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JDA Focus 2018: The Launch of JDA Luminate

Inventory Management | By Chris Cunnane • 05/09/2018

I am writing this post from Orlando, FL, where I am attending JDA Focus 2018. The theme of the conference was “Expectations. Fulfilled.” and based on some of the new product strategies that were announced, it seems to be an appropriate theme. The big news from the conference was the launch of the JDA Luminate Platform, a connected platform that combines next generation digital technologies with product enhancements to JDA’s core solutions.

I talked with the JDA team a number of times about the impact of Luminate, and where they see the platform going. Essentially, the platform is going to bring in edge technologies and real-time data sets to every node within the supply chain. This includes the Internet of Things (IoT), artificial intelligence, machine learning, and social media, news, events, and weather (SNEW) data. The goal is to have real-time, actionable data available to make better decisions across the entire network. This will eliminate the need for batch updates which only give a snapshot in time rather than a real-time look at inventory, transportation, etc.

There are two key characteristics of JDA Luminate that should be noted. First, Luminate includes brand new, net new products. These products are designed for real-time decision making, leveraging an assortment of data streams. The three new products are Luminate Control Tower, Luminate Assortment (which is an enhancement and renaming of Retail.me), and Luminate Store Optimizer. And second, JDA’s existing core products will be enhanced with the technology. These enhancements will be add-on services that customers can choose. While the company has developed roadmaps for each of these enhancements, the enhancements will likely take place on the normal maintenance path of existing core applications.

JDA also set out to ensure that the new products would resonate with customers. To that end, the company did not develop Luminate in isolation. Instead, as JDA CEO Girish Rishi noted, customers were part of the development process. JDA worked with two to five customers on the new initiatives. This enabled the company to have measured value when the products were released. It also ensured that they did not build key pieces of functionality that did not meet customer expectations; instead, the functionality was developed in conjunction with actual customers, which should make the applications more appealing.

From what I saw, a few of the Luminate products stood out to me. JDA Luminate Control Tower is a centralized mechanism for analyzing digital, real-time, granular data. The application addresses inventory visibility and supply chain disruptions. It will be the foundation of all data science for JDA, and utilizes machine learning for prescriptive decision making. JDA Luminate Demand uses machine learning to develop highly accurate forecasts. The solution brings in additional information such as SNEW data to make decisions. JDA Luminate Store Optimizer looks to build out the store’s capabilities in the omni-channel environment. The application dynamically merchandises individual stores, starting with shelf-level hygiene and better inventory accuracy. It also prioritizes tasks within the store, so associates are able to work more efficiently.

JDA also had some exciting news around its partner network. As visibility has become more important, the partnerships JDA is focusing on reflect this. One such partner is Fourkites, a company that offers a real-time supply chain visibility and predictive analytics platform with more than four million GPS/ELD connected devices. The company has launched Predictive Capacity Management (PCM). This offering can predict when a truck will be available to pick up a load based on their tracking algorithms; using real-time data, it can give a narrow timeframe for when that truck will be ready. I also saw a demo of Luminate Transportation, which had Fourkites embedded in the workflow. The application allowed shippers and carriers to match backhauls based on the timing of fronthauls. Because of the technology, and knowledge of where the truck would be at what time, backhaul visibility and availability can be determined any time from when the fronthaul is scheduled to when the truck is making its return trip. The end goal is to eliminate deadhead miles.

JDA has also onboarded projectt44 as a partner. Project44 has an API network that provides real-time connectivity with carriers. The company provides a single integration point to the network. The application sits within JDA’s TMS, and automates the process of matching freight with carriers. The sweet spot in this market is LTL and Volume LTL. For Volume LTL, project44 helps to eliminate overage charges if you are trying to use LTL as well as paying for capacity you don’t need if using TL.

JDA Focus showcased the company’s move to more innovative supply chain offerings. The JDA Luminate Platform is a perfect example of this innovation, as it moves the company away from product nodes and into a real-time connected network. By utilizing SaaS, machine learning, artificial intelligence, and IoT, JDA is moving the product suite in a direction to meet the changing needs of its customers and uses the vast amount of actionable data available to make better decisions. Since the network was just launched, we will have to wait and see how customers react and what success this brings.

