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Quality management practitioners have yet to cease the potential of digitalisation. Furthermore, there is a lack of tools such as frameworks guiding practitioners in the digital transformation of their organisations. The purpose of this study is to provide a framework to guide quality practitioners with the implementation of digitalisation in their existing practices.

A review of literature assessed how quality management and digitalisation have been integrated. Findings from the literature review highlighted the success of the integration of Lean manufacturing with digitalisation. A comprehensive list of Lean Six Sigma tools were then reviewed in terms of their effectiveness and relevance for the hybrid digitisation approach to process improvement (HyDAPI) framework.

The implementation of the proposed HyDAPI framework in an industrial case study led to increased efficiency, reduction of waste, standardised work, mistake proofing and the ability to root cause non-conformance products.

The activities and tools in the HyDAPI framework are not inclusive of all techniques from Lean Six Sigma.

The HyDAPI framework is a flexible guide for quality practitioners to digitalise key information from manufacturing processes. The framework allows organisations to select the appropriate tools as needed. This is required because of the varying and complex nature of organisation processes and the challenge of adapting to the continually evolving Industry 4.0.

This research proposes the HyDAPI framework as a flexible and adaptable approach for quality management practitioners to implement digitalisation. This was developed because of the gap in research regarding the lack of procedures guiding organisations in their digital transition to Industry 4.0.

This study aims to investigate the feasibility of employing a multi-objectives integer-programming model for effective allocation of resources for cyberfraud mitigation. The formulated objectives are the minimisation of the total allocation cost of the anti-fraud capacities and the maximisation of the forensic accounting capacities in all cyberfraud incident prone spots.

From the literature survey conducted and primary qualitative data gathered from the 17 licenced banks in South Africa on fraud investigators, the suggested fraud investigators are the organisation’s finance department, the internal audit committee, the external risk manager, accountants and forensic accountants. These five human resource capacities were considered for the formulation of the multi-objectives integer programming (MOIP) model. The MOIP model is employed for the optimisation of the employed capacities for cyberfraud mitigation to ensure the effective allocation and utilisation of human resources. Thus, the MOIP model is validated by a genetic algorithm (GA) solver to obtain the Pareto-optimum solution without the violation of the identified constraints.

The formulated objective functions are optimised simultaneously. The Pareto front for the two objectives of the MOIP model comprises the set of optimal solutions, which are not dominated by any other feasible solution. These are the feasible choices, which indicate the suitability of the MOIP to achieve the set objectives.

The results obtained indicate the feasibility of simultaneously achieving the minimisation of the total allocation cost of the anti-fraud capacities, or the maximisation of the forensic accounting capacities in all cyberfraud incident prone spots – or the trade-off between them, if they cannot be reached simultaneously. This study recommends the use of an iterative MOIP framework for decision-makers which may aid decision-making with respect to the allocation and utilisation of human resources.

The originality of this work lies in the development of multi-objectives integer-programming model for effective allocation of resources for cyberfraud mitigation.

An increasing demand of agricultural intensification and value addition necessitates the use of improved inputs such as improved seed. Smallholder farmers contribute about 70% of agricultural production in Tanzania. Agriculture sector in Tanzania contributes about 24.1% of the GDP, 30% of exports and 65% of industrial raw materials. Thus, agriculture development, economic growth and industrialization are inseparable. Due to the nature of the product, smallholder farmers cannot judge the overall excellence of seed at the time of buying. This paper assessed quality uncertainty in maize and vegetable seed and its implication for market exchange between farmers and seed sellers in Kilolo district, Iringa Tanzania. The study used a random sample of 130 smallholder farmers and representatives from ten seed companies. Asymmetric information prevails between the two trading sides, i.e. sellers and buyers, leading into quality uncertainty. Moreover, product augmentation is profoundly overlooked whereby most of seed companies have not augmented their products. Because an improved seed is a quintessential example of an experience good, quality uncertainty of some crop varieties under field conditions favored some seed brands to be used more by farmers compared to others. This paper offers a thorough deduction on quality uncertainty under farmers' field condition and its implication on market exchange. It adds information in the body of knowledge on how an improved seed can contribute to sustainable production of food and industrial raw materials, which is a step towards desired industrialization agenda in Tanzania.

