Virtual Watch Towers for supply chain visibility

(Illustration: Sandra Haraldson)

Cargo owners and transport buyers would like to have enhanced visibility and forecasts on the times of goods and (returnable) assets arriving at various locations in the global end-to-end multi-tiered supply chain network. Logistics service providers also need to stay informed on progress and disruptions on the activities related to their cargo.

Carriers can benefit from visibility before and after their activity. Given the vast amount of data that now are becoming released through diverse data streams there lies great opportunity to achieve enhanced situational awareness for the involved parties and the clients of multi-modal transport chains.

Virtual watch towers are pieces of software that pull data together for analysis to create business value, like the location of assets and goods, or drive higher on-time arrival compliance. The term ‘tower’ is often used to indicate an overview of operations for a defined scope providing the means to act in a smart way. Although the virtual tower is not a physical building overseeing operations in the sky like the surveillance towers at airports, the purpose is similar.

A virtual tower is a piece or suite of software used to provide situational awareness. In the case of the virtual value chains watch tower, such situational awareness is focused on end-to-end visibility built on the large amounts of data that is gathered from numerous feeds and which is analysed continuously to identify delays and risks, find and prioritise mitigating measures and empower people in the organisation to avoid disruption and ensure compliance. The virtual watch tower is a digital agent that uses descriptive, predictive, and prescriptive analytics for enhanced decision making and exception management. It also orchestrates the heterogenous data streams, deals with organisational issues and ensures the operational business alignment of different actors toward supply chain goals.    

The purpose of a watch tower is to provide a greater level of knowledge about the entire picture and specific events to every link of the supply chain. Virtual watch towers use cloud-based intelligence and analytics to not only detect risks and variances to plan but derive the best course of action to mitigate the identified risks or correct deviations and prevent them from happening again. The revolutionary push from reactive to proactive behavior is at the heart of the virtual watch tower.  

Towers for the transport industry

During the last decades as digital technologies like cloud, blockchain etc. have become more advanced and widely deployed, more virtual approaches to tower capabilities have been established, with development accelerating recently. Within road, rail, and sea transport we have been seeing both public and private initiatives establish situational awareness of operations within a particular geographical area, for a transport hub, and/or a particular corridor or fleet.

Other initiatives taken have improve maritime cargo track and trace capabilities, with platforms like Tradelens. Other insight providers with varying focus and approaches are Blume, FourKites, Project44, Roambee and Tive. They all are building upon enhanced digital connectivity and big data. 

Different types of supply chain control towers have emerged over the years. A first generation of such towers were brought to use in the 1990’s which were limited to logistics operations, and were more reactive than proactive, only working on normalising operations when there was a disruption that someone spotted. Those however suffered from a lack of verifiability and connectivity across the supply chain.

The second generation were aimed at more operational control and provided near real-time data on fleet locations, inventory levels, as well as custom alerts when exceptions or anything that affects overall supply chain performance occurred. This generation of supply chain control towers suffered from a reliance on manual processes however, as well as a lack of end-to-end and multi-tiered perspective, uniformity, and information overload.

The third generation introduced an additional focus on the end-to-end logistics and supply chain. That generation however suffered from lack of adequate or uniform infrastructure (technology, connectivity), the need for manual intervention for exception handling, and shortcomings in prescriptive analytics along the entire value chain of a company. The fourth generation puts more emphasis on automation and better analytic capabilities.

It is important is to distinguish between the different types of control towers, like logistics/transportation control towers, fulfilment control towers, inventory control towers, supply chain assurance control towers and end-to-end (E2E) value chain control towers. Some are custom-built by large enterprises, others are provided by vendors, for example forwarders, to act on behalf of their customers based on standard operating procedures (SOPs). This article looks at logistics/transportation control towers.

As supply chains are global, there is a need for a framework that is holistic in nature and that guides the developments of watch towers throughout the world. We use the concept of “watch” tower, rather than “control” tower, to stress the importance of the need to build smart decisions on rich sets of data, creating situational awareness.

Foundational model for the global supply chain “watch tower”

The proposed underlying model for the virtual watch tower builds upon a digital agent that detects actual or future supply chain disruptions. In its processing, the agent takes numerous data streams into consideration, cleanses the data to detect the transports/transport or upstream and downstream value chains that have been negatively impacted and/or are likely to be disrupted in relation to the planned transports and transports being conducted.