Controlling Robots, Easier Said than Done

Material Handling | By Steve Banker • 03/27/2017

Robots running amuck is a common theme in science fiction.

When it comes to autonomous mobile robots (AMRs) in warehouses, the concern is more prosaic. There needs to be a layer of software that allows the robots to work optimally with people and other material handling systems.

In modern warehouses, the central software is a warehouse management system (WMS). WMS has the order logic; it understands what inventory is coming into the warehouse and needs to be put away, and what specific goods need to be picked and shipped out. It has also had logic that makes sure humans are moving efficiently through the warehouse. And the WMS understands where the specific products are stored in the warehouse.

Warehouse control systems contain material handling ACTION logic. The warehouse management system tells the WCS what needs to be picked from a specific location and moved to another destination. The WCS does not need to understand what the items are or how they fulfill specific orders, it just needs to ensure that goods move from a specific starting location to a specific destination. The warehouse control system also has logic to make sure the material handling systems are running optimally; that conveyors, for example, don’t have too many goods being inducted onto them which could cause log jams at downstream ejection locations.

The material handling systems in turn have a thin layer of machine control that acquires data from the machine, makes sure that a specific machine is operating in normal parameters, and signals when the machine needs maintenance.

Now the trick is to make sure that all these different systems operate efficiently together to optimize the operations of the entire warehouse. Thus, for example, it does no good to have more goods arriving on a conveyor at pack stations than workers can handle; the goods from the conveyor need to be efficiently throttled to handle the speed at which the humans are putting goods into cartons and labeling those cartons.

But autonomous mobile robots create problems for traditional warehouse control systems. Traditional material handling is bolted down, AMRs move about.

Experts in logistics technology have a wide variety of opinions on the best way to globally optimize a warehouse utilizing mobile robots.

Matt Butler, Director of Solutions Strategy at JDA Software, argues that AMRs can be efficiently managed by the WMS. “People’s work is throttled by a WMS.” Mr. Butler explains, a robot or Automatic Guided Vehicle (AGV) is no different than a typical user; you can control it through a directed work task queue just like you would a human. This task queue knows source and destination locations, and work gets routed to users (human or robotic) based on permissions, priority, and proximity of the work to be performed. Mr. Butler views controlling high speed sortation as being a type of automation where a better argument can be made for control at the warehouse control level.

SAP offers both WMS and WCS. Markus Rosemann, the Head of the Supply Chain Execution Line of Business at SAP agrees that the level of control for robots does not need to be as tight as for other forms of automation. “There is more intelligence in the devices” and instructions similar to what humans are given can be used. But a total solution “still requires a granular level of flow control” across a variety of material handling technologies.

Bernd Stöger, the Product Manager for Knapp’s Software line, has a different perspective. Autonomous robots or shuttles need a layer of fleet management logic. “The fleet management knows where each of those shuttles are, the battery load, whether the container on the shuttle is loaded at that moment, and its target location.” Fleet management is a module within Knapp’s KiSoft software suite.

Man Robot Interaction with the Knapp Open Shuttle

For all the material handling solutions and the humans in a warehouse to operate effectively together, “there needs to be clear layering and modular software” Mr. Stöger said. Knapp, an Austrian company that offers both material handling solutions, including robots, and warehouse management software, has this architecture. “We can integrate to any new robot or even drones. We could do it in a couple of days to a couple of weeks depending on the device.” This same abstraction layering, Mr. Stöger asserted, is also important to support Internet of Things (IoT) capabilities.

The use of autonomous mobile robots in warehouses is set to explode. The question of how best to manage those robots won’t go away any time soon.

ARC recently completed a study of the global warehouse management system market, the increased importance of automation to serve e-fulfillment warehouses is key theme that emerged in the study.

What Fleet Routing Solution is Right for My Company?