Authorities have set up numerous security checkpoints during times of armed conflict to control the flow of commercial and humanitarian trucks into and out of areas of conflict. These security checkpoints have become highly utilized because of the complex security procedures and increased truck traffic, which significantly slow the delivery of relief aid. This paper aims to improve the process at security checkpoints by redesigning the current process to reduce processing time and relieve congestion at checkpoint entrance gates.

A decision-support tool (clearing function distribution model [CFDM]) is used to minimize the effects of security checkpoint congestion on the entire humanitarian supply network using a hybrid simulation-optimization approach. By using a business process simulation, the current and reengineered processes are both simulated, and the simulation output was used to estimate the clearing function (capacity as a function of the workload). For both the AS-IS and TO-BE models, key performance indicators such as distribution costs, backordering and process cycle time were used to compare the results of the CFDM tool. For this, the Kerem Abu Salem security checkpoint south of Gaza was used as a case study.

The comparison results demonstrate that the CFDM tool performs better when the output of the TO-BE clearing function is used.

The efforts will contribute to improving the planning of any humanitarian network experiencing congestion at security checkpoints by minimizing the impact of congestion on the delivery lead time of relief aid to the final destination.

This study aims to examine academic staff’s engagement with sustainable development goals (SDGs) in higher education institutions.

The triangulation, convergence model of the mixed methods research design was adopted as the strategy for inquiry. A total of 56 questionnaires and 25 interviews were used to collect the data, and this was buttressed by document review and use of secondary data obtained from Scival.

The results show moderate levels of engagement of academic staff with the SDGs. However, SDGs familiarisation is not correlated with the rate of localisation. The lack of funding deflated political will by university management, demotivated academia and shrinking government support are the leading impediments to SDGs localisation.

The results could be improved by using a larger sample size equally distributed across disciplines. Triangulation of academics’ views with those of students and non-academic staff could have improved the understanding of other dynamics involved in the localisation of SDGs by university teaching staff.

The results point towards the need for a university-based framework that interweaves national, institutional, thematic, structural and personal aspects into the SDGs implementation matrix. The underlying determinants of successful localisation of SDGs by academia need to be addressed through a bottom-up approach.

To the best of the authors’ knowledge, this paper is the first attempt in Zimbabwe to exclusively look at University teaching staff’s engagement with SDGs.

This paper explores Lean Six Sigma principles and the DMAIC (define, measure, analyze, improve, control) methodology to propose a new Lean Six Sigma 4.0 (LSS 4.0) framework for employee occupational exams and address the real-world issue of high-variability exams that may arise.

This study uses mixed methods, combining qualitative and quantitative data collection. A detailed case study assesses the impact of LSS interventions on the exam management process and tests the applicability of the proposed LSS 4.0 framework for employee occupational exams.

The results reveal that changing the health service supplier in the explored organization caused a substantial raise in occupational exams, leading to increased costs. By using syntactic interoperability, lean, six sigma and DMAIC approaches, improvements were identified, addressing process deviations and information requirements. Implementing corrective actions improved the exam process, reducing the number of exams and associated expenses.

It is important to acknowledge certain limitations, such as the specific context of the case study and the exclusion of certain exam categories.

The practical implications of this research are substantial, providing organizations with valuable managerial insights into improving efficiency, reducing costs and ensuring regulatory compliance while managing occupational exams.

This study fills a research gap by applying LSS 4.0 to occupational exam management, offering a practical framework for organizations. It contributes to the existing knowledge base by addressing a relatively novel context and providing a detailed roadmap for process optimization.

This article aims to optimise energy use and consumption by integrating Lean Six Sigma methodology with the ISO 50001 energy management system standard in an Irish dairy plant operation.

