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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.

Prominent Carrier Reduces Turnover by Using Predictive Analytics

Business Intelligence | By Steve Banker • 03/09/2017

Covenant Transportation Group, Inc. (CTG) is a $650 million freight transportation and logistics services company headquartered in Chattanooga, Tennessee. Three of its business units carry loads for shippers, but these unit’s business models were not exactly driver friendly. CTG does not have many dedicated opportunities in its largest Business unit leading to few regular routes. Two business units have operations that request drivers to stay on the road for at least 21 straight days. Another unit has an expediting service, which leads to deadline pressures. They also have their own Commercial Driver License Class A (big truck) training operations. Turnover for new drivers, who don’t fully understand the demands of this profession, is very high.

So it is perhaps not surprising that in 2012 CTG was slightly above than the industry norm in terms of driver turnover. According to Doug Schrier, the Vice President of Continuous Improvement and Program Management at CTG, that just was not good enough. Even though in many ways CTG’s operations are more demanding than other carrier groups, and the way they calculated turnover was more stringent than some other carriers – they include drivers they have let go for performance problems in their calculation – the company wanted to perform significantly better on this key performance indicator than the industry as a whole.

CTG started a predictive analytics project in 2012; “we learned a lot” Mr. Schrier said. “But while we began to understand the power of predictive analytics, we also realized our in-house capabilities were immature. We wanted to bring these capabilities inside and improve our maturity.” In 2014 they hired an expert, Chris Orban, who had been doing predictive analytics for over a decade.

The next decision involved which analytics vendor to use. They had several bids. Mr. Schrier said, “we chose SAP HANA because they had a good modeling tool.” In particular, they believed that SAP’s ability to rapidly develop analytic models was superior to the other suppliers.

The HANA project started in March of 2014, they had developed their first model by August. The main bottleneck was that in addition to deploying a new tool, they needed to develop a mechanism to deliver the task recommendations to managers.

“Each model is a bit different,” Mr. Schrier explained. “A model for a professional driver who has been on the job for 6 months is different from a model for a driver that has been on the job for five years. The model for someone who is home every other week is different than that for a driver who stays out for 21 days.”

There are a number of predictive factors considered. A few of the more powerful predictive factors include how frequently drivers run the same hours during the course of the day; how fully utilized the driver is; and the variability of driver pay over multiple weeks. “If a driver can’t predict what they will make next week, that adds stress.”

The company is also looking for inflection points. Changes in behavior can indicate an inflection point. For example, if a driver didn’t routinely take advances and then begins, this indicates a change in the drivers financial behaviors which could indicate that a driver is under stress. Stress leads to an increase likelihood of turnover. There are 100 to 200 attributes that have predictive power in the models, but thousands that are considered. “The power of HANA, is that a new factor can be considered and then computed that quickly.”

Safety issues are also a predictive factor for turnover; “the data off trucks is amazing” Mr. Schrier said. “Near misses and other critical events may indicate that a driver is not focused on their current task.” Safety issues lead to coaching by safety personnel and other management. “If the manager handles the coaching poorly, the driver is more likely to leave. It’s just like any other interaction, but potentially more stressful because of the adverse nature of the discussion. But safety factors are key. If a driver is not coachable on safety, we want them to leave anyway.”

The company also brings in external data. One example of this is that the CTG searches job boards for potential new hires. They sometimes discover that a driver they employ is looking for work.

When a model predicts that a driver may be thinking about leaving, there is an intervention event between the manager and the driver. “These are relatively simple,” Mr. Schrier explained. “We ask the manager to have a ‘what’s up, Jack’ conversation.” Day to day operations can be so busy, that many managers rarely have these types of conversations unless they are prompted to. “We encourage the fleet manager to spend 5 to 10 minutes on a call with the driver and build rapport. The manager needs to “see where they are at” and how explore how the company could better assist them. For example, if the driver has a sick child, they may need to spend more time at home with that child. Another driver may be experiencing financial pressure, advances or more predictable schedules might help that driver.

What are the results of this program? Despite trucking operations that are more challenging than many of their peers, CTG’s job turnover is 5 to 8 percent lower than when starting the program.