Transportation Management Systems | By Steve Banker • 01/04/2017

Several providers of transportation management systems (TMS) have invested in the user experience (UX). These suppliers have worked to improve both the ease of use, the intuitiveness of solution, and the ability of power users to do their work efficiently, for example, without having to have multiple screens open. In TMS, that means a focus in particular on the role of the dispatcher. TMS solutions have added a Google Maps look and feel to the dispatcher landing page, and most TMS solutions have drag and drop to allow dispatchers to drag loads from one truck to another, or a driver from one load to another, and then have the plan automatically update itself.

SAP has also invested in providing their planners with Gantt Charts to view the scheduled loads. Bill King, the Director of Supply Chain Execution Solution Management at SAP, made the point that with a “Gantt Chart dispatchers are not forced to trust the output of the optimization engine. It appears to us that many transportation planners must be from Missouri, the ‘show me’ state, because you have to show them before they believe it.”

The idea that a supply chain solution should not be a “black box” solution – a solution that forces planners to blindly trust the output of the optimization engine – has been around for close to twenty years. In transportation, the need for this has been glaringly obvious because of all the parameters that can go into a “solve.” The goal of the solve is to hit service commitments at the lowest cost. But historically the output of the engine might not be totally feasible for a whole host of reasons. So for example, Gina does not want to be on the road for more than seven days in a row, or George does not have enough Hours of Service (HOS) left to do a particular delivery and arrive on time.

In addition to driver constraints, there can be constraints surrounding equipment (two trucks are in the shop for maintenance), customers (an important customer wants the deliveries to be handled by Dave), products (Hazmat products can’t ship with produce), and sites (you can’t deliver to this location after 6 pm). Unless the optimization model fully understands these constraints, and historically they have not, the output will often not be fully feasible.

This is why TMS solutions need a planner’s workbench. These workbenches give planners the ability to override an engine’s output and manually manipulate loads prior to releasing a truck and a driver to a route.

But while recognizing that not all plans produced by an optimization engine will be fully feasible, and that there will always be a need for planners to adjust the output, leading TMS suppliers are making a real effort to have solutions capable of modeling virtually all constraints.

Derek Gittoes, the Vice President of Supply Chain Management Product Strategy at Oracle, said “the goal really should be to try and get away from replanning. You should not get to the stage where you are assigning loads and find our you can’t cover all 20 shipments.”

The Oracle solution is a robust solution, but the goal of being able to fully model all constraints is never ending. “In our fourth quarter release we are expanding our driver management capabilities.” This means modeling when drivers are available, when they want to work or not work, their qualifications – whether they are qualified for Hazmat loads, for example, and what type of driver they are (over the road or a local driver, for example).

Fab Brasca, the Vice President of Solution Strategy for Intelligent Fulfillment at JDA, pointed to the difference in routing a private fleet versus loads that are tendered out to common carriers. “In setting up a fleet it is important not to treat it like a commercial carrier, the fleet needs its own behavior.” Things like asset types – a reefer versus a flatbed, for example, are the same from a scheduling perspective in either type of solution. But in other areas, the fleet model has to be deeper. So for example, if you tender out to a carrier, the carrier is expected to make sure that the driver has enough Hours of Service to make the trip. For your own fleet, you need to know what the HOS situation is for specific drivers in your fleet. You also need to model the home domiciles for drivers and things like the locations where trailer swaps can occur. A trailer swap occurs when drivers meet in the middle of long routes heading in opposite directions and switch trailers.

But Mr. Brasca also strongly made the point that for shippers with private fleets, fleet routing is not the be all and end all. “Transportation optimization requires a holistic view across all modes. JDA’s approach is that as opposed to building specific fleet capability, we come from a paradigm where you look to plan across all modes concurrently.” Further, when it comes to road transport, the model should be capable of planning fleets in conjunction with common carriers. And for multimodal shipments, the solution should be capable of making “very complex comparisons through multiple legs.”

