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Trucking & IoT : Yes we can!




The Internet of Things (IoT) is certainly not a novel phenomenon to commercial trucking. Automated systems have been making their presence felt in this sector of the logistics industry since 2011, when Daimler Trucks North America (DTNA) initiated its ‘Detroit Virtual Technician’ reporting system and Scania introduced its ‘active connectivity’ package. Both these systems crossed 100K installations last year and there is every indication that systems with enhanced integration are in the works. The manifest success of systems provided by OEM’s, coupled with trusted vendors such as Intel throwing their hat in the ring, is accelerating the pace of IoT assimilation in this space. The demonstrated benefits associated with IoT enabled solutions are giving vendors pause to consider implementation scenarios, while novel applications are mushrooming everywhere. The fact that deploying these solutions could potentially reduce deadhead miles and improve the utilization of existing capacity, makes a default case for embracing them. The provision of factory-installed telematics and auxiliary data collection equipment by truck manufacturers is catalyzing the integration of stand-alone sensory nodes into connected networks of IoT enabled systems, across the board of processes associated with trucking. The maturing M2M communication and asset-tracking technologies would also be central in expediting the assimilation of IoT enabled data into larger industrial processes.


Today, IoT enabled applications have moved beyond the individual truck, to encompass coordination at the fleet-level for capitalizing on even greater fuel efficiencies. Furthermore, they also provide for optimization by managing externalities, such as relationships with key stakeholders and regulatory requirements. An example of the latter includes the selective bypassing of weighing and inspection stations. Another such application, known as platooning, creates mutually aligned fleet movements which provide for substantial savings through increased fuel efficiencies. The system devises a chain of trucks to dovetail into a convoy with coordinated movements, which are computer-assisted and somewhat akin to boxcars running on a railroad. This syncing of trucks is facilitated by selectively automated acceleration and braking, capturing efficiencies at scale all the while ensuring safety.

The adoption and integration of IoT systems by the trucking and the logistics industry, at large, is bound to be catalyzed by certain corners of the logistics industry which will serve as the guinea-pigs that provide first use-cases. Intermodal freight containers carrying valuable goods as well as food, medication or other products requiring refrigeration are some of the niches where early adoption is happening. This provides an opportunity for heavyweight incumbents to leverage such niches as sandboxes for piloting these capabilities and the case of UPS’s acquisition of Parcel Pro could turn in this direction.

There has also been a growing trend of startups targeting various individual services of logistics and transportation providers. The “unbundling effect” has indeed extended over the logistics industry! This includes on-demand shipments, crowd-sourced expedited deliveries, platform services providing integrated solutions for underserved customers (SMB’s, luxury providers, et cetera) and ancillary services (freight insurance, customs clearance, et cetera).

The case of trucking sector is particularly interesting because such technologies can be readily exported to groups of passenger cars and initiate the deployment of self-regulating vehicular fleets. There have been multiple such forays in developing semi-automated functions and applications, the impact of which could potentially even be extended to the larger ecosystem of human mobility. In the development of completely automated vehicles, greatly hyped advances have already been followed up with reality checks leading to mature applications. However, considerable challenges need to be tacked before these can be commercially deployed. It may be estimated through a cursory look at the trajectory of the current technologies that partially automated technologies would pave the way to self-guided applications in the future.


Internet has multiplied the number of gateways, not only for collecting but also for accessing data. This is bound to increase the efficiency of decision-making structures at multiple levels, enhancing the quality of decisions, boosting productivity and improving the extent of collaboration. This would also permit the extension of IoT systems for remotely controlling and managing freight, so as to effectuate the decisions. Besides, there is already a perceivable shift in the deployment of IoT, from merely tracking and data collection for effective decision-making to controlling for implementing those decisions. This would be further amplified through a data-driven relationship between various stakeholders in the supply chain. Further breakthroughs would be enabled by technological advancements that allow contextual availability of crucial information for expediting business decisions.


The maximization of benefits derived from IoT enabled systems would involve, not only the deployment of sensory nodes and on-board systems connected to the internet, but also the creation of unified processes that can connect technological implements and combine data streams in a seamless experience such that efficiencies can be captured, en masse. It would be important to remind ourselves, at this point that the essence of IoT systems is to increase the performance of larger human-centered processes and to offload some of the peripheral decision-making burden from the human actors. Therefore, the objective of such IoT enabled process integration would be to increase the productivity of human-centered processes. These are comprehensive solutions that integrate equipment and data into business processes, to create fluid meta-processes. Examples include solutions by OEM’s such as the Virtual Technician service by DTNA, as well as bespoke solutions, provided by external vendors and managed in-house, such as the IoT system developed by Intel and deployed by the fleet of Saia LTL. The factory-installed systems also allow a provision for creating value-add services that leverage these capabilities and scale them to provide for the enhancement of auxiliary processes. It creates considerable opportunities for cross-selling supplemental solutions.


The future of IoT applications in the trucking and, more broadly, in the auto industry would necessarily entail in the consolidation and harmonization of the various data-streams, through integrated service platforms that aim to reinforce and advance entire industrial processes. Such platforms will be leveraged to provide shared services to various functional mechanisms, including analysis, prioritization, communication, and decision-making, which will translate in enhanced efficiencies and leaner operations. Such optimization may be effectuated at various levels:-

1. Truck Level: At the individual truck level, productivity gains would mainly be achieved in the functional areas of fuel economy, safety, and vehicle maintenance.

2. Fleet Level: IoT has begun to penetrate the ecosystem at fleet-level, which would unlock even greater efficiency gains through scale.

3. Shipment Level: Far greater macro-level benefits may be realized from the integration of IoT applications throughout the supply-chain operations.