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.

Enhancements in SAP Production Planning and Scheduling and Future Integration with ERP

Supply Chain Optimization | By Valentijn de Leeuw • 07/04/2016

At the SAP Insider SCM CRM 2016 event in Vienna this month, Ulrich Mast presented the current status and the planned future for Production Planning and Detailed Scheduling (PPDS), including SAP Advanced Planner and Optimizer PPDS (SAP APO PPDS) within the SAP Supply Chain Management suite (SCM) and S/4HANA .

Smaller lot sizes, increasing variability and shorter product life cycles tend to make planning and scheduling more complex, resulting in increased order lead times, stock levels and work in progress. Responsive supply chain networks can compensate for these effects, in particular by more tightly integrating engineering with supply chain and manufacturing and provide real world feedback on supply chain performance to planning and scheduling. This shortens time to manufacturing and provides the opportunity to reschedule continuously based on actual manufacturing progress. Industry specific planning methodologies such as Kanban, Lean or product wheels also help manufacturers to be more responsive and efficient. Key according to Mr. Mast, and we agree with him, is the ability to:

plan and schedule taking detailed ‘real-world’ resource constraints into account,
apply exceptions to rules for specific products or resources to cater for personalization and specific order needs,
having the capability to analyze causes of delays or shortages,
adapt scheduling continuously to reflect actual demand and plant conditions, and reconcile schedules to meet priorities,
provide optimized plans and schedules to maximize efficiency

Workflows and data flows are currently seamless between ERP and SCM. Some recent enhancements are simpler navigation and embedded analytics provided in side panels, supporting faster and improved decision-making. SAP APO is available on SAP HANA since November 2012, accelerating end-to-end processes by on average 40% to 50%, and providing synergies with APO operational analytics deployed on the same SAP HANA server.

In many customer cases, unconstrained Materials and Resource Planning (MRP) is used alongside constraint-based planning and scheduling (PPDS), many customers run two systems sequentially. SAP presented plans to embed PPDS in S/4HANA on a single shared platform, therefore having the capability to harmonise a single MRP run providing ease of use, efficiency and time benefits. Currently this option is available for ERP (SAP Business Suite on HANA) with PPDS embedded on SAP HANA platform. Other major benefits are that “material masters” and “resource maintenance” come from the same source and integration is significantly simplified. Also presented were other improvements to usability such as new production scheduling boards based on SAP Fiori

ARC Advisory Group believes that recent enhancements in PPDS and the embedding of PPDS in SAP Business Suite on HANA with and in future in SAP S/4HANA will deliver benefits in terms of cost and efficiency, but also in terms of value. We think of improved decision quality, improved responsiveness, and improved planning and scheduling accuracy. ARC Advisory Group believes that recent enhancements in PPDS and the embedding of PPDS in ERP on SAP HANA will deliver benefits in terms of cost and efficiency, but also in terms of value. We think of improved decision quality, improved responsiveness, and improved planning and scheduling accuracy.

A Most Impressive IoT Project

Logistics Technologies | By Steve Banker • 05/19/2016

The Hamburg Port Authority (HPA) has engaged in the most impressive Internet of Things (IoT) project that the logistics industry has seen. And their work is not yet done.

Sascha Westermann, the Head of ITS and Intermodal Traffic Management, spoke at SAPinsider on March 21^st about HPA’s progress to date and future plans.

The Port of Hamburg is the second busiest container port in Europe, and is a key trade lane connecting Eastern Europe to Germany and the rest of the world. The HPA is in charge of providing efficient infrastructure in the port area: managing the real estate; making sure quay walls, bridges, wharves and other waterfront structures are maintained, maintaining 140 kilometers of roads and 130 bridges within the port area, and managing the traffic – across ocean, barge, rail, and truck – into and out of the port. Other parts of the infrastructure, like the 4 big container terminals, are managed by industrial companies.

Over 9 million twenty-foot equivalent units (TEUs) are transported through the port every year, and this is forecast to double by 2025. But the Hamburg Port is located in an urban area and they cannot increase the size of the port to handle increased traffic. To handle shipments efficiently, the HPA must operate smarter. Better traffic management also helps to minimize pollution. The port and city are impacted by up to 40,000 truck trips daily and no one wants those trucks spending a high amount of time idling.

