Just as production processes have become increasingly automated, so have logistics practices, largely due to the creation of increasingly advanced TMS, which can unlock new levels of efficiency, improve opportunities for automation and data consistency, and also offer both savings and process improvement.
The demand for transportation management systems (TMS), a basic logistics platform that comes under the subset of supply chain management, is on the rise. The primary function of a TMS is to manage truck and carrier dispatch, fleet maintenance, driver records, billing and driver pay, and DOT compliance. Additionally, a TMS streamlines operations by optimising carrier routes, managing customer activity, and reporting analytics.
For most multi-channel businesses, overhead shipping cost now exceeds even essential costs such as direct and indirect payroll or utilities. Whether a business has complex logistics requirements or basic transportation needs, its bottom line is affected greatly.
Moreover, as the logistics and transportation sectors continue to grow, the need for faster, smarter, and more efficient shipping services becomes crucial, which is expected to make a significant contribution to the growth of the market. By 2020, same-day delivery will account for up to 50% increase in logistics costs as a percentage of revenue. Hence, companies are now looking for every opportunity to keep the increasing shipping costs in check, such that overall service quality is not compromised.
Transportation management systems have a proven ROI. Primarily, a TMS can save companies money by lowering their freight spend. According to Logistics Management, most users experience about 5 to 10% freight cost reductions after implementing TMS, with the higher end of the scale increasing by at least 2 percentage points.
New technologies such as Artificial Intelligence (AI) and Blockchain are significantly contributing to TMS. Companies are making huge investments in their technological R&D, to improve payment processing, freight location monitoring, and order fulfilment. With supply chains grappling to keep up with the Amazon Effect, the choice of an effective TMS is the order of the day.
The primary reason companies buy a TMS is for freight savings. These freight savings can be attributed to simulation and network design, load consolidation and lower cost mode selections, and multi-stop route optimisation.
Many companies also buy a TMS to maintain or improve their service levels. TMS maintains the service levels by understanding the origin to destination lead times and using that as a constraint during the optimisation run. There are also analytics associated with the system. For example, a shipper can analyse which carriers are too often late, and which lanes and destinations often receive late shipments.
All important functions, but limited in scope for sure. At least, that’s the way TMS has been. But Machine Learning (ML) promises to allow us to go deeper, and to capture nonobvious tradeoffs.
Sometime back, the division of CH Robinson that provides managed transportation services- TMC published a white paper ‘Multi-stop Trucking: How It Affects Load Acceptance and Pricing’. What it shows is that for multi-stop truckloads, every additional stop lowers the on-time delivery level.
Multi-stop loads are different…The more stops there are, the worse the on-time delivery percentage if one of the early pickups is delayed or late… Trucks that picked up late on a three-stop load, for example, averaged on time delivery only 71 per cent of the time. On single-stop truckload shipments, 80 per cent of the load picked up late is still delivered on time.
Traditional on-premise TMS solutions will calculate the many situations where multi-stop loads save money. The TMS understands the lead times. It assumes the lead times will be adhered to and the loads will be delivered on time. But the TMS does not show in-line analytics to a planner. For example, “if you go forward with this shipment, there is only an X per cent chance the last customer on the route will receive their load on time.” Traditional TMS solutions are just not built in a way where these kinds of relationships can be discovered and easily acted upon.
But, network-based TMSs are rich in data. ML depends on Big Data sets. The problem above would probably be solved using supervised learning. In supervised learning, the system is provided with a raw data set but then also provided with a target. In this case, the system is asked to predict on-time deliveries (OTDs) based on a variety of data inputs.
“The time a job takes to complete is dependent not just on the miles that need to be driven, but on the congestion, the product being delivered, the type of delivery destination and value-added services. ML can be used to ‘learn’ the type of constraints mentioned above, rather than having to do time studies and hard code these constraints into the solution,” says Chris Cunnane, Research Director- Supply Chain Managemen at ARC Advisory Group.
“Part of TMS’ expansion into the planning process is last-mile routing. ML and AI will be very important here” adds Cunnane.
A key player in the development of TMS’ expanding capabilities is the Cloud.
The older-style TMS platform is a software solution that requires installation on individual systems or a physical server. The Cloud-based TMS, on the other hand, relies on a Cloud-based storage system and solutions are offered via subscription-based services (software as a service – SaaS). Essentially, it resides on a single or multiple redundant servers. Each of these servers is what is referred to as a virtual serve. TMS, in fact, were one of the first execution-based supply chain applications to move to the Cloud. Cloud and SaaS offerings make TMS more accessible and affordable to a larger population of companies – including the medium- to small-sized organisations.
The Cloud speeds implementation, eliminates hardware compatibility challenges, and allows for easier communication and collaboration among carriers and trading partners.
Prahlad Tanwar, Partner and Logistics expert at KPMG adds, “Among the various transformations technology has been enabling in the logistics sector, the most apparent in recent times have been the ability to enable platforms that enhance connectivity between a fragmented logistics services community and end users, and enhanced visibility and accountability across supply chain stakeholders resulting in operational efficiencies and reduced costs.”
Most importantly, cloud technology prompted dramatic drops in the cost of many TMS solutions. Shippers no longer have to invest in software licenses and hardware, nor mammoth installation projects.
Cloud also makes it possible to integrate TMS, not just with complementary software solutions but with next-gen technologies from AI to IoT. The outcome, Cunnane says, is smarter systems, improved visibility, an increased use of sensors for real-time asset tracking, temperature controls and other metrics.
