The proposed multidisciplinary and cross-sectoral research project aims to ensure responsible packaging and handling of essential food and pharmaceutical products in the aftermath of the COVID-19 outbreak, by using safer packaging technology standards, data driven model and artificial intelligence techniques in the cold chain system.   

Cold chain’s contribution towards the economic growth in terms of GDP is substantial in the emerging economies. Typically, a cold chain is challenged to maintain the safety of the product in several ways: lack of coordination among stakeholders; inefficient utilisation of infrastructure; no information sharing among stakeholders and non-use of technology to automate the monitoring of process variables. COVID 19 has caused severe disruption along the supply chain, particularly affecting the perishable and life-saving pharmaceutical products. Business sectors supporting the cold chain from storage to distribution sectors, are therefore experiencing novel challenges such as virus-free packaging, increase in supply chain costs and information tracing. New data driven models to normalise and deal with the after-effect of lockdown policies and large-scale area restrictions are needed. For example, cold storage facilities for food commodities globally experienced over-capacity for some items, such as seafood, chicken, fresh fruits and vegetables. The supply chain costs spiked upwards due to transportation restrictions and food safety issues. These new norms, in addition to the food safety requirements, increase overall cost in the entire cold chain network, which is then transferred to the consumers – subsequently when customer purchasing ability has been reduced substantially. 

In order to address these problems, there is a need for safer and more secure cold chain business models that share responsible value propositions by aligning food and pharma supply chain stakeholders to focus on key activities, resources, partners, customer relationships, operation channels, cost structure and revenue streams.   

The major objective of the studentship is to carry out ground-level field research with stakeholders in order to validate the developed packaging standards, data driven model and methodologies, and to refine the model with stakeholder support. 

Rationale, aims and objectives, methodology and project plan for the studentship 

Rationale: 

Cold chain growth is predicted to rise by 30% in emerging economies (Brin, 2016). In emerging economies, the food and beverage industry has become one of the mainstay manufacturing sectors, making a major contribution to national economic growth.  This research will investigate and establish the steps to be taken by stakeholders to run a healthy cold supply chain in the post COVID scenario that has shifted distribution priorities.  In the supply chain perspective, a food product has a long chain flow, from seeds, farmers, manufacturers/ processing, distribution and retailers, to restaurants and households. So, the government needs the support and collaboration of logistics service providers to distribute goods safely and efficiently. With the knowledge of all aspects, stakeholders can expected to run a cold supply chain well in the post Covid-19 pandemic.  

Existing literature: 

Till now, many studies have indicated that there are several research gaps in cold chain management with respect to tracking the product during handling process and its implication with decision making. Shashi et al., (2017) mentioned notable gaps in their review article on food cold chain management, including lack of modern packaging methods, eco-labelling and inadequate packaging standards. In addition, it is well articulated that the packaging and information would assist cold chain partners to tackle inefficiencies in managing inventory, to reduce cost, wastage and lead-time and to react effectively to the surges in demand as per the context (Shashi et al., 2017). Moreover, another well-known research gap that needs validation in sectors such as perishable food and pharma is the effective integration of customers, suppliers and internal integration of focal companies to improve cold chain performance (Atasevan and Nair, 2017; Brison and LeTallec, 2017). 

Previous studies also emphasise the importance of data analytics in cold chain management (Chaudhuri et al., 2018). Specifically, the benefits of improving flexibility and effectively managing  demand volatility and cost fluctuation, allowing businesses to make better decisions (Nakandala et al., 2016; Chaudhuri  et al., 2018).  The review article by Chaudhuri et al. (2018) particularly mentioned the benefits of capturing transactional and environmental data.  Multiple studies particularly addressed the need for tracking the product during handling, in order to improve visibility and better decision making. Furthermore, better coordination may translate into reduction of food costs by 70% less than current prices and there is a possibility to increase exports (FAO, how to feed world in 2050). 

This studentship will benefit the emerging economies cold chain through learning from the advances in the UK cold chain, while the UK cold chain will learn how to reduce waste by dealing with decentralized distribution. Overall, multi-stakeholder coordination is well explored in automotive and other manufacturing sectors.  However, it is not the same in perishable products sector and to the best of our knowledge, there have been few studies in a country-specific context of cold chain stakeholder integration with novel packaging standards and better data driven models. Hence, this studentship will develop an advanced packaging standard for safe handling of products and inclusion of a new norm data driven challenges.  

Objectives: 

• To map the bottlenecks in the cold chains in emerging economies, post COVID-19, to identify the lack of coordination, utilization of infrastructure, status-quo of information sharing and technology use, issues surrounding packaging, and decision support system for stakeholders.  
• To develop a safe packaging standard that will trace information about the handling of products, based on comparative investigation of case studies in emerging economies, with respect to perishable food and pharma products. 
• To develop integrated data driven models and methodologies, for responsible distribution of food with minimal wastage. 
• To implement, test and validate the packaging standards and data driven models. 
• To disseminate the findings through a range of linking activities and initiatives such as taskforce meetings, and conferences, engaging experts to share and exchange their research results and experiences. 

