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The aim of this study is to investigate the body of literature on lean published by the International Journal of Production Research (IJPR), which has been interested in the subject since its dawn. This review adopted a dynamic and quantitative bibliometric method composed of the keywords co-occurrence network and keywords burst detection. The analyses performed on keywords co-occurrence networks highlighted how research in IJPR has addressed research on lean over time and allowed a comparison with the consolidated research streams in literature. The burst detection completed the analysis highlighting the trends and most recent research areas characterising IJPR publications. The outcomes of this study reflected the evergreen relevance of lean; indeed, the latest research trajectories identified in IJPR stressed its link with the increasingly topical issues concerning industry 4.0, sustainability and remanufacturing. The analysis recognised in 'lean Six Sigma', and specifically in its support to the service sector, an under-considered topic, hence a scope that offers room for further study, in accordance with IJPR objectives.

Notwithstanding the existence of a broad research base on assembly line balancing (ALB), companies do not use the mathematical approaches developed in the literature to configure assembly lines. This article aims to fill the gap between research and application by presenting and testing in a real industrial context a methodology based on complexity reduction and kaizen events. First, the methodology supports reducing the complexity that affects real-life assembly systems in terms of the variety of, e.g., finished products, materials and parts. Next, the methodology proposes the conduction of kaizen events by using lean manufacturing tools, such as process analysis, time observation, waste identification, workstation standard documents, and yamazumi charts. The methodology is successfully applied to a case study that describes its use in the confectionery process for a major chocolatier company along with the results of the application. The main contribution of this paper consists in presenting a method to manage the line balancing activity within everyday industrial realities, helping practitioners to improve and maintain the performance over time.

The literature discusses data science (DS) as a very promising set of techniques and tools to support lean production (LP) practices. DS could aid manufacturing companies in transforming massive real-time data into meaningful knowledge, increasing process transparency and product quality information and supporting improvement activities through data-driven decision-making. However, no attempt has been made in the literature to formalise the links between DS and LP practices. Thus, this study aims to overcome this gap by clarifying the DS techniques and tools that can support LP practices and how to apply them. This study employs a quantitative bibliometric method-specifically, a keyword co-occurrence network analysis-on a set of papers extracted from Scopus. The results obtained allowed the researchers to identify a set of DS techniques and tools that can support LP practices and to develop a model to guide their implementation based on the typical improvement implementation stages of the plan-do-check-act cycle. The model shows how to use DS techniques and tools in LP for: identifying areas for improvement and subsequent implementation (plan); enabling a better knowledge and process management (do); identifying/predicting potential problems and employing statistical process control (check); providing remedial actions and effectively applying process improvement (act).

Materials Requirement Planning (MRP) technique is widely employed by most manufacturing companies, even though field applications point out some weaknesses, including ignored production capacity constraints and fixed lead-times. These weaknesses often lead to infeasible production schedules, which trigger fluctuating workloads over time, significant adjustment effort and eventually unpredictably long lead times. This paper introduces a capacity-oriented MRP procedure that combines the traditional MRP procedure with an approach based on linear programming: in this way, requirement of lead times pre-determined a priori outside the MRP procedure is overcome. The new procedure is then applied to a real-life company and results highlight that feasible plans of orders are generated without requiring lead-times as input and without relevant computational burden