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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.

The lean philosophy has demonstrated its effectiveness to improve firms' operational performance. However, the impact of lean practices on financial performance is still unclear due to the poor understanding of the link between operational and financial measures and the conflictive results obtained by previous research. The purpose of this paper is to conduct a systematic literature review to understand whether lean companies have improved their financial performance. Moreover, this article aims to uncover research gaps in the literature and examine which time spans of research have been considered to analyse both the degree of lean implementation and the measurement of financial outcomes.

A systematic literature review has been conducted to identify peer-reviewed articles that analyse the effect of the lean production paradigm on the financial performance measures of manufacturing companies. Then, the identified articles were processed using a combination of descriptive and content analyses methods to draw new conclusions, uncover gaps and find novel paths for research.

Various authors indicate that lean initiatives lead to an enhancement of financial performance measures. JIT and TQM lean practice bundles are suggested as the best enablers of financial performance in terms of sales and profit. In contrast, according to some scholars, lean does not necessarily improve companies' financial results if it is not properly implemented.

Several studies have focused on analysing the effects of lean on performance. However, only a small part of the literature has addressed the study of the effects of lean practices on financial performance metrics. The originality of this study lies in the investigation of the connections between lean practices and financial performance measures found in the literature. The outcome is the identification of various possible positive impacts of some lean practices on financial metrics.

Improvement of reverse supply chains requires accurate and timely information about the patterns of consumption. In the consumer context, the ways to generate and access such use-visibility data are in their infancy. The purpose of this study is to demonstrate how the Internet of Things (IoT) may be operationalised in the domestic setting to capture data on a consumer’s use of products and the implications for reverse supply chains.

This study uses an explorative case approach drawing on data from studies of six UK households. “Horizontal” data, which reveals patterns in consumers’ use processes, is generated by combining “vertical” data from multiple sources. Use processes in the homes are mapped using IDEF0 and illustrated with the data. The quantitative data are generated using wireless sensors in the home, and qualitative data are drawn from online calendars, social media, interviews and ethnography.

The study proposes four generic measurement categories for operationalising the concept of use-visibility: experience, consumption, interaction and depletion, which together address the use of different household resources. The explorative case demonstrates how these measures can be operationalised to achieve visibility of the context of use in the home. The potential of such use-visibility for reverse supply chains is discussed.

This explorative case study is based on an in-depth study of the bathroom which illustrates the application of use-visibility measures (UVMs) but provides a limited use context. Further research is needed from a wider set of homes and a wider set of use processes and contexts.

The case demonstrates the operationalisation of the combination of data from different sources and helps answer questions of “why?”, “how?”, “when?” and “how much?”, which can inform reverse supply chains. The four UVMs can be operationalised in a way that can contribute to supply chain visibility, providing accurate and timely information of consumption, optimising resource use and eliminating waste.

IDEF0 framework and case analysis is used to identify and validate four UVMs available through IoT data – that of experience, consumption, interaction and depletion. The UVMs characterise IoT data generated from a given process and inform the primary reverse flow in the future supply chain. They provide the basis for future data collection and development of theory around their effect on reverse supply chain efficiency.