IoT data integration platform for supply chain companies


< Project overview >


The lack of shared information across companies in an international supply chain causes inefficiencies. As products move from the supplier, via logistics partners, to the final destination they can be handled by multiple companies, each of which will face potential disruptions due to a range of issues, such as incorrect packing lists, customs delays, routes changes and delays for vessels and trucks to name a few. Consequently, companies have to adjust their plans on the fly, often allocating extra time and resources in case of unforeseen disruptions.

This project’s vision is to enable the information sharing between partners across an international supply chain, including operational and IoT data.

Project aims

The main aim of this project is to build a prototype of the IoT data integration platform that serves as a single source of up-to-date information for all the players in the supply chain, and enables dynamic decision making. An important derived aim is to depict the process map of an international supply chain to identify the main points when the players interact and share information.

The barriers to IoT adoption that are targeted in this project are:

  • (1a) Data sharing where companies derive competitive advantages.

  • (6b) Lack of understanding of the full landscape of possible architectures for a possible IoT solution.

  • (6c) Lack of understanding in how IoT will/can generate value in a given application domain

  • (7a) Data issues concerning privacy and access to sensitive data in cloud environments

  • (8a) Incorporation/streamlining of IoT based applications/decisions with existing business processes.

  • (8b) Lack of IoT adoption by other dependent organisations (eg supply chain) and internal process owners.

All companies in the supply chain can benefit from higher visibility of the information, but mainly logistics companies and port operations can improve accuracy of their plans, and thus their efficiency and resilience.

What was done?

This project was supported by the UK Warehousing Association and a group of their members, John Lewis, Meachers Global Logistics (logistics company), Import Services (distributor), DP World Southampton (port operator).

First, following a set of interviews with the collaborators, an end-to-end process map of the international supply chain was created, detailing how all these companies participate and interact in the supply chain. Next, we focussed on one part of the supply chain – the movement of goods from the UK port (where the goods arrive) to the importer’s warehouse.

The companies shared extracts of the data they manage during the planning and execution of their processes in this stage, enabling us to design an integrated data model for the supply chain. Using this information data was generated by an agent-based simulation for a variety of scenarios. Data was integrated by the prototype platform in order to investigate the potential impact of the data integration platform on each player’s efficiencies.

To make it easy for companies to copy our processes, we used Google’s cloud services to build the platform, having been granted free research programme credits by Google.


This project delivered a generic end-to-end process map of an international supply chain, and a demonstrator of the IoT Data Integration Platform in the context of the port to importer’s warehouse. As part of the demonstrator an agent-based simulation of the end-to-end process was developed.

Technical documentation produced will enable the replication of the IoT Data Integration Platform in the cloud (for different cloud providers), and in custom servers. The process map, together with an integrated data model, help explain the information owners, accountants, and sharing points throughout the supply chain. It also matched the constraints, security and access needs for each player to each piece of integrated data required for them to execute their processes.

The process map helped project partners identify the efficiency improvements and potential competitive advantages to be gained from sharing information. The demonstrator helped project partners understand the value of incorporating IoT into their day-to-day processes, for example fleet status awareness, advance notice of missing deliveries to avoid fines, where imported goods are at any moment.

Deliverables and other tangible outputs

1. International supply chain process map

An end-to-end process map of a generic international supply chain was modelled in BMPN (business process modelling notation) including the inputs of the project partners given during multiple individual sessions and workshops. The process map includes all the typical partners involved in the movement of goods from foreign countries into the UK: vendor (eg manufacturers and suppliers), logistics companies in the country of origin (eg hauliers, forwarders), shipping companies and carriers, port operators, customs, logistics companies in the country of destination (eg UK hauliers and distributors), and importers.

Detailed on the map are all the potential points at which the product could change location, right down to the movement of containers on a shipping vessel as it passes through ports and reorganises its cargo on board.

See figure 1 below for a visualisation of a simplified version of the supply chain.

2. IoT Data Integration Platform architecture

The platform consists of mainly two parts:

  • Data harmonisation module that was designed to harmonise the different types and formats of data from partners and to enable data access to the integrated and up-to-date data.

  • A Data Lake to store data in different versions for traceability (ie raw, standardised in JSON, clean, and integrated data). In summary during the process the data is extracted in multiple formats, transformed into one format (FeedHandlers), cleaned (DataCleansers), and integrated (DataIntegrators); each version of the data is timestamped and stored; and, specific data access is given to the relevant partners (DataAccessClients).

See figure 2 below depicting the architecture of the IoT Data Integration Platform.

