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Supply Chain

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Future-proof your Supply Chain

Against a backdrop of increasing uncertainties, addressing unprecedented challenges in the age of Industry 4.0 and post-COVID-19 is pertinent to industry players, as well as to the larger economy.

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Supply Chain Shocks
For every 3.7 years, 1+ month of supply chain disruption occurs

Aging pop

Ageing Population
By 2030, 1 in 4 Singaporeans will be above 65 years old

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Changing Consumer Models
In 5 years, Southeast Asia's e-commerce gross merchandise value is projected to almost double (~US$254 billion)

Supply Chain FP1

Supply Chain Footprint
As supply chains become increasingly complex, their footprint is becoming larger while incurring heavy financial and environmental costs

Supply Chain 4.0, initiated by A*STAR’s Advanced Remanufacturing and Technology Centre (ARTC) helps businesses to integrate technological innovations into their operations, to deal with the additional diverse challenges posed by growing supply chains.

What sets Supply Chain 4.0 apart from existing commercial solutions?

Ecosystem effort for translational R&D
  • We have a team of experienced supply chain and I4.0 research scientists and engineers to work with you
  • Collaboration opportunities with Institutes of Higher Learning
  • Partnership with commercial solution leaders to leverage additional R&D capabilities
  • Development of modular solutions highly-customised to individual industry use cases, to fill existing technological gaps
  • Learning and training platforms dedicated to the latest supply chain happenings
  • Regular events that bring industry players and R&D experts together, generating ideas for the next potential breakthrough
Supply Chain Control Tower (SCCT)
  • Testbed for newly developed solutions at ARTC’s Supply Chain Control Tower before and in line with commercial adoption to de-risk investment

Our work packages

WP 1
Federated Microservice-based Supply Chain Collaborative Platform

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Federated Microservice-based Supply Chain Collaborative Platform

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Industry Challenges

In order for companies to sufficiently deal with disruption situations, and overall visibility of the operation's supply chain is necessary. However, this involves data-sharing at a massive level that induces fears of data privacy breach.

Our Solution

The Federated Microservice-based Supply Chain Collaborative Platform is a multi-enterprise supply chain network platform that brings about visibility for multi-tier supplier collaboration to manage disruptions and traceability, all while preserving data privacy and ensuring organisations' data security.

A federated database architecture is one in which a collection of independent database systems are united into a loosely coupled federation to share and exchange information.

Central to this solution is a Hybrid between Cloud and Local resources, where only metadata, models and references are kept by the Cloud. This allows actual data to be preserved locally, avoiding mass data migration and privacy concerns among different organisations in the supply chain—a novel approach not currently available commercially.

Use Cases

  • Cloud-based Federated Platform without the need to centralise data 
  • Supply Chain Management Ontologies for truly end-to-end visibility
  • Smart edge Gateway to bring analytics on-site and to overcome latency issue

Industry Collaborations

Together with a multinational aerospace and defense company, we developed a Proof of Concept (POC) to showcase how data between business partners can be shared among them with less time, effort and cost using a multi-tier supply chain collaborative platform.

Programme Partners

  • The Advanced Remanufacturing and Technology Centre (ARTC), Agency for Science, Technology and Research (A*STAR)
  • Centre for Next Generation Logistics (C4NGL), National University of Singapore (NUS)
  • Singapore University of Technology and Design (SUTD)
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Authentication & Onboarding Framework for Massive IoT Devices

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Authentication & Onboarding Framework for Massive IoT Devices

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Industry Challenges

Supply chain has become the new favourite target among hackers. 2021 saw a 639 million jump in cyber attacks compared to 2020, amounting to US$6 trillion in losses. From January to June 2021 alone, there were 1.51 billion IoT breaches. At the same time, it was found that 73% of companies globally are not prepared for cyber threats as hackers become more rampant and more creative in their pursuit.

Compared to typical IT systems, IoT devices have:

250,000x smaller memory

100x lower computing power

2000x lower bandwidth

Under this condition, most cyber-security solutions are not able to work, resulting in low or no security features. IoT devices hence, become the easiest point of attack in supply chains.

