A Path Towards Business Cases for Automated and Autonomous Heavy-Duty Mobile Machinery : An Interdisciplinary Approach

Abstract

Heavy-duty mobile machines (HDMMs) play an important role in critical application areas of the society such as mining, construction, and logistics. However, skilled labour shortages, aging populations, and fatal accidents due to HDMM operations affect the productivity, profitability, and continuity of operations in these critical application areas. Thus, the automation of HDMMs, especially when HDMMs can operate without human intervention, i.e., autonomously, are being envisioned as a viable solution to mitigate these broader societal challenges.

However, research and development of HDMMs is predominantly based on mechanical engineering principles, which have remained unchanged for several decades. Thus, the development of new generations of automated HDMMs require new skills, competences, and key enabling technologies from the fields of robotics, artificial intelligence, and software engineering. These new requirements increase the development costs of automated and autonomous HDMMs, thereby impacting their feasibility for large scale deployment. Thus, the HDMM industry also needs new non-technological research perspectives from business, management, and social sciences to enable the successful and sustainable deployment of automated and autonomous HDMMs.

Accordingly, this dissertation investigates new business cases for automated and autonomous HDMMs. Since technological research is abundant but fragmented into research silos within the HDMM industry, this dissertation utilises an interdisciplinary approach to analyse automated and autonomous HDMMs from a holistic, but predominantly non-technological perspective. Thus, by utilising an array of Mixed Methods and diverse interdisciplinary theoretical concepts of automation, autonomous solutions, Organisation Theories (Stakeholder Theory, Transaction Cost Economics, and Dynamic Capabilities), business models, ecosystems, and production engineering, this dissertation develops theoretical artefacts and practical frameworks to analyse business cases for automated and autonomous HDMMs.

In doing so, this dissertation proposes and develops the following artefacts: A novel taxonomy which standardises definitions for levels of automation for HDMMs; Outlines the multidisciplinary collaborative challenges associated with the development and deployment of autonomous HDMMs; Contextualises the business and industrial context of HDMMs within research literature on autonomous solutions; Develops a stakeholder mapping of the HDMM industry; Describes the trends, drivers, barriers, and value propositions which are influencing the developments in the HDMM industry; Proposes a novel quantified framework to classify and prioritise stakeholders in emergent ecosystems; Finally, conceptualises standardised key performance indicators to measure and benchmark the performance of automated and autonomous HDMMs.

The practical implication of this dissertation is that it provides guidance regarding the appropriate automation capabilities and feasibility of deploying autonomous solutions in the HDMM industry. Such guidance could be useful to diverse organisations in the HDMM industry such as manufacturers of HDMMs, technology/component suppliers, end-user groups, standardisation working groups, and legislation. An illustration of the research results from this dissertation are also visualised on the Business Model Canvas. Accordingly, this dissertation paves the path towards the successful and sustainable deployment of automated and autonomous HDMMs by developing pragmatic and practice-oriented frameworks.

The theoretical implications of this dissertation are two-fold: First, it attempts to unify the fragmented research on HDMMs under a single umbrella of automated and autonomous HDMMs; Second, this dissertation introduces the business and industry context of the HDMM industry within academic research by formalising tacit knowledge, thereby attempting to bridge the existing research-praxis gap within non-technological research on autonomous solutions within the HDMM industry.