One of my jobs as an industry analyst is to put together supplier selection guides that contain configurable criteria that companies can use to select a solution that is right for their business. This year’s TMS supplier selection guide was enriched with much more fleet functionality than the previous version.

In putting together this guide I came to the realization that if you understand a solution’s model, what constraints and parameters the solution models, and how granular that model is, you also understand the functionality that solution will be able to deliver. So in a request for proposal (RFP), for example, you might ask for functionality such as support for road trains – a cab pulling multiple trailers. Or you can ask how the solution models equipment. For example, does it model cabs and trailers separately? And what kinds of regulatory details are included in the routing map?

In the title I asked the question, “What Fleet Routing Solution is Right for My Company?” Clearly the user experience and the depth of the supply chain model are two key things to look for.

JDA’s Roadmap for Supply Chain Visibility

Supply Chain Visibility | By Steve Banker • 08/22/2016

On Friday JDA Software announced that it received $570 million in additional equity funding. This made me think about their product roadmap. In particular, would this change their roadmap for supply chain visibility? I hope if anything it enhances it.

Supply chain visibility has become hot, as multinationals again begin to explore supply chain control towers. For JDA, not unexpectedly, much of the initial roadmap for visibility has been focused on their transportation management system (TMS) solution because ensuring on time shipments makes visibility particularly critical to this application. Their vision is to become a “Hub of Hubs” type visibility platform.

Public cloud B2B solutions have special advantages when it comes to supply chain visibility. These solutions offer a large preexisting network of companies already connected to the platform, which means key supply chain partners are apt to find it easier to provide visibility to each other. And these solutions offer a record of marketplace transactions that greatly reduces “he said, she said” type arguments surrounding transportation contracts.

The problem is that no one B2B marketplace offers robust visibility across all modes of transportation and industries. That is why JDA needs to find strong partners in order to be able to provide “hub of hub” visibility. In conjunction with their big user conference in March – JDA Focus – JDA announced three partnerships in this area: INTTRA, FourKites, and TransVoyant.

INTTRA I knew. INTTRA offers Ocean visibility based on EDI messaging, with the visibility to the ever changing sailing schedules of Ocean carriers being particularly important.

FourKites was new to me. I reached out to FourKites and their CEO – Matt Elenjickal – was kind enough to talk to me. EDI is a far from a perfect mechanism to track shipments. For truck tracking purposes, real-time GPS data is much better. The FourKites cloud-based solution connects to feeds from more than 40 GPS device manufacturers whose devices are widely used in the trucking and intermodal industry. FourKites also has access to traffic data. The idea is that real-time data from GPS tracking will be combined with traffic data and with JDA TMS load plan data to allow for much better ongoing reoptimization of transportation plans. So for example, if something is causing huge traffic jams in Kansas City, the FourKites/JDA integration will allow new estimated times of arrivals to be calculated for trucks stuck in that traffic and new routes to be generated for trucks that have not yet arrived in that metropolitan area.

I also knew nothing about TransVoyant, but was able to have a conversation with Dennis Groseclose, TransVoyant’s CEO, about their solution. TransVoyant also makes use of real-time data for tracking of ships and planes. The company combines current and historical data streams with predictive analytics to forecast whether a particular shipment will arrive on time if the goods are shipped from a particular port or airport or cross a border at a particular border crossing. I also managed to talk to one of TransVoyant’s customers who confirmed robust Air tracking capabilities. This customer said they have not finished the testing surrounding Ocean visibility, but suspects it will also be very good. TransVoyant’s capabilities in the predictive analytics area have been strengthened by the ongoing work they are doing with intelligence agencies “to analyze, predict, respond to, and prevent both natural and man-made threats.”

While each of these partnerships has a very specific function that aligns with JDA’s TMS solution, the JDA/TransVoyant partnership is particularly important to JDA. TransVoyant will become a key part of the JDA platform because of their ability to take large data streams and analyze that data within a rules framework that allows for new insights to be discovered. While much of JDA’s initial work is based on using new visibility streams to improve transportation optimization, in the longer term they want to use new streams of data – like social and weather data – to improve demand-supply balancing. TransVoyant may have a big role to play here.

