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The study aims to compare and analyze the impact of the commonly used pallets (wooden pallet and plastic pallets), using life cycle assessment (LCA), then provide certain suggestions for the development of green packaging.

In this study, software Simapro was used to calculate and analyze the whole life cycle of pallets from the stage of raw materials and production to processing and waste disposal.

A total of 12 environmental categories were used to quantitatively analyze the environmental impact of the four different pallets. The results showed that, regardless of raw material, processing, or waste stage, the environmental impact of wooden pallet was lower than that of plastic pallet. Wooden pallet was better than plastic pallets.

This study compared and analyzed the pallet of 1 × 1.2m with different materials.

Green packaging is the development trend of the future packaging, which follows the principle of 3R1D. According to the calculation results, corresponding suggestions can be put forward from production, processing, using, wasting and other aspects, and make corresponding contributions to the development of green packaging.

The contribution and impact of each stage of the product on the environment can be studied. The environmental impact, such as global warming potential, water scarcity, can be reduced through different solutions such as the use of green materials, good processing techniques and higher recycling rates.

Pallets ensure efficient processes in logistics and are exchanged between the different actors, while passing through various supply chains several times. In common practice, the exchange is often not directly carried out on site, e.g. due to a lack of time, so that additional trips and new pallet purchases become necessary. To reduce these negative effects, a digital cross-actor platform is designed, and its potential is investigated.

The authors developed an agent-based simulation model with mathematical optimization. Using experience from practitioners, as well as real-world datasets which were analyzed, the authors ensure a realistic model of the pallet exchange system in Germany.

The authors demonstrated that, with the help of this platform concept, transport routes can be shortened, debts and receivables can partly be equaled out through balancing, and the quantity of pallets in the overall system can be reduced.

The results are not directly transferable to pallet exchange systems in other countries without considering their general settings.

Digital networking increases the efficiency of the existing pallet exchange system. Even small collaborations prove to be reasonable.

The authors developed new mechanisms for a digital pallet exchange platform, which takes on the role of a central planning instance, in addition to recording pallet receivables and debts. It enables the planning of the commodity flow of empty pallets, which are transported by the forwarders on regular routes, and distributed between the platform participants.

Saudi Arabia's 2030 vision targets an increase of 34% in non-oil revenue participation in the GDP, thus the need for automation and digital transformation. The Company ER is a market leader producing high-quality dairy products in the Kingdom and is a pioneer in the production industry. The company has recently increased the capacity of its milk factory to meet its vision. An investment was made to automate the pallet handling procedures at the milk factory to provide increased production for daily consumption. The new automation transition in Company ER's milk factory provides a unique opportunity to utilize lean management tools to improve the current automated processes before commercialization.

OEE (overall equipment effectiveness) will monitor losses for different operational losses in the new automated system and indicate system improvements, with 85% as the target. Based on DMADV (design, measure, analyze, design and validate) methodology, this study analyzes the entire automated pallet handling system. It uses lean tools to identify areas for improvement, identify waste elements and propose solutions to achieve Company ER's OEE targets.

In this paper, the outcomes will be presented as documented solutions that address the losses encountered in the production system, showing a 12.8% increase in the system's OEE.

Owing the time and resource constraint, this study only involved automated pallet handling procedures in a milk production facility. Hence, the generalization of the result is slightly limited. More studies in several different processes and sectors are required.

This study provided a valuable tool for researchers for gaining deeper understanding regarding the lean manufacturing and its implementation. For practitioners, it is useful to evaluate the degree of lean manufacturing tools in their material handling systems.

This study is the first attempt to develop lean manufacturing constructs for evaluating the automated pallet handling procedures in a milk production facility.

The purpose of this paper is to highlight the prominent cognitive biases that influence Lean practices in organisations that have a multi-cultural work environment which will aid the organisational managers and academics in enhancing the understanding of the human thought process and mitigate them suitably.

A multiple case study was conducted in organisations that were previously committed to Lean practices and had a multi-cultural work environment. This research was conducted on five companies based on 99 in-depth semi-structured interviews and seven process observations that sought to establish the system-wide cognitive biases present in a multi-cultural Lean environment.

The novel findings indicate that nine new biases influence Lean implementation and practices in a multi-cultural environment. This study also found strong connectivity between Lean practices and 45 previously identified biases that could affect positively or negatively the lean methodologies and their implementation. Biases were resilient enough that their influence on Lean in multi-cultural workplaces, even with transient populations, did not demonstrate cultural differentiation.