To help keep traffic flowing smoothly, the HPA worked with big companies like SAP on a series of 20 projects collectively called smartPort Logistics. This work began five years ago. The SAP HANA Cloud Platform was used to enable a real-time connection to the port’s various stakeholders through a mobile business cloud. Those stakeholders include: the HPA itself; shippers; trucking, rail, and ocean carriers; customs authorities; terminals; contract warehouses; parking providers; and other businesses across the port. People, processes, and things data is then consolidated and analyzed using the SAP HANA Cloud Platform. The process data is based on an extended supply chain view that begins with a shipment from another port and can extend to inbound truck drivers hundreds of kilometers outside of Hamburg. Based on the analysis, HPA provides various types of schedules, tasks, and insights to the people who need it via their mobile devices.

Under the banner of smartPort logistics they have predictive and preventive maintenance capabilities. Mr. Westermann gave the example of a movable raise bridge that had a malfunction of a push button that raised and lowered the bridge. A maintenance technician got an emergency alert, was able to use his mobile tablet to search for the part in the SAP maintenance application and requisition it, and was then able to drive to the site and replace the part before any of the port’s customers even knew there was a problem. In some cases, maintenance can be based on sensor information. The Port Authority has sensors on their most intensely used rail switches, for example, and is experimenting with sensors that measure the structural strain and tension of a bridge.

Another smartPort application is known as the Port Monitor and is used for vessel traffic services (VTS). VTS is a marine traffic monitoring system similar to air traffic control for aircraft. VTS makes use of digitals maps combined with AIS data on the positions of vessels, and data on sea levels, berths, whether bridges are closed or open, and where construction is occurring.

Just as there is a control room for managing waterborne traffic, there are control rooms used to manage traffic on the railways, and roads, as well as for managing the movable infrastructure. About 300 traffic sensors and 270 kilometers of fiber optics feed data into these control rooms. Eventually these separate control rooms will be merged into one virtual control room to increase synergies.

The road traffic control room also pulls various streams of data to predict travel time into terminals and suggests alternative routes when traffic is heavy. In addition to traffic sensors, data sources include the schedule for bridges being raised and lowered, the situation at the container terminals, and other sources as well. Truckers can view this data on their telematics devices.

Telematics Screen Shots

Trucks are not allowed to enter the container terminals unless they have an appointment for a slot. If they will be late for their appointment, they must see if they can get a new slot or whether they must exit the port and wait until a new slot can be booked. Once entering the yard, very often a truck will be directed to a marshalling area to park bumper to bumper until traffic eases. This prevents idling. Smart parking capabilities direct trucks to lots with available space.

Smart traffic lights make sure a platoon of trucks get an extended green light through an intersection, while the smaller number of trucks on the cross street must wait. If pedestrians are crossing an intersection when the light is red, a safety warning message is sent to the vehicle to let them know of the danger associated with trying to beat the light. HPA is experimenting with functionality that will automatically increase street lighting if pedestrians or cyclists are present; and video streams that help to predict potential traffic accidents, for example, if a vehicle is stopped even though there is no traffic jam.

In concluding the presentation, Mr. Westermann made a variety of points. Slot management is key to increasing efficiencies and reducing pollution, but it even works better with the ability to pull the wide variety of data sources together. These data sources have included information from competing stakeholders, and role-based visibility had to be constructed in a way that preserved privacy and competitive information while providing useful information to all stakeholders. Much work went into creating a simple user interface, but nevertheless there have still been issues associated with getting truck drivers and other stakeholders to efficiently use the information that is available. And finally, the worse the traffic conditions, the higher the value this system provides.

Is this really the logistics industry’s most impressive IoT project? Probably. It is certainly the most holistic use of data sources fed to a single platform for the purpose of increasing logistics efficiencies that ARC has ever come across.