Our Solution

The Authentication & Onboarding Framework for Massive IoT Devices is designed to enhance the security of existing supply chain network (SCN) via Onboarding Framework with Game Theory and Dynamic Watermarking algorithm. Optimal for abnormality detection, protection from threats and device identity management to secure all IoT gateways and devices when applied to key areas in the supply chain.

SC4.0 hybrid cloud comprises a security architecture to boost users' confidence in the ability of people, technology and processes to create a secure digital world. Digital trust is issued to companies which have proven to provide safety, privacy, security, reliability, and data ethics with their online programs or devices.

Use Cases

  • Security Intelligence & Mass Onboarding framework for “Cloud Collaboration Trust” to establish digital trust across whole supply chain
  •  Track & Trace Platform to facilitate monitoring and visibility of logistics operations 

Programme Partners

  • The Advanced Remanufacturing and Technology Centre (ARTC), Agency for Science, Technology and Research (A*STAR)
  •  Centre for Next Generation Logistics (C4NGL), National University of Singapore (NUS)
  • Singapore University of Technology and Design (SUTD)
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Dynamic Network Configuration & Simulation

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Dynamic Network Configuration & Simulation

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Industry Challenges

Different supply chain simulation models work in silo and have to be manually interpreted. Not only is the data (from the different models) exchange a labour-intensive and time-consuming process, but the accuracy of the resultant analysis is also often not guaranteed. The market lacks a software integrator to connect different components into a coherent whole that would translate fragmented data into functional business tools. 

Our Solution

The Dynamic Network Configuration & Simulation package is a comprehensively-integrated supply chain simulation solution with semantic modelling to analyse, design and optimise supply chain networks (SCNs). This deepens users' understanding of supply networks, to strengthen impromptu decision-making capability in dynamic and disruptive scenarios to achieve efficient, accurate network configuration and production facility design and planning.

Use Cases

  • O²DES Warehouse Digital Twin enables data visualization for risk assessment capability and to pre-empt disruption impacts for prompt decision-making.

Industry Collaborations

The Dynamic Network Configuration & Simulation team has worked with a Fortune 500 company to test drive its solution.

Through the collaboration, the company was able to utilise a simulation testbed in order to significantly improve network configuration designs and to further adapt the organisation's technologies customised for the future situations and disruption events.

Programme Partners

  • The Advanced Remanufacturing and Technology Centre (ARTC), Agency for Science, Technology and Research (A*STAR)
  •  Centre for Next Generation Logistics (C4NGL), National University of Singapore (NUS)
  • Singapore University of Technology and Design (SUTD)
WP 4
Flexible Order Management & Re-planning

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Flexible Order Management & Re-planning

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Industry Challenges

Order processing is currently labour-intensive and tedious. While Natural Language Processing can generally recognise and interpret voices, it is not sufficiently contextualised to process orders. This results in business losses and inefficient use of resources, especially human labour. 

Our Solution

A Natural Language Processing (NLP)-supported interpretive order capturing application that enables end-to-end, no-touch order processing and an order-planning system with in-memory database technique for dynamic replanning. The solution can be applied to critical sites along the supply chain testbed to fortify supply chain planning capabilities. 

Use Cases

  • Interpretive Sales Order Capture for intelligent information extraction from text and voice via NLP
  • No-touch Order Processing, Dynamic Order Plan and Re-plan for accurate real-time inventory tracking
  • Order Fulfillment Plan Execution enables hands-off delivery order and logistics plan generation 

Programme Partners

  • The Advanced Remanufacturing and Technology Centre (ARTC), Agency for Science, Technology and Research (A*STAR)
  •  Centre for Next Generation Logistics (C4NGL), National University of Singapore (NUS)
  • Singapore University of Technology and Design (SUTD)
  • Singapore Institute for Manufacturing Technology (SIMTech)

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Predictive Analytics in Supply and Demand Planning

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Predictive Analytics in Supply and Demand Planning

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Industry Challenges

Amidst the rise of e-commerce in an age of uncertainty, advanced analytics enable businesses to forge ahead of times by accurately pre-empting disruption scenarios and to make apt business decisions based on existing data.

