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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.

This study aims to carry out a systematic literature review (SLR) on the integration of Lean, Industry 4.0 and the supply chain or the Lean Supply Chain (LSC) 4.0. The research analyses the current research on the LSC 4.0 concept in an increasingly digitalised world. The authors present the benefits, motivations, critical success factors and challenges of integrating the LSC with Industry 4.0 technologies within this emerging area of research.

An SLR is carried out on how Lean can be integrated with Supply Chain 4.0. Using the search strings of “Lean Supply Chain 4.0,” “Lean Supply Chain Management 4.0” and “Lean Supply Chain Digitalisation,” a review of published literature was carried out via searches on academic databases.

Industry 4.0 has a synergistic effect on the LSC and, depending on the technology and sector applied in, can complement and enhance the LSC. Similarly, the LSC is a precursor for digitalisation. There are considerable implications in the LSC 4.0 for green and sustainable processes.

Organisations can use this study to understand what the LSC 4.0 means to industry, the benefits and motivating factors for implementation, the critical success factors (CSFs) to implementation and the challenges for implementation.

This study adds to state of the art around the LSC 4.0 and future directions in this nascent research area. This study will aid organisations in understanding how Lean, supply chain management and Industry 4.0 can be integrated.

This paper focuses on image-to-text manuscript processing through Handwritten Text Recognition (HTR), a Machine Learning (ML) approach enabled by Artificial Intelligence (AI). With HTR now achieving high levels of accuracy, we consider its potential impact on our near-future information environment and knowledge of the past.

In undertaking a more constructivist analysis, we identified gaps in the current literature through a Grounded Theory Method (GTM). This guided an iterative process of concept mapping through writing sprints in workshop settings. We identified, explored and confirmed themes through group discussion and a further interrogation of relevant literature, until reaching saturation.

Catalogued as part of our GTM, 120 published texts underpin this paper. We found that HTR facilitates accurate transcription and dataset cleaning, while facilitating access to a variety of historical material. HTR contributes to a virtuous cycle of dataset production and can inform the development of online cataloguing. However, current limitations include dependency on digitisation pipelines, potential archival history omission and entrenchment of bias. We also cite near-future HTR considerations. These include encouraging open access, integrating advanced AI processes and metadata extraction; legal and moral issues surrounding copyright and data ethics; crediting individuals’ transcription contributions and HTR’s environmental costs.

Our research produces a set of best practice recommendations for researchers, data providers and memory institutions, surrounding HTR use. This forms an initial, though not comprehensive, blueprint for directing future HTR research. In pursuing this, the narrative that HTR’s speed and efficiency will simply transform scholarship in archives is deconstructed.