Deciding to implement emerging technologies: the help of digital technologies in planning for the implementation of robotics and autonomous systems

Deciding to implement emerging technologies: the help of digital technologies in planning for the implementation of robotics and autonomous systems in food manufacturing firms.

Klerkx and Rose (2020) use the term "Agriculture 4.0" to describe the way that technologies are radically changing the way that food is produced, processed, and traded. Examples include robotics and sensors in precision agriculture, 3D food printing techniques, and blockchain to enhance supply chain traceability. This is driven by the need to increase food production to feed a growing population whilst dealing with climate change, labour shortages and changing consumer habits.

Successfully implementing emerging technologies is an important yet challenging task for manufacturing businesses. There are several decisions to be made, for example, which technologies specifically, where in the business to implement them, how to integrate them into operations, and when to deploy the technologies (Kerr et al., 2006; Mortara et al., 2009; Mortara, 2015). Organisations operate within complex, uncertain, dynamic environments where they must make decisions based on incomplete information - this is especially pertinent for decisions to incorporate sophisticated technologies that are still in the making.

Decision makers must collect data from various sources, filter it to find relevant information, and then use insights from the extracted information to make informed decisions (Darioshi and Lahav, 2021). The ever-increasing availability and complexity of data in dynamic, inter-connected environments places significant cognitive loads on decision makers, sometimes resulting in sub-optimal choices. Tools developed by technology management researchers can help.

Companies often use combinations of techniques, such as roadmapping (Phaal, Farrukh and Probert, 2001, 2004), scenario planning and scoring methods (Mitchell, Phaal and Athanassopoulou, 2018), to reconcile opportunities and risks associated with implementing emerging technologies. Digital technologies, such as augmented reality (AR) and virtual reality (VR), can be used to alter the interface through which information is presented to decision makers. Such changes can influence decision-making behaviour and affect the decision outcome (Thaler and Sunstein, 2009; Thaler, Sunstein and Balz, 2014).

This research aims to investigate how digital technologies can be used to support decision makers implement emerging technologies to aid the transition to a more sustainable agri-food system.

The objectives are as follows:
Review literature relating to decision support systems at different organisational levels
Assessment of the state of the art of technologies for data visualisation (such as mixed reality)
Develop a decision support system for adoption of technologies necessary for sustainable food systems and "Agriculture 4.0"

The expected results of the work include insights into how to leverage digital technologies to effectively represent information to decision makers within organisations, specifically for adoption of emerging technologies in the agri-food sector.

The proposed CDT provides a unique vision of advanced RAS technologies embedded throughout the food supply chain, training the next generation of specialists and leaders in agri-food robotics and providing the underpinning research for the next generation of food production systems. These systems in turn will support the sustainable intensification of food production, the national agri-food industry, the environment, food quality and health.

RAS technologies are transforming global industries, creating new business opportunities and driving productivity across multiple sectors. The Agri-Food sector is the largest manufacturing sector of the UK and global economy. The UK food chain has a GVA of £108bn and employs 3.6m people. It is fundamentally challenged by global population growth, demographic changes, political pressures affecting migration and environmental impacts. In addition, agriculture has the lowest productivity of all industrial sectors (ONS, 2017). However, many RAS technologies are in their infancy - developing them within the agri-food sector will deliver impact but also provide a challenging environment that will significantly push the state of art in the underpinning RAS science. Although the opportunity for RAS is widely acknowledged, a shortage of trained engineers and specialists has limited the delivery of impact. This directly addresses this need and will produce the largest global cohort of RAS specialists in Agri-Food.

The impacts are multiple and include;

1) Impact on RAS technology. The Agri-Food sector provides an ideal test bed to develop multiple technologies that will have application in many industrial sectors and research domains. These include new approaches to autonomy and navigation in field environments; complex picking, grasping and manipulation; and novel applications of machine learning and AI in critical and essential sectors of the world economy.

2) Economic Impact. In the UK alone the Made Smarter Review (2017) estimates that automation and RAS will create £183bn of GVA over the next decade, £58bn of which from increased technology exports and reshoring of manufacturing. Expected impacts within Agri-Food are demonstrated by the £3.0M of industry support including the world largest agricultural engineering company (John Deere), the multinational Syngenta, one of the world's largest robotics manufacturers (ABB), the UK's largest farming company owned by James Dyson (one of the largest private investors in robotics), the UK's largest salads and fruit producer plus multiple SME RAS companies. These partners recognise the potential and need for RAS (see NFU and IAgrE Letters of Support).

3) Societal impact. Following the EU referendum, there is significant uncertainty that seasonal labour employed in the sector will be available going forwards, while the demographics of an aging population further limits the supply of manual labour. We see robotic automation as a means of performing onerous and difficult jobs in adverse environments, while advancing the UK skills base, enabling human jobs to move up the value chain and attracting skilled workers and graduates to Agri-Food.

4) Diversity impact. Gender under-representation is also a concern across the computer science, engineering and technology sectors, with only 15% of undergraduates being female. Through engagement with the EPSRC ASPIRE (Advanced Strategic Platform for Inclusive Research Environments) programme, AgriFoRwArdS will become an exemplar CDT with an EDI impact framework that is transferable to other CDTs.

5) Environmental Impact. The Agri-food sector uses 13% of UK carbon emissions and 70% of fresh water, while diffuse pollution from fertilisers and pesticides creates environmental damage. RAS technology, such as robotic weeders and field robots with advanced sensors, will enable a paradigm shift in precision agriculture that will sustainably intensify production while minimising environmental impacts.