The phrase “in-line” has become common in describing a particular feature of analytics solutions. “In-line analytics” display a metric that is meaningful during a particular step in a multistep process. Critical to the JDA visibility enhancements will be JDA’s robust set of APIs and existing ability to import external content and build robust in-line capabilities. For example, JDA has 300 plus APIs just to support their TMS solutions. JDA customers, for example, that have subscribed to Chainalytics’ transportation lane benchmarking data can already use one of those preexisting APIs to punch out to the Chainalytics’ content, pull it into a transportation planner’s user interface, and have the relevant data be displayed at just the right point in a tendering process.

In conclusion, JDA’s focus on in-line optimization is interesting. “In-line optimization” applies that same in-line concept widely used in analytics to optimization. Their platform will reach out and grab pertinent event, risk, and predictive data and bring it back to their optimization engines to allow for ongoing replanning when unexpected events have led to degradation of the initial plan. But JDA is not interested in visibility for the sake of visibility. Their new capabilities must provide tangible and realistic enhancements to their existing applications.

Supply Chain Operating Networks are Finally Beginning to Emerge

Cloud/Software-as-a-Service | By Steve Banker • 05/24/2016

The concept of a network of networks – a network that rides on top of other networks and leverages those other network’s data – has been in existence for a while. ARC prefers the term “supply chain operating network,” which we have been writing about since 2003. While the terms are in use, what has not been present is an actual working example of a supply chain operating network. That may be about to change.

Supply chain visibility & collaboration solutions take different forms. Some visibility solutions are built on top of BI and event management technologies, collaborative planning solutions can be fast optimization solutions, and other solutions have evolved out of B2B networks.

In the early 2000’s, a large amount of venture capital was invested in the creation of B2B marketplaces. 90 percent of those marketplaces disappeared. However, the B2B networks that remain, and whose visibility extends beyond procurement processes, are among the leaders in the supply chain visibility & collaboration market. These suppliers include GT Nexus, E2open, Neogrid and Elemica. This is a different set of suppliers than dominate other supply chain management (SCM) software markets. The very fast growth this market has experienced has come, and will continue to come, largely from the B2B public cloud providers.

Further, contributing to the strategic position of B2B network providers is the concept of a supply chain operating network. This vision is being pursued by JDA, Oracle, and SAP. All three of these companies recognize that supply chain synchronization capabilities are key for success in this market.

All three of these big SCM suppliers offer a variety of planning and execution solutions. All have developed robust supply chain cockpits built on BI technologies that allow for the real-time analysis of supply chain data with drill down capabilities. All have very fast in-process memory technologies that allow for simulation and predictive analytics. All have cockpits that allow the sales & operations planning process to be effectively managed. And all are seeking to go beyond inward looking S&OP to more collaborative planning environments built upon better visibility.

Traditional View of a Supply Chain Platform

To accomplish this, these suppliers have partnered or procured providers of B2B networks. Yet these SCM suppliers recognize that no one B2B network provides all the visibility necessary. Descartes, for example, is very good for Air cargo visibility, while Transporean excels at visibility into European road transportation. So to take supply chain orchestration to the next level, they will need to collaborate with a variety of B2B network providers.

Supply Chain Platform with a Supply Chain Operating Network Layer

All three of these SCM solution providers recognize that effective integration to those networks will depend upon semantic integration; in other words, a transport order as defined in SAP will need to be mapped to the same concept in a solution like GT Nexus to insure that apples are not being confounded with oranges.

Oracle, JDA, and SAP recognize that semantic integration is difficult and admit they are early in their journey. And in all three cases the vision is that they will be a supply chain operating network, the visibility layer that rides on top of all other networks. The execution of this vision is apt to be rather slow, but steady. In a few years, our view of what a supply chain platform looks like could be completely different.

This concept of a network of networks is one of the key factors that will drive growth in the supply chain visibility and collaboration market. Yet this is already the fastest growing of all supply chain software markets. For more information on this study, click HERE or contact Conrad Hanf at chanf@arcweb.com.