Our Solution

A set of user-friendly engineering tools with Machine Learning and Deep Learning demand and sales forecasting models to be applied to existing and new products, paired with proper GUI to aid end-users in minimising human intervention. The solution analyses big data captured on all nodes of the supply chain, enabling seamless production planning and scheduling, hence stabilising production and avoiding disruption. 

Use Cases

  • Demand forecasting using internal historical and/or external data with new algorithms capable of more accurate supply and demand forecasting
  •  Product similarity search engine ideal for new product forecasting based on existing big data analysis that helps businesses gain a competitive edge by catering to evolving customer demands 

Programme Partners

  • The Advanced Remanufacturing and Technology Centre (ARTC), Agency for Science, Technology and Research (A*STAR)
  •  Centre for Next Generation Logistics (C4NGL), National University of Singapore (NUS)
  • Singapore University of Technology and Design (SUTD)
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Autovanning & Devanning

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Autovanning & Devanning

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Industry Challenges

In the transition towards a skilled and knowledge economy, many heavy warehouse operations need to undergo a makeover. The current ageing population and general unavailability of unskilled labour for many back-breaking logistics tasks are additional push factors towards warehouse automation.  

Our Solution

The Auto Vanning & Devanning programme comprises a flexible, mobile, compact and cost-effective robotic prototype in terms of hardware, complemented with software—AI-based computer vision and motion-planning capabilities for vanning and devanning, picking and placing of diverse loads in challenging warehouse conditions (e.g. low-light, space constraints etc.) 

Use Cases

  • Computer vision system to effectively identifies and locates boxes to be applied to operations such as palletising and depalletizing, tote loading of boxes, and cargo loading and unloading
  •  Motion planning system facilitates work within constrained environments, including shelf picking and replenishment, as well as warehouse loading and unloading
  • The overall system is capable of vanning and devanning of containers filled with boxes that traditionally requires manpower to manage 

Programme Partners

  • The Advanced Remanufacturing and Technology Centre (ARTC), Agency for Science, Technology and Research (A*STAR)
  •  Centre for Next Generation Logistics (C4NGL), National University of Singapore (NUS)
  • Singapore University of Technology and Design (SUTD)
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Warehouse Control Platform

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Warehouse Control Platform

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Industry Challenges

To outshine competitors, warehouse automation is on the rise. In order to facilitate the increasing use of more diverse and more autonomous robots within a single operation, an effective fleet management system helps to minimise warehouse footprint, and ultimately cuts operation cost. 

Our Solution

The Warehouse Control Platform is an affordable, intercommunicable warehouse control platform integrated with Warehouse Management System (WMS) that enables planning and resource optimisation through robot fleet management, providing bi-directional data from mobile robots. The platform should optimally manage a fleet of diver mobile robots composed of 20 to 50 vehicles. 

Use Cases

  • Fleet management system that efficiently organises dynamic robot operations for warehouse optimisation
  •  Drone-based application catered to inventory management, warehouse auditing and outdoor delivery to boost productivity and to optimise manpower 

Programme Partners

  • The Advanced Remanufacturing and Technology Centre (ARTC), Agency for Science, Technology and Research (A*STAR)
  •  Centre for Next Generation Logistics (C4NGL), National University of Singapore (NUS)
  • Singapore University of Technology and Design (SUTD)

Frequently Asked Questions (FAQ)

What is the goal of Supply Chain 4.0?

SC4.0 is a 2-year programme funded under RIE2025 to look at developing digital and automation technologies, together with industry partners who can contribute their knowledge and data to any of the 7 Work Packages that they want to participate in. This will in turn benefit companies who contributed as the development is based on their data/solutions, reducing customization required if they intend to adopt/deploy the technologies.