On the flip side, the watch tower will not identify those activities that are expected to be pursued as planned with a high degree of probability. Fundamentally, the watch tower therefore builds upon data covering planned times, estimated times, and actual times, i.e. plan vs. progress and plan vs. anticipated progress.

As digitalisation allows us to detect multiple instances and calculate multiple outcomes, emerging choke points can be identified in advance, as e.g. congestions occurring at particular transport nodes, like ports and terminals, can be detected hours, days or even weeks in advance, given that it would be possible to achieve situational awareness through analysing vessel data or running simulations of how many transports are expected to be served at the same time at a particular node. Of course, this may need some time to reach a high level of accuracy.

Once the digital agent has detected a potential variance to plan, the engine can calculate alternative options which the agent can prioritise to prescribe the one with the most promising outcome based on pre-set parameters or rules, e.g. on-time and in-full (OTIF) delivery at minimal costs. The agent triggers action, either through an automated process or by guiding people through the distribution of deviation and risk alerts and identified best possible alternatives.

The model that guides the composition of the watch tower is divided into four activity components; aggregate, alert, analyse, and act (figure 1). Within these components, the digital agent would pursue most of the tasks, while humans would instead monitor and execute on the recommended options. The human influence on the process should be limited to validating conformity with the rules, ensuring proper system performance, and to factor in last-minute information.

Figure 1: Fundamental model for the watch tower

The model uses the current transport plan for one actor covering its transports within the scope of interest. The plan can cover a particular piece of a multi-modal and multi-tiered value chain or the whole chain dependent on the scope of the operations that the actor is engaged in.

An example could be a fleet operating center of a shipping company that may be concerned with transports from one port to another, but it may also concern a transport buyer requesting a multi-modal transport. Or it could be about the entire value chain of a certain company, covering a network of upstream suppliers and the transport legs in-between.

The transport plan should both capture data about the cargo being transported, the points and timing of departure and destination, and the routes that are planned to be taken by the logistics service provider, since possible decisions on re-routing and mitigating actions need to take the characteristics of the cargo and the original plan into consideration. The transport plan is dynamic in the sense that it both continually captures new planned transports and also reflects decisions of changes, and also new suppliers in the extended network.

The different action components of the model can be unpacked in the following aspects;

  • The aggregate component takes the maximum number of data feeds into consideration that may be of relevance for the transports depicted in the transport plan and supplier network. This component also entails the cleansing of the data through artificial intelligence/machine learning (AI/ML) features.
  • The alert component continuously scans the cleansed “watch” data and produces signals that point to potential disruptions or deviations with potential impact on the transport plan. This component uses thresholds, such as temperature ranges or an expected arrival time at an intermediary transport node.
  • The analyse component produces for each signal, with the help of AI/ML, a list of alternative options (if available) that could be adopted according to known parameters, e.g. shifting to a truck instead of using the planned train to ensure compliance with the transport plan.
  • The act component is an operational function that provides machines or humans with the agent’s findings to take effective measures, such as re-routing a truck or selecting an alternative parts supplier.

Different digital components are reflected in contemporary solutions such as control tower functions, as e.g. adopted by Maersk, focusing on data provision and dashboards as foundations from which customers can optimise their supply chain to the next level, or solutions for distributed data collection and aggregation, such as The Flex pulse mobile solution populating situational awareness for the Flex situation room facilitating collaborative problem solving among the operators along the supply chain.

The watch tower is a continual model of which actions taken in one cycle constitute the new plan for the second cycle etc. The model also takes new transport assignments into consideration through which it becomes continual.

The digital agent is continuously evaluating the transport plan and when thresholds are reached it identifies alternative options and recommends those that improve the outcome. This is closely linked to procedures of sequential decision-making which continuously evaluate the situation the further one gets into the transport chain, i.e. decisions are improving with the growing amount of information. This would mean that the model of aggregate-alert-analyse-act would be pursued iteratively (figure 2).

Figure 2: Iterations of cycles as foundational for the watch tower

An emerging landscape of data streams

We expect an exponential growth of data streams. The watch tower builds upon that the “aggregate” component is fed by numerous data sources, like internal systems and external data sources. Data can stem from in-house or partner systems or from connected devices. Video streams can provide node intelligence capturing data streams that provide real-time context on what is happening at certain nodes.

We need to distinguish direct data gathered first-hand, e.g. sensor and video data, and indirect data streams, for example milestone-based check-ins. Direct data is more granular than indirect data.

Complementary data sources may capture the same thing, such as the bridge system of a ship providing data on position, heading, and planned time of arrival but also internet of things (IoT) sensitised containers being onboard the same ship providing data on its position. The development of VDES satellites is going to foster the full connection of vessels across the world. There are also a multitude of radio frequency identification (RFID) readers in use e.g. on railroad trucks in Europe to allow for real-time updates when a train wagon passes a particular location.

Computer processing capabilities now allow event data to be generated from video streams, such as a truck entering a transport node zone or a crane operating containers, as in the case of the Kalmar ONE solution providing event data based on camera data.

An obvious data source within the maritime supply chain is automatic identification systems (AIS) that may be used as a foundation for generating event data to be shared, but we will also see new data streams emerging from the detection of e.g. container ID’s captured by video streams and analysed through computer vision techniques.

Monitoring of online news, governmental agencies, weather stations, social media and other institutions can create external event-based data indicating a heightened risk of delay in a particular node of the supply chain. External event data informs about natural disasters, weather, strikes, bankruptcies, pandemic/health or even war. Finally, watch towers can leverage AI models that detail the multitiered aspects of value chains in order to address potential bottlenecks upstream in supply chains and thereby gain valuable time.

Situational awareness may become populated with multiple data sources allowing the virtual watch tower to operate in the most effective way.

Interacting watch towers providing situational awareness for all

This foundational model for watch tower functionalities could be adopted by stakeholders across the global supply chain network creating a network of virtual watch towers exchanging information between each other. One watch tower sits on pieces of data that are a useful data stream for other watch towers allowing them to identify, mitigate and act upon risks and deviations from plan beyond their scope of operation.

When there are multiple virtual watch towers along the same supply chain the value can be increased, by providing data for broader analysis that would highlight more risks and deviations than one single tower

The result is a community of prosumers of data, as everyone that establishes a virtual watch tower would be both producers and consumers of data.

Connecting ports is not new. The Singapore port for example is linked to 600 ports in over 120 countries. But connecting watch towers would be an innovation. There are also initiatives taken by e.g. the Digital Transport Logistic Forum (DTLF), the FEDeRATED project, the International Port Community Association (IPCSA) to facilitate the distribution of data between local data sharing communities, and now by the US department of Transportation responding to supply chain challenges with increased data sharing.

Concluding words

Empowering supply and value chain visibility with logistics/transportation and other types of watch towers is made possible due to ever-increasing computing power and distribution of data across the globe. People are becoming more connected, which also allows people using mobile devices to feed systems with data about what they see and achieve, while they can also receive recommendations from virtual watch towers to act upon on the same devices.

In this article we propose a network of logistics/transportation watch towers to meet the demands of 1) cargo owners and transport buyers to get more insights into cargo progress and how disruptions are managed that may affect the arrival of their goods, and 2) enhanced situational awareness for logistics service providers allowing cargo flows to be managed more efficiently. While carriers themselves also benefit from enhanced E2E visibility they may face questions from their customers about some routing decisions that are made visible through the higher level of transparency.

Watch towers empower just-in-time operations across the supply chain optimising on time, space, and resources as well as optimising for sustainable operations with the smallest amount of CO2 emissions possible.

Shipping is global and digitalisation enables seamless interaction across the globe. As more and more data streams are made available and more sophisticated filtering and analytic tools are developed, proactive management of the supply chain is increasingly possible. Connecting watch towers for supply chain visibility in a network of smart centres around the world would lift the concept of control towers to a totally different level.

The authors invite practitioners and academics to provide their views on the topic and ideas how this promising concept can be brought to life.

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About the Authors

Mikael Lind, Research Institutes of Sweden (RISE) and Chalmers University of Technology

Wolfgang Lehmacher, Anchor Group

Sandra Haraldson, RISE

André Simha, MSC

Tobias Larsson, Altana

Berit Hagerstrand-Avall, Stora Enso

Magnus Lyrberg, RISE

Phanthian Zuesongdham, Hamburg Port Authority

Scott Hurley, Roambee

Xiuju Fu, A*Star/IHPC

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