Software Roundtable: The Supply Chain Execution Puzzle

From one end of the supply chain to the other, processes that spent centuries focused on hardware are now underpinned by the exponentially growing capabilities of software. Because a given piece of hardware only ever needed to be good at its own job, industrial software solutions rose from the optimization of specific functions. Over time, the pieces were rounded together until they created solutions that encompassed an entire warehouse, or enterprise.

Even amid the information revolution of recent decades, the industry remained comfortable with a handful of broad software solutions adept at managing individual nodes of a supply chain. Each node had a list of software suppliers with expertise in one area or another, and connectivity between the solutions was often difficult and expensive.

Enter the supply chain execution (SCE) platform, which harmonizes all inventory, orders, transportation modes, customers and stakeholders in an organization around just one goal: profitable customer satisfaction. This goal of truly optimizing each transaction throughout the supply chain—every penny, each footstep—is becoming a more tangible reality, at least from a software perspective. Software suppliers have embraced the cloud, open architecture and solutions to support end-to-end visibility.

Although software providers might have spent recent decades boasting about their unparalleled skills in certain areas, not one of them will claim to have solved the SCE puzzle. The pace of change, both in technology and customer expectations, has led the industry to adopt a mindset of continuous improvement.

Modern talked with five experts about their thoughts on current and future trends in supply chain execution: Sean Elliott, vice president of technology at HighJump Software; Dan Grimm, vice president of solutions strategy for JDA Software; Eric Lamphier, senior director at Manhattan Associates; Roddy Martin, vice president of supply chain product marketing and supply chain thought leadership for Oracle; and Richard Howells, global vice president for SAP.

Over the next few pages, they offer their insights into trends in software development, implementation and integration, and share practical advice for how end-users can prepare for the future.

Modern: What is the “state of the union” in terms of the emergence of SCE platforms?

Elliott: What we’re seeing is more and more smaller companies beginning to realize the need for execution on the fulfillment side. Customers have tended to be fish or fowl—brick-and-mortar or e-commerce—and have found they need to merge and unify the execution side of each. The role of the supply chain is shifting and is more about customer satisfaction than the old focus on things like staff optimization, shrinkage and cost mitigation.

Grimm: An intelligent fulfillment SCE platform goes beyond just execution to include some planning capabilities. Many organizations are breaking down silos, and we’ve recently seen more people in charge of “supply chain” as opposed to just warehousing or transportation or planning, as companies realize those abilities go hand in hand. Otherwise you have one side of the fence planning, one side focused on execution, and they only talk when they have to. You can facilitate collaboration by providing better visibility upstream to planning, and conversely, if you can communicate the plan to the execution world, they can make decisions about assets, people and scheduling and be more prepared. Instead of suddenly getting an e-mail that you will get 50 trucks next week, you will have more specific and detailed information about what is coming at you.

Howells: The lines between planning and execution are blurring, which requires an integrated execution platform with linkage between warehousing and transportation, track-and-trace, and visibility across the supply chain. It’s the way of the future, in fact the way of today. This is a challenge for software, since many companies have implemented niche systems for warehouse or transportation, and if they are not talking to each other or designed to interact, there will be latency problems with data transfer. End-to-end platforms can be competitive for integration costs alone, since cobbling together different products on different upgrade schedules requires a big financial decision each time an upgrade is due.

Elliott: The concept of a platform is accurate, but it’s also misleading in some respects. A common solution package is ideal, but if you’re working with something else, you’re not always going to rip and replace. Therefore it’s important for vendors and end-users to look at the platform play, but also consider opportunities for competitive differentiation. A hub-and-spoke approach can work well, where you integrate products A and B to a master data layer instead of to one another directly. A new member of the solution family doesn’t have to integrate with 12 other products, just with the data hub. The goal is to build a platform backbone that is services-based to plug transportation management systems (TMS), warehouse management systems (WMS), etc., into a cohesive solution. There is no finish line, but the industry is pretty well down that path.

Martin: All companies are moving down a demand-driven, end-to-end supply chain model. It started with retail since they could more readily associate with the consumer, but now it’s moved into industrial areas and all companies.

Companies evolve their models at five stages of maturity:

1. Reacting to change. You didn’t see it coming, which resulted in stock-outs and other supply events.
2. Logic-based improvement. This is characterized by a focus on deploying solutions like transportation management, WMS or quality management in project silos.
3. Grouping of projects for functional excellence. Planning and sales and operations planning (S&OP) might coordinate, but still operate as functional silos.
4. End-to-end capabilities. This starts with the buyer and goes back to supply.
5. Value network. If you look at it as a business model, you see a demand-driven value network as a fundamental growth opportunity that makes supply chain leaders part of the senior executive team. It’s a very exciting time in supply chain management.