What kind of commitment is expected from companies taking part in the Supply Chain 4.0 Programme?

i) If any of the 7 Work Packages are of value and interest to companies, ARTC will arrange regular updates and discussions with them It is preferable to appoint a Point-of-Contact to work with A*STAR. Companies do not need to commit financially to be involved in SC4.0.

ii) They can also contribute by giving in-kind contributions to the research themes they wish to participate in, whether software or hardware, so that A*STAR development can be built on top of their solutions and data.

iii) If they wish to take our development further by customizing to their company’s business processes and operations, ARTC can scope it as a project, then develop & deploy it for the company. This will require financial commitment as the Engineers/Scientists will need to be reimbursed for their time to develop the solutions.

When is the deadline for the call of industry participation for the programme?

There is no deadline set, and companies are free to participate throughout the whole duration of the SC4.0 programme (2 years, starting from Nov 2021).

Who owns the intellectual property for the 7 work packages in the programme?

As this programme is funded by public monies in Singapore, A*STAR owns the intellectual property developed in the 7 work packages of the Supply Chain 4.0 Programme.

Will companies who participated and contributed (Cash/In-Kind) in this programme be able to use the IP to develop solutions with or on top of A*STAR development?

There are various options to explore:

i) Licensing option
Companies who intend to commercialize/sell the solution developed in the Work Packages can work with A*STAR on securing the licensing rights (exclusive/non-exclusive).

ii) Companies owning the Foreground IP (FIP)
Companies may want to adopt certain  Work Packages for further development with A*STAR, customized to their requirements and processes. In that case, the IP developed from the Work Packages will be considered as Background IP (BIP), owned by A*STAR and documented in the Project Agreement. New development/process derived as the outcome of this project with the companies will be considered Foreground IP (FIP). Companies can work with A*STAR to negotiate the terms to own the FIP.

We understand the default A*STAR IP positioning in “Research Collaboration Agreement (RCA)” does not allow companies to fully own the FIP in the projects. What are the ways to own the FIP?

For the Foreground IP (FIP), the default IP position in A*STAR project agreements using RCA is either “Jointly-Developed, Jointly Owned” or “Sole-Developed, Solely-Owned”, which either allows the IP to be co-owned with A*STAR or fully owned by A*STAR. For companies who would like to own the FIP, an escalation process is to be made subject to approval by our Deputy Chief Executive (DCE, A*STAR).

Alternatively, if companies join as an ARTC member (consortium membership model), we have 3 types of Project Agreements.1 of which allows companies to own the FIP, namely:
i)   Schedule 4 - MEMBER OWNED FOREGROUND IP AND FOREGROUND KNOW-HOW
ii)  Schedule 5 - ARTC OWNED FOREGROUND IP AND FOREGROUND KNOW-HOW
iii) Schedule 6 - JOINTLY OWNED FOREGROUND IP AND FOREGROUND KNOW-HOW

Is the project done in collaboration with other companies (both MNCs, LLEs and SMEs), or just with A*STAR?

The main development for each Project will be led by A*STAR Engineers/Scientist to achieve the deliverables highlighted under each Project. The team will be working with companies in the development to ensure relevance to industry needs and opportunity to proliferate the technologies back to the industry. This is an open platform. It is both an invitation for companies to work with us in this programme, and for companies to be able to network and collaborate.

Do companies pick one of the projects to work on? Is it limited to any of the 7 packages?

i) Are the problem statements fixed?
The Work Packages are meant to solve problem statements gathered from extensive industry feedback. We also understand that each company has its own challenges and is at different technological maturity/stage. Hence, we are open to exploring and discussing with the companies on how we can work on their problem statements as a one-to-one project.It can be both ways. An example could be a company working with us on 1 of the 7 Work Packages, but also engaging us in parallel on a project that is looking at utilising AGV for their warehouse/production.

ii) For the work deliverables of each Project, are those existing solutions that ARTC has, which ARTC would like to test out with companies?
No, they are not existing solutions. They are what we plan to develop in 2 years. But companies do not have to wait 2 years for us to develop before they get to test it out. There are multiple Minimal Viable Products (MVP) milestones set up for each Work Package that companies can take and test out. We are able to share more with the companies.

Is there funding to send companies’ workforce for training regarding Supply Chain related courses?

Training courses have been developed and are currently in the process of seeking WSG subsides.