This work utilised Lean Six Sigma methodology to identify methods to measure and optimise energy consumption. The authors use a single descriptive case study in an Irish dairy as the methodology to explain how DMAIC was applied to reduce energy consumption.

The replacement of heavy oil with liquid natural gas in combination with the new design of steam boilers led to a CO₂ footprint reduction of almost 50%.

A further longitudinal study would be useful to measure and monitor the energy management system progress and carry out more case studies on LSS integration with energy management systems across the dairy industry.

The novelty of this study is the application of LSS in the dairy sector as an enabler of a greater energy-efficient facility, as well as the testing of the DMAIC approach to meet a key objective for ISO 50001 accreditation.

Many organizations currently transition towards digitalized process design, execution, control, assurance and improvement, and the purpose of this research is to empirically demonstrate how data-based operational excellence techniques are useful in digitalized environments by means of the optimization of a robotic process automation deployment.

An interpretive mixed-method case study approach comprising both secondary Lean Six Sigma (LSS) project data together with participant-as-observer archival observations is applied. A case report, comprising per DMAIC phase (1) the objectives, (2) the main deliverables, (3) the results and (4) the key actions leading to achieving the presented results is presented.

Key findings comprise (1) the importance of understanding how to acquire and prepare large system generated data and (2) the need for better large system-generated database validation mechanisms. Finally (3) the importance of process contextual understanding of the LSS project lead is emphasized, together with (4) the need for LSS foundational curriculum developments in order to be effective in digitalized environments.

This study provides a rich prescriptive demonstration of LSS methodology implementation for RPA deployment improvement, and is one of the few empirical demonstrations of LSS based problem solving methodology in industry 4.0 contexts.

Focusing on the adoption of Total Quality Management (TQM) principles in universities, this research paper explores how the “soft” dimensions of TQM trigger its “hard” dimensions considering them at the individual (micro-) and the university (meso-), and eventually at cluster (system-), levels.

Adopting a qualitative approach, this study presents an in-depth, longitudinal case study of University of Siena, one of the oldest Italian universities, that has been at the core of the research-based cluster on vaccines, today converged in the Tuscan Life Science Cluster. In particular, data were collected between 2018 and February 2022 and consists of archival data (press articles, websites, books), nine interviews to key informants, multiyear experience of the Life Sciences sector by two of the authors and other material put at disposal by university offices, and emails. Data analysis relied on a timeline, a coding procedure that considered three levels of analysis (individual, organization and cluster). Finally, the authors looked at the “how” and “why” the emerged themes have contributed to academic excellence.

This paper unveils how “soft” and “hard” sides of TQM are blended across multiple levels for reaching academic excellence. The grounded model emerged enlightens the importance of an individual “soft” dimension, academic passion (composed by its three subdimensions of individual research, teaching and entrepreneurial passion) and also sheds light on the organizational “soft” and “hard” sides that the university has been able to design for encouraging research, teaching and third mission quality. Academic excellence has been possible thanks to the capitalization of the individual and organizational “soft” sides into real outcomes as represented by the organizational and individual “hard” sides.

The paper suggests the importance of TQM principles applied at universities' level, providing an in-depth description of “soft” and “hard” sides dimensions of TQM and their impact on all the three pillars of academic excellence. The study findings suggest implications for managers and professionals in the higher education domain as well as for policymakers emphasizing the importance of supporting the individual and organizational soft sides of TQM. The authors provide practical implications recommending universities to consider not only the organizational dimensions but also individual ones when pursuing higher education excellence. In particular, individual passion plays a crucial role and universities need to identify ways of nurturing it. The authors also recommend policymakers to think about new ways to sustain universities as crucial actors in boosting a cluster development, as well as to consider higher education institutions, especially in more rural areas, as a privileged player not only capable of nurturing academic excellence but also able of creating an internationally renowned cluster.

TQM principles have been intensively analysed from an industrial perspective focusing on manufacturing and services, while this paper focuses on TQM in universities, presenting a grounded model that blends the individual and organizational “soft” and “hard” sides.