Are 3PLs Better Able to Leverage TMS Solutions than Shippers?

Logistics Service Providers | By Steve Banker • 11/30/2015

We have a new generation of transportation management systems (TMS) based upon more powerful in-memory technologies. What this has created are more advanced transportation simulation capabilities. The way this works is that a user would have dual instances of the TMS. They would have an operational TMS that continues to plan, manage, and execute transportation moves. They would also have a simulation/test environment that can take months of actual historical shipment data (forecast data can also be used), loads massive amount of data into the tool (this is truly a Big Data application), and answers the question, “What would have happened if we had done this instead of what we actually did?”

I believe this is the most underutilized TMS functionality in modern TMS solutions. So I was glad to come across a company actually using this feature set. I talked to Steve Sensing, the President of Global Supply Chain Solutions at Ryder Systems, and he described how Ryder is using this.

Steve Sensing

Ryder’s Supply Chain Solutions (SCS) division is the unit that provides managed transportation as well as integrated supply chain services. SCS purchases and executes nearly $5.0 billion in freight spend for their customers. From a modal perspective, they plan and execute across all modes with a particular focus on truckload, LTL and parcel. The range of services includes transportation optimization and planning, execution, carrier procurement/rate negotiation, and freight bill audit. They also use their visibility into their customer’s moves to provide capacity management services, essentially helping customers discover backhaul opportunities. Their transportation optimization and execution, procurement group, and freight audit groups are located in control towers in Novi Michigan, Roanoke Texas, and Nuevo Laredo Mexico. They have over 450 employees associated with these towers.

While Ryder uses a variety of other tools for specific logistics problems, their primary TMS is JDA TMS. This is the TMS solution they use for the TMS-based simulations. Steve pointed out that one of the nice things about having a mirrored simulation environment is that you can test a new transportation policy – perhaps looking at the impact on total landed cost and service levels from the change – and if the change makes sense, it becomes very easy to flip a few changes in the TMS and make the change. In fact, it can make sense for policies to change by season, or even more frequently.

I asked Steve for an example of what kind of policies they test, Steve mentioned testing whether a pool point – a location for shipment consolidation – should be used. He also gave the example of one customer, a technology manufacturer that was getting ready for peak season deliveries to a retail client at stores over a two week period. They ran different scenarios to drive the cost per hundred weight down while hitting service targets. Those scenarios involved looking at product mix, modes, and cross docks.

I also talked to Tom Knutilla, Ryder’s Supply Chain Services divisional CIO, about this solution. He mentioned that they use TMS simulation in three types of situations. First of all, the tool is used when Ryder is in a managed transportation sales cycle. They can load a TMS with the potential customer’s historical transportation data, then load in the carrier rates that they could drive using their carrier base, and thus be able to understand the potential savings they could drive for a customer.

Secondly, if Ryder wins the business, they can use TMS simulation to validate the savings they have achieved for a customer.

Finally, Ryder can use transportation simulation to look at potential policy changes, and then ask customers if they agreed that the policy change would make sense. So, for example, if the customer can agree to a latter pick up time at a warehouse, that may create the opportunity to move some freight from parcel to less expensive less-than-truckload shipments. If policy changes will not affect service levels or require any changes in the way a customer’s operation are run, Ryder can implement cost savings changes without going through an approval process with the customer.

When shippers need to improve their transportation capabilities they have one of two primary paths they can take. They can buy and implement a TMS. Or they can work with a 3PL. One possible reason to work with the 3PL is that they may be able to get more out of that TMS solution than a shipper that implemented a TMS would be able to achieve.

ARC has recently completed a supplier selection guide for MTS. This guide include market share data based on a recently completed MTS global market study; the features, functions and service capabilities a shipper should be looking for from MTS providers; and ROI data on what kind of payback is common in these kinds of engagements. If you are interested in learning more about this guide, you can contact Conrad Hanf at chanf@arcweb.com.

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