Research plan of studentship

Year 1: Mapping the bottlenecks in current cold storage network  

Task 1: The student will carry out value stream mapping with a selective food and pharma supply chain in the emerging economies to identify the key activities, key resources, key partners, customer relationships, channel, cost structure and revenue streams. Specifically, this year will enable the student to understand the lack of coordination, utilization of infrastructure, status-quo of information sharing and technology use, and issues surrounding packaging and decision support system for stakeholders. Value stream mapping tool using a case-based approach will be used. Several interviews will be carried out among different stakeholders to carry out an in-depth analysis.  

Year  2: Development of safe packaging standards and data driven models for food and pharmaceutical products 

Task 1: Tracing information to ensure safe handling of products from source to destination.  

The student will consult Professor Alan B Dolton to develop safe RFID-based eco-labels for packaging. Working with the Physics leading expert in nanomaterials and pioneering development of new micro level RFID eco-labels. 

Task 2: Development of data driven model and data analytics for new norm cold chain management 

The student will explore and develop data driven model and artificial intelligence techniques to maximise value proposition. Data driven models will be developed using multiple case studies to collect data from perishable food and pharma companies. Both transactional and environmental data will be collected for the end-to-end cold chain. Artificial intelligence techniques will be used to fine tune parameters including cost and other soft factors to ensure safe handling of products.  

Year 3: Implement and validate the collaborative cold chain models 

Packaging standards and models will be implemented and validated in selected food and pharma companies in the post-Covid-19 scenario. In addition to changes in customer behaviour, in the form of purchases of goods and food online, other changes including the level of customer awareness about the safety of food ordered and delivery with the ease of tracking and tracing will be studied. Earlier contact with the project supervisors would enable the student to get access to field observation, interview and group discussion.  

Year 4: Dissertation and EPSRC grant proposal write-up 

The student will use this year to complete the writeup of dissertation and large EPSRC grant proposal. Attempt will also be made to disseminate the findings. In addition the supervisors will engage with a range of impact activities such as writing blogs, running an Insta story, and extensive use of social media to promote the benefits to a wider community.  

Strategic importance of the project

This project will have significant impact over both short and medium terms to the school’s sustainability strategy and immensely contribute to achieve the aims of following research mobilisation groups i) supply chain 4.0 and artificial intelligence group within the school. For instance, in the short term, the studentship would enable to train a researcher to map the challenges within the cold chain network from the multiple stakeholder perspectives to solve practical problems and reduce the costs of storage and distribution. This will enable the supply chain 4.0 research group capacity to successfully write the EPSRC grant proposal with the interaction from the expertise of SPRU, Management and Physics disciplines. In the longer term, the studentship will pave way to leverage the findings to establish a global cold chain centre with the support of EPSRC grant to develop a new category of workforce, through the creation of a tailored degree programme and the design and delivery of executive training. A few deliverables from the studentship will be:

• A complete map of cold storage network in a specific emerging economy. 
• A safe packaging standard with bar code embedded packaging for the perishable food and pharma sectors. 
• A data driven model for responsible cold storage system.  
• Artificial intelligence methodologies.  
• Two articles in leading journals.  
• Compilation of student internship impact articles published in regular and social media. 

Research Environment and Training:

Science Policy Research Unit has the biggest PhD student cohort within the school and the student will get access to the peer learning network and specific training in research methodologies. The student will become member of two research mobilising groups including Supply Chain 4.0 and Artificial Intelligence and benefit from the frequent interaction with experts participating in the research seminars, paper reading group and annual conferences.  

In addition the expertise of the three supervisors such as data driven models, eco labelling, artificial intelligence and resilience will hugely benefit the student to interact with multiple stakeholders given below and to achieve the specific objectives of the studentship mentioned above.  

Farmer/Active pharmaceutical industry suppliers: Able to work with downstream members in the supply chain and get access to technology and ‘fair share’ for their effort post Covid-19 pandemic. 

Processor/focal companies: Will jointly align with downstream members to reduce production losses and hidden costs of operation. This will help them to increase productivity and improve their ability to match supply and demand through access to customers via several channels during the Covid-19 pandemic. 

Transporter: Improved coordination will increase efficiency and responsiveness and assist in the reduction of greenhouse gas emissions. In addition, access to technology will improve the traceability of the product at pallet level. It will also help measure the status of perishable products and vaccines during transit.  

Retailer: The collaborative data driven model will empower retailers to adopt decentralised decision-making strategies, ensure safety of products and understand customer behaviour in both the present and post-COVID 19 eras.  

Policy maker: Eco label and data driven model and artificial intelligence will encourage policy makers to push stakeholders to adapt certain novel technologies and to prioritise regional production as per supply and demand. Overall, the project will stimulate policy makers to enhance competition among food supply chains and pharmaceutical companies for maximised operation under the new ‘normal’ conditions.