Like any qualitative research, constructivism and narrative analyses are subjected to understanding based on knowledge gained on the subject, and data may have been interpreted differently. Constructivist co-recreation of process scenarios based result limitations is therefore acknowledged. The interactive participation in exploring the knowledge sought after and interaction that could have a probable influence on the participant need to be acknowledged. However, the research design, multiple methods of data collection, generalisation based on data collection and analysis methods limit the effects of these and findings are reliable to a greater extent.

The results can provide an enhanced understanding of biases and insights into a new managerial approach to take remedial steps on biases’ influence on Lean practices that can result in improved productivity and well-being from a business process perspective. Understanding and mitigating the prominent biases can aid Lean manufacturing processes and support decision makers and line managers in improving lean methodologies’ effectiveness and productivity. The biases can be negated and used to implement decisions with ease. The influence of biases and the model could be used as a basis to counter implementation barriers.

To the best of the authors’ knowledge, this is the first study that connects the cognitive perspectives of Lean business processes in a multi-cultural environment to identify the cognitive biases that influence Lean practices in organisations that were previously committed to Lean practices. The novel findings indicate that nine new biases and 45 previously identified biases influence Lean implementation and practices in a multi-cultural environment. The second novelty of this study shows the connection between cognitive biases, Lean implementation and practices in multi-cultural business processes.

Using a soft-hard continuum of drivers and barriers, this research seeks to explain wood companies' adoption of circular economy (CE) practices.

Multiple interviews, complemented by secondary documents and site observations were used to investigate three wood-based companies that have adopted CE practices. The 10R framework and soft-hard continuum are used to guide data analysis.

The adoption of 10R practices were explained by soft-factor incentives of leaders' values and vision and openness for innovation, all within a regulatory void, and eventually overcome hard-factor barriers of process development, supply chain capability and customer behaviours at product end-of-life.

Crucial for CE model adoption are leaders' positive attitudes, subsequently grown across the companies. The 10Rs are a prompt for CE practice adoption to capture and retain value and generate revenue. Collaboration across the supply chain, including customers and other value capture companies (e.g. repurposing companies), is essential to maximise value retention. Government should play an increased soft-factor incentive regulatory role and support CE practices to overcome hard-factor barriers.

This study contributes an explanation of CE adoption within a relatively unsupported context. Despite the regulatory void, CE practice adoption was driven by leader values. To achieve their vision and overcome the numerous barriers, suppliers and customers required a large investment in education. Indeed, customer behaviour, previously thought to be an incentive for CE adoption, is also identified as a barrier.

Prior literature has widely established that the design of storage locations impacts order picking task performance. The purpose of this study is to investigate the performance impact of unit loads, e.g. pallets or rolling cages, utilized by pickers to pack products after picking them from storage locations.

An empirical analysis of archival data on a manual order picking system for deep-freeze products was performed in cooperation with a German brick-and-mortar retailer. The dataset comprises N = 343,259 storage location visits from 17 order pickers. The analysis was also supported by the development and the results of a batch assignment model that takes unit load selection into account.

The analysis reveals that unit load selection affects order picking task performance. Standardized rolling cages can decrease processing time by up to 8.42% compared to standardized isolated rolling boxes used in cold retail supply chains. Potential cost savings originating from optimal batch assignment range from 1.03% to 39.29%, depending on batch characteristics.

This study contributes to the literature on factors impacting order picking task performance, considering the characteristics of unit loads where products are packed on after they have been picked from the storage locations. In addition, it provides potential task performance improvements in cold retail supply chains.

This research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework (IQ4.0F) for quality improvement (QI) based on Six Sigma and machine learning (ML) techniques towards ZDM. The IQ4.0F aims to contribute to the advancement of defect prediction approaches in diverse manufacturing processes. Furthermore, the work enables a comprehensive analysis of process variables influencing product quality with emphasis on the use of supervised and unsupervised ML techniques in Six Sigma’s DMAIC (Define, Measure, Analyze, Improve and Control) cycle stage of “Analyze.”

The research methodology employed a systematic literature review (SLR) based on PRISMA guidelines to develop the integrated framework, followed by a real industrial case study set in the automotive industry to fulfill the objectives of verifying and validating the proposed IQ4.0F with primary data.

This research work demonstrates the value of a “stepwise framework” to facilitate a shift from conventional quality management systems (QMSs) to QMSs 4.0. It uses the IDEF0 modeling methodology and Six Sigma’s DMAIC cycle to structure the