For a stage 2 organization, order management is very manual, with multiple supply chains, one lean and fast-moving, one slow-moving and nimbler, some more mature than others, and lots of complexity, which tended to be handled by the enterprise resource planning (ERP) system. Now I need distributed order management (DOM) to be as automated within those supply chain segments as possible. This involves a fundamental change. Traditional consumer-driven companies talk about capacity utilization or on-time delivery, but as you start operating at stage 4, it’s about customer service levels. It doesn’t matter if you assemble the perfect order in the DC if it isn’t delivered on time. End-to-end metrics look at cost to serve and customer satisfaction throughout, and every stakeholder along the way is held accountable for those metrics.

This gives rise to another exciting trend of moving from transactional analytics to predictive analytics. We’re not necessarily interested in taking transactional data out of the ERP or WMS and asking how many times something happened. Instead, you can mine the data for patterns and what is likely to happen. Traditional transactional data allows me to answer questions about the business I have. Now use the data to look for the questions I should be trying to answer.

Modern: How is the concept of DOM evolving?

Grimm: The way DOM started was to save a sale and keep a customer even though you might lose money to do that. The message we’re sending lately is that just saving the sale is yesterday’s news. No. 1 is to satisfy the customer and No. 2 is to do it in the most profitable way.

Many people are still losing money on every e-commerce order they ship, just trying to stay in front of the customer. From a DOM standpoint, it’s important to understand that an order management system can only make the best of an existing situation, so the benefit of planning tools is to make sure the inventory is ideally positioned in the first place. Otherwise, DOM just makes the best of a bad situation.

Elliott: DOM is part product, part service. Order management is a product that aggregates orders across channels. The “distributed” part is the rules engine, which comes up with the optimal way of getting product to the customer, accounting for geography, cost, timeliness and a number of other factors. That optimization piece is a service and is in some ways a philosophical question about balancing profitability and customer satisfaction.

Carrier performance is a huge piece of this. Today it’s a manual decision, and you might get a carrier scorecard for on-time delivery and damage in transit. But, what about customer satisfaction on delivery, or a scorecard based on the friendliness of FedEx or UPS? It’s anecdotal and qualitative, but critically important for customer satisfaction. Decisions can then be based not on profitability, but on the likelihood of repeat business.

For example, one of our refrigerated warehouse customers was launching and aggressively merchandising a new product. A familiar big customer still gets some priority, but the company had a strategic campaign to give even greater priority to new customers, since the long-term return on that activity is potentially higher. You probably won’t influence the buying patterns of a big customer with institutional history, but you don’t want to fail the first time at bat with a new customer.

Howells: Customer-centricity is a driving factor, and the need to respond to evolving demands. It’s now possible that a customer might know more about your product than you do, for example when it’s trending up or down. This is often localized, when a product is hot in Vermont and not Boston, and requires an execution system that can make sure inventory is in the right place at the right time and the transportation processes can make that happen. Predictive analytics are another emerging capability and are based on structured data like historic trends, but also that unstructured data like social media.

Lamphier: The grand plan is to move away from hardwired decisions, like routing any order from east of the Mississippi to one facility and western orders to another. A number of other things are being considered for more intelligent routing, and our customers now have increased control at the order management layer. Say there is a massive snowstorm in the Northeast. Employees and trucks might get to the stores, but nobody is going to shop. The customer can now send an extra 50% of online orders to the store. These factors are not yet automatically sensed, but the system will allow a savvy customer to make those sorts of decisions.

Modern: What is the changing role of WMS and warehouse execution systems (WES), and how are broader SCE functions impacted by each?

Lamphier: There is an interesting emerging trend. If the WMS can only send really large batches and chunks of orders, there is an opportunity for the WES to take that batch and reshuffle the cards, break down preconceived notions of wave, process and task, and make adjustments for better decisions in real time. Some legacy systems have rigid, hardwired rules. The need for something more fluid created the waveless concept, which makes sense for a highly automated facility. What we’ve done in response to that is what we call order streaming. Without the batch nature of a wave process, our approach to waveless takes orders from the ERP and pushes them through the allocation step instantaneously. Then, as people or equipment become available, the system adapts accordingly. A picker’s cart status, for example, is no longer two hours old, but was calculated minutes before they do the work.

Grimm: In the past, as the WMS fed tasks to equipment, in some cases there was not a lot of thought or optimization around what to do with those tasks. It’s now much more sophisticated to better use the expensive equipment companies are purchasing to get better at e-commerce. Then, you can leverage some analytical tools to see downtime, especially if you’re passing products from one piece of equipment to another, and optimize that based on equipment capabilities.

How big should a wave be so that each step can accumulate enough work to be optimally efficient? Will a 30-minute wave keep all equipment busy? What about 30 minutes in the morning and one hour in the afternoon? In the past, those decisions were a matter of trying and seeing if something worked based on instinct or gut feel. Now we can base those on real-world information that provides expected results and might even identify opportunities you never thought to try.