A Fresh Look at Supply Chain Visibility

Cloud/Software-as-a-Service | By Steve Banker • 04/11/2016

In ARC‘s recent Supply Chain Visibility & Collaboration Study, we found that the supply chain visibility & collaboration market is the fastest growing supply chain market. Over a year ago, ARC started to come across large corporations investing in supply chain control towers as a way of taking their supply chain program to the next level. We decided we needed to look into this, and kicked off a market research project on this market.

When it came to supply chain collaboration, we defined this as having a collaborative, multi-supplier, multi-tier, supply chain planning solution. For visibility, the definition was a solution providing visibility to the order process (order placed, order adjusted, order accepted), inventory at rest or inventory in motion events, or supply chain risks, or providing a stand-alone supply chain analytics/event management solution not embedded in other solutions.

Based on our definitions, solutions built with different tech stacks – BI, optimization, and B2B networks – were involved in providing different types of visibility and collaboration solutions. But in particular, it was B2B network solutions – both for visibility and collaboration – that was driving this market’s very rapid growth.

What are the reasons for this? Well there can be a very good ROI associated with visibility projects. The primary way Supply Chain Planning (SCP) applications drive ROI is through improving service levels with less inventory. The more companies can reduce their lead times, and the less variability there is surrounding those lead times, the better the ROI that can be achieved with SCP tools. Supply Chain Visibility, provides the data and metrics surrounding lead-times, and then allows Lean practitioners to work with supply chain partners to improve and control those lead-times.

Much of the traffic flowing through B2B networks is either initiated by supply chain masters or mandated. Many of these very large multinational corporations work to tightly orchestrate their end-to-end supply chain from suppliers all the way out to customers. Without good visibility, tight orchestration is impossible. Further, the more outsourcing – use of contract manufacturers and 3PLs – the more valuable collaborative planning applications are.

Supply Chain Synchronization

But beyond visibility is supply chain orchestration. Orchestration takes the service improvements and inventory reductions to the next level. In this scenario, balancing supply and demand is not merely improved through faster lead times, but rather the supply demand matching process is actively and collaboratively managed on an ongoing basis. Improved supply-demand balancing also leads to increased revenues by reacting to surges in demand more quickly than a company would be able to do based on a traditional sales & operations planning process. It also helps companies react more nimbly to supply disruptions, helping to deliver on orders that have already been booked but which are now in jeopardy.

And ARC thinks this market will continue to grow quickly because the concept of a “network of networks” is being pursued by both SAP and Oracle. In short, just as companies with very strong supply chain capabilities are looking closely at this space, so are some of the largest supply chain software suppliers.

The supply chain product set and visibility roadmaps of SAP and Oracle are strikingly similar. SAP and Oracle are not only leading suppliers of supply chain solutions, through partnerships they exert additional influence on all supply chain software markets. Their product sets and vision matter.

Both SAP and Oracle recognize that supply chain synchronization capabilities are key for success in this market. Both suppliers offer a variety of planning and execution solutions, many of them with best of breed style functionality. Both have developed collaborative planning solutions (SAP is earlier in their journey) that are well adapted to companies with a large degree of outsourcing in their supply chains. Both 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. Both have very fast in-process memory technologies that allow for simulation and predictive analytics. Both have cockpits that allow the sales & operations planning process to be effectively managed. And both are seeking to go beyond inward looking S&OP to more collaborative planning environments built upon better visibility.

To accomplish this, both have partnered or procured providers of B2B networks. Yet, both suppliers recognize that no one B2B network provides all the visibility necessary. Descartes, for example, is very good for Air visibility, while Transporean has robust visibility into European road transportation. So to take supply chain orchestration to the next level, SAP and Oracle will need to collaborate with a variety of B2B network providers. Both recognize that effective integration to those networks will depend upon semantic integration; in other words, a transport order as defined in Oracle will need to be mapped to the same concept in the electronic messages sent through the Descartes network to insure that apples are not being confounded with oranges. Both SAP and Oracle recognize that semantic integration is difficult and admit they are early in this journey. And in both cases the vision is that they will offer a Network of Networks visibility solution, they will provide the visibility layer that rides on top of all other BWB networks. Both Oracle and SAP are well positioned to achieve this.

A complete analysis of logistics trends, technologies, and services

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