4. Client Level: Efficiency gains from all of these would eventually percolate to the client and result in better informed planning and coordination.

The data collected by sensors deployed throughout the truck is presently analyzed by centralized fleet management systems. But with time trucks are bound to dissolve into more extensive and accessible circuits that allows for even more distributed, localized and contextual utilization of data, permitting either or both, of analysis and communication. Moreover, as processes across the supply chain become more integrated, multilateral and cross-platform utilization of data would allow harvesting increasing amounts of value through these interactions. However, they would be effectively harnessed once the centralized IoT systems become more parsed and are contextually deployed. This would eventually allow for charting the movement of freight across the span of a supply chain. For instance, if off-the-shelf asset tracking systems could be integrated with newer or already deployed sensor-systems, it would translate into a giant step towards visualizing shipments throughout the supply-chain. As these systems evolve, it is highly likely that standalone capabilities would converge with others and be assimilated into broader faculties.

This visibility could be facilitated by platform providers that aggregate logistics and supply chain solutions and provide consolidated freight forwarding. Visibility of shipments is an important component as it would allow for more informed decision making, thereby optimizing business processes. If, for example, a time-sensitive shipment of medicaments for the treatment of an epidemic could be tracked, delays could be avoided through rerouting, backup shipments, etc. In the same vein, information facilitated through such platforms over the visibility of equipment (trucks) could even be leveraged to capture greater efficiencies in ancillary practices. An example would be turning virtual marketplaces for discovering excess capacity into optimized fleet operations, managed through IoT automation. Presently, market platforms that allow for pooling capabilities to optimize operations reply on voluntary information contributions. These could provide far greater opportunity if the process absorbs IoT applications to pull real time feeds from routes and positions, to provide information on schedules and predictive ETA’s. However, for all of this to be possible it is inevitable that the IoT systems be modular and the information streams be compatible.

In the future, even regulation and compliance functions could effectively leverage the platform capabilities of IoT enabled data stream. So, for instance, in automating the processes of this ecosystem, preexisting metrics such as the Compliance Safety Accountability (CSA) scores of the Federal Motor Carrier Safety Administration (FMCSA), are presently being utilized. Then, perhaps compliance could be directly monitored by pooling data from IoT enabled sensors and the regulatory standards revised.


The complications that remain for deploying IoT systems may be considered under three heads: the inevitable technical hindrances, those relating to the management and value-extraction, and those emerging from the industry itself.

There are also some considerable technical challenges which will only be tacked with technical advances, over time. One such challenge is that of the data deluge. For instance, almost 10MB of data is collected by an Intel gateway computing device from a network of sensors deployed in the truck, per mile. The effective utilization of such a vast amount of data would necessitate shrinking the capabilities of cognitive computing and advanced analytics so they may be accommodated into remotely deployable systems, for it is inevitable that some of the decision process would have to be delegated to algorithms. If the most desirable piece of information can be extracted from the data made available and transmitted to executives at critical moments, so as to enhance acute decisions, it would prove greatly advantageous. Another problem — common to all remotely installed IoT systems — is the provision of a power source to sensory and computational apparatus, which becomes more pronounced where trucks don’t come with factory-installed systems and need to be retrofitted. However, what has been accomplished, for instance, with solar charging for Orbcomm’s GT 1100-CTS chassis tracking system, might be effectuated in this regard using (if not solar) piezoelectric systems which take advantage of vibrations emanating from the truck’s chassis and suspension.

IoT enabled systems necessitate changes in the management’s decision-making frameworks to accommodate the information made available. These structures might need some tweaking for being able to work with a constant flood of data and, secondly, extract representative data-types from this dynamic barrage, for basing their judgment. While this may be facilitated by features built inside IoT or information-management platforms, onboarding would still be inescapable.

However, the most cumbersome hurdles are those which arise from challenges facing this industry. There are two main impediments facing the trucking industry, today. The recruiting, training, and retention of drivers continue to be the biggest issue, with expectations of the truckload rates to increase by as much as 12–18% (in the US market) to accommodate payroll increments. Added to this, the tightening capacity coupled with falling truckload miles and a host of other factors — such as ageing equipment, rising interest rates and the worsening regulatory situation — is poised to considerably increase the operating costs. Some industry executives have even suggested an increase of as much as 25% in the truckload rates (for the US market) over the next couple of years. These hurdles are bound to compound IoT adoption. For instance, drivers have already been reported to be reluctant participants in the implementation of IoT initiatives. Then, increasing costs and rising interest rates would make it difficult to purchase new equipment.


The trucking industry is poised to witness significant and consequential adjustments over multiple aspects, in the next few years. However, what seems surprising is that the level of preparation for accommodating these changes and profiting from these macro-level shifts is low, both within and outside the trucking sector. Even now, some lone bugles sounding misgivings about executives being caught off guard — as in the West-coast port crisis — can be clearly heard. Therefore, IoT solutions should give teeth to the trucking industry for tackling these difficulties. Systems that project near-term cost savings and enhance the quality of work-hours for the driver should fare well. Also, since the average age of class-8 trucks has far exceeded the peak reached in 2010, these would be nearing replacement sooner which makes a case for solution providers to explore arrangements with OEM’s.

The improvements in the productivity of trucking are poised to positively impact the entire supply chain ecosystem. Being the largest component of transportation costs, it is an important link in the supply chain ecosystem and changes herein would be reflected everywhere. The case of trucking is also interesting, for developments in this quarter could be reflected in the non-commercial motor vehicle segment. This would not only allow for fine tuning the applications, but also creating the infrastructure and capabilities required for them to be replicated in the personal vehicle space, thereby catalyzing rapid adoption.


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