Distribution and fulfilment centers depend on Cloud-based TMS for their inbound and outbound freight scheduling. Having access to this ability at all times, no matter where you are physically located is crucial. And that’s exactly the sort of thing that only a Cloud-based TMS can provide!
Cloud systems also do more. They not only automate shipment planning and execution, but sophisticated algorithms also allow them to optimise shipments across modes and routes.
Fast-forward to 2020, the walls preventing small- to mid-sized companies from realising the benefits of TMS will no longer exist.
Visibility is increasingly becoming a big deal with TMS. According to Geoffrey Milsom, Vice President at enVista, 50 to 60% of shippers and 3PLs use TMS today. But less than 10% of those TMS users have an external or third-party visibility software application integrated into their TMS for real-time visibility.
However, now that’s changing. “Visibility has exploded over the last 12 months,” notes Bart De Muynck, Vice President- Transportation Technology, Gartner in a recent article. “Companies want the same kind of visibility and shipment tracking they get as consumers shopping on Amazon. More and more TMS providers are building that into their solutions for B2B applications.”
Positioned between a shipper’s enterprise resource planning system (ERP) or other legacy solutions and its warehouse management system (WMS), TMS automates the inbound and outbound shipping processes. However, the savings on inbound shipments is larger than those on outbound.
Charged with obtaining the best possible mode at the lowest possible price, TMS also handles the execution process (i.e., rate acceptance, carrier dispatch, etc.) and provides visibility into the shipping process (shipping from point A, arrival at Point B, Customs clearance, and so on).
Historically, suppliers—not customers—have established relationships with carriers. As a result, client companies often have little visibility to cost-savings opportunities. For instance, the company could be using suppliers located within one region that all ship less-than-truckload (LTL). TMS can identify opportunities to consolidate shipments to reduce costs and cycle time.
This often requires reengineering processes. While the supply chain or warehouse team usually controls outbound shipments, purchasing typically controls inbound shipments. For simplicity, many products arrive with freight already rolled into their costs. Changing this process will alter buying habits and negotiations with suppliers.
Augmenting the last-mile
As companies push to position their fulfilment and warehousing operations as close to their customers as possible, TMS is helping them achieve that goal, driven by Cloud-based platforms.
“The last-mile is definitely an area of interest for a lot of shippers due to its dynamic nature,” says Amit Sethi, Senior Manager for Logistics and Supply Chain at Capgemini. “A lot of shippers have already ‘been there, done that’ in terms of getting the cost savings out of the long-haul aspect of transportation, or those shipments moving more than 300 miles.”
Also piquing shipper’s interest right now are the metrics, analytics and key performance indicator (KPIs) that a modern TMS can provide. That data can be used to make better decisions. In essence, a TMS paints a KPI/metric-based picture of the entire end-to-end supply chain, Sethi says, all while sitting outside of the four walls of the enterprise and warehouse. It also interacts with trading partners in the value chain—vendors, customers, freight providers and carriers—thus creating a complete loop that logistics professional can turn to as a reliable source of data and intelligence.
For example, while TMS has historically centered on batching and tracking shipments at certain times throughout the day, running a successful last-mile operation demands 24/7 optimisation, with feedback coming from drivers on an almost real-time basis. Then, based on that feedback, the TMS is being asked to optimise not in batch mode, but on an in-line, constant, 24×7 basis.
Going forward, Sethi expects to see more innovation on the vendor side and higher TMS adoption rates on the shipper side.
A software segment that was once dominated by larger vendors like SAP, JDA, and Oracle, TMS has become a hotbed for innovation by smaller, Cloud-based providers that have opened their platforms up to a larger group of shippers.
The change has come just in time, as globalisation and e-commerce are opening new markets to small and medium-sized businesses (SMBs), allowing them to work with suppliers and customers around the world.
Until now, SMBs that were unable to afford or maintain an enterprise TMS platform were faced with a stark choice- either manage their operations with a rudimentary in-house TMS or partner with a third-party logistics (3PL) service provider that could provide one to them.
For example, many small shippers are choosing TMS products that are offered on a software-as-a-service (SaaS) basis, subscribing to the cloud-based software for a relatively modest monthly fee rather than footing the cost of on-premise computing equipment and an IT staff.
The TMS capabilities of these smaller players are pretty comprehensive—to the point where they could take over some of the market share that larger TMS providers held in the past.
Industry analysts say that the Cloud is pushing both sides of the equation. It’s making TMS more accessible to more shippers while also creating a ‘sandbox’ of sorts for technology vendors looking to develop Cloud-based supply chain systems. In most cases, those solutions are more affordable than earlier, on-premise options. This makes TMS more attractive to shippers that have been reluctant to surrender their manual, paper-, and phone call-based transportation management approaches.
Today, we are also seeing predictive analytics embedded in TMS versus just being ‘bolted onto’ an existing solution. They’re getting creative and making an all-in-one solution. A lot of companies don’t like the idea of implementing TMS and then having to add additional services at a later date. The vendors that are embedding analytics and other capabilities are differentiating themselves from some of the other options on the market.
Advancing technology will continue to impact the TMS market. The next several years should bring greater deployment of the IoT, predictive analytics, and mobility. These advances will create an environment in which technology allows us to automate processes, proactively flag supply chain disruptions, and better track and trace shipments.