifying synergies among multiple e-commerce orders so that they can be processed with batch-like efficiencies – just in time to make the last truck pickup of the day. The WES knows exactly which orders are in the queue and how long it will take to process them from the time they are released until they exit the DC.

The latest warehouse technologies are leveraging artificial intelligence (AI) to analyze customer orders as well as the productivity of associates and material handling equipment, says Steven DeNunzio, a senior lecturer and director of the Master of Business Logistics Engineering Program at The Ohio State University’s Fisher College of Business. “More and more solutions in the WMS/WES/ERP space are moving toward the concept of lean fulfillment and away from traditional batch waves,” he says. “All of this is in response to an increasingly sophisticated and difficult-to-predict customer and the need to remain agile and adaptive.” “It’s not always about speed,” says DeNunzio. “I think the buzzword today is ‘precision’. Do we meet the omer’s expectations? That may include speed. Today, it usually does. But it also includes things like getting it to them precisely when they want it, or packaged exactly the way want it, or delivered on the other side of their garage or front door.”

DeNunzio offers an example of Amazon Day. Amazon customers select a day that’s most convenient for them to have their Amazon purchases delivered. “Unless you specify otherwise, everything’s delivered on that day. This concedes that while a customer wants something fast, they don’t want to be peppered with deliveries every day. That may not be their definition of ‘service’. They also need delivery convenience,” he says.

Gap Inc.’s investment in warehouse-related software and automation is helping the retailer respond better to quickly changing fashion trends. This includes fulfilling the demand from its expansive store network and individual consumers’ online orders. “Knowing that we must serve both channels (retail and online) seamlessly, the tools we use need to be flexible and focus on speed and scale. That allows us to react quickly to demand and keep our service levels high,” says Releford. “At Gap Inc., we are always looking to serve our customers better and will continue to invest in automation that allows our teams to move faster and remove friction.”

Finally, there is a trend among retailers towards using regional satellite DCs to better respond to localized demand. Such smaller urban and pop-up DCs will continue to become more common, says DeNunzio, who also expects to see the repurposing of unprofitable retail stores into points-of-presence for distribution. This might include part of an unprofitable store or the entire store. “This is just the continued evolution of true omnichannel/unified retail,” he says, as retailers focus on “finding better ways to shorten the last mile and improve service levels to customers.”

Conclusions

The time is ripe for retailers to leverage technology to fulfill orders, regardless of channel, from a common inventory pool within the same DCs, rather than trying to manage separate stockpiles and DCs for their e-commerce orders. By doing so, they optimize warehouse capacity, machinery, and labor and can often respond more quickly and cost-effectively to consumer demand.

It’s true that fulfillment requirements for these different channels – shipping in bulk to stores vs. shipping one or two items to individual consumers – can seem at odds with each other. But the technology exists today to efficiently – and expediently when necessary – flow both types of orders through the same facility. It’s not magic, but WES technology advances can make it seem as if the right products are making it to the shelf or to the consumer’s doorstep “automagically.”

Check out LS Central, an ERP system designed for retailers. Along with Dynamics 365 Business Central, all your warehousing, inventory, and distribution needs will be taken care of. If you would like to speak to a consultant please call allonline365 on  +27 (21) 205 3650 or email us on info@allonline365.com. 

Supply Chain and Master Data Decisions in ERP

supply chain and master data

Blog Written By | ERPFocus

ERP and Supply Chain: Master Data Decisions

It is often surprising for a supply chain team to see how much master data they create and maintain when its collected and organized in one place. Because ERP generally provides increased functionality, there is also usually additional master data required to use that functionality. Without attempting to examine every master data field in ERP, discussed below are three broad areas that hopefully help you to start thinking about supply chain master data issues.

1.  Data ownership when there is overlapping responsibility

An easy example of this category is the bill of materials. Many functional areas depend on the information in the bill of materials: finance for product costing, the supply chain for material demands, engineering for spec sheets or blueprints, and development for new product adoption. Which of these functional areas should have the ultimate authority over the numbers and relationships in the bill of materials? Typically, in legacy, every functional area created their own version of a BOM, because every functional area had a different agenda which marginally affected the data. Costing wanted numbers that reflected the lowest possible product cost; the supply chain wanted higher numbers to ensure they never ran short of anything. The best candidate for ownership of this type of data is the one with the least agenda, such as development. The paradox is because they have no agenda, they have no real passion for accurately maintaining the data.

2.  Yields and tolerances

This tends to be a greater issue in process manufacturing than discrete, but it needs reasonable consideration in both types of industries. In this context, “yield” is referring to the calculated expectation of how much first quality product will be produced on average from a fixed amount of components. “Tolerances”, in this context, refer to how much over or short you can be in filling an order, and still be of value to the customer. These two data pieces work in tandem as a hedge against manufacturing variation to determine how consistently you can satisfy customer expectations.

3.  Computing rules

These are shorthand codes that tell the MRP portion of ERP how to behave. Each rule is generally understandable on a stand-alone basis, but as the rules begin influencing each other, the results – while always logical – can be complex, unexpected, and unwanted. These computing rules involve everything from how to treat safety stock inventory to whether a material is purchased or manufactured to whether a material is make-to-order or make-to-stock. To master these rules generally involves experimentation, rather than intuiting the setup based on the written explanations.

To the maximum extent possible, assign informed people to figure out how to set up supply chain master data as soon as legitimate testing can occur. These people don’t have to own master data forever, they just need to discover and document what the right settings are. In the supply chain, master data has almost as big an impact on ERP performance as the configuration does.

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