3. Demonstrator of the IoT Data Integration Platform

A demonstrator was developed to showcase the IoT Data Integration Platform and to validate the approach in different scenarios in the supply chain. The demonstrator consists of an agent-based simulation that generates data for controlled scenarios, and a development and deployment of the IoT Data Integration Platform using commodity hardware (Raspberry Pi for development, custom server for testing).

4. Guidelines and documentation

During the project, the process map was documented in a BPMN diagram, with parts of the process described in more detail.

The architecture of the IoT Data Integration Platform may be adapted depending on the business case, partners, and type of supply chain. Hence, a document with generic alternative approaches and guidelines for data integration was created. Guidelines on how to select appropriate cloud services from different providers to implement the platform in the cloud were also documented.

The most up-to-date integrated available version of all the data from all the supply chain partners was kept in the ‘golden record’. The golden record data model was documented, using visual paradigm and MySQL Workbench following the E/R data modelling paradigm, and implemented into a MySQL data base and a JSON structure.

Example scenarios, used as the basis for validation design, were documented together with a theoretical validation of the approach.


The impact of this project focussed on the involvement of the industrial partners.

  • Visits to UK warehousing companies during the early stages of the project. Some of the challenges in logistics and warehousing, due to lack of data sharing, were identified.

  • From May 2020 the UK Warehousing Association (UKWA) organised multiple workshop sessions with their members to kick-off the international supply chain process map.

  • A workshop in September 2020 organised with the UKWA, gathered other industrial partners including John Lewis, Meachers Global Logistics, DPWorld Southampton, and Import Services. These sessions elevated the interest of the industrial partners in the project which resulted in subsequent workshops in December 2020, January and February 2021 to complete the process map. Meachers Global Logistics and DP World Southampton provided valuable data for the simulation design and the golden record data model.

  • The platform architecture was presented in an online workshop at the Pitch-in manufacturing day where a good number of researchers communicated their interest in creating synergies to use the platform as basis for their Pitch-In projects.

  • Google accepted the project in their research programme and granted $5,000 worth of credits to implement and deploy a prototype of the IoT Data Integration Platform using Google Cloud services.

Next steps

As a result of the project outcomes, further activities will focus on:

  • Continued involvement with industrial partners, on follow-up future projects.

  • Incorporation of analytics and visualisation for dynamic/automated decision-making.

  • Deployment of a prototype of the IoT Data Integration Platform using Google Cloud services within Google’s Research Programme.

  • Providing research and technical support in the design and development of a platform geared for condition monitoring and predictive maintenance of key cranes in the port of Felixstowe using 5G networks. Particularly, integrating data from IoT sensors installed in the cranes and from programmable logic controller (PLC) controlling them, and enabling data access to applications for AI analytics and visualisation.

Lessons learned

This project has demonstrated how supply chain companies can improve their efficiency, reduce unnecessary costs, and gain competitive advantages by sharing data with their industrial partners. It has also demonstrated that existing, low-cost, and open source IoT technologies can be used for developing digital solutions for supply chain companies.

As a result, the University has benefited from:

  1. Exposing students and academics to real-world supply chain scenarios and involvement with key participants in international supply chains.

  2. Association and involvement with big UK companies and associations in the sector

  3. Immediate reutilisation of generated knowledge in new projects.

This project has tackled one of the biggest challenges in the industry: the lack of data sharing. However, despite the involvement of several industrial partners in the project, there has not been a possibility to deploy the Data Integration Platform in a controlled yet real-world scenario to measure the practical impact and to evaluate other technical aspects derived from the integration of diverse systems data from every partner.

One of the key aspects of this research project was the involvement of partners in the sector. The collaboration of industrial partners arrived late in the project (UWKA late May, and the rest in September 2020), which delayed the creation of the prototype for validation. Some of the next steps could have been tackled if partners had been involved earlier. It would also have been beneficial to involve other companies upstream (eg shipping companies, carriers, forwarders, vendors) to create a more accurate picture of the supply chain.

Access to deliverables, resources and media content

Figure 1. Simplified international supply chain showing the collaboration between partners.

Figure 2. IoT Data Integration Platform architecture.

What has Pitch-In done for you?

The results and knowledge generated in this Pitch-In programme have been crucially transferred to the CDBB Digital Twin for Built-environment project and the Driving port efficiency through 5G-enabled connectivity project. The Pitch-In programme enabled an initial exploration of IoT technologies to deliver proof-of-concept demonstrators for IoT and industrial data integration.

Project lead

Professor Duncan McFarlane

Duncan leads the Distributed Information and Automation Laboratory (DIAL) at the Institute for Manufacturing (IfM) part of the Engineering Department at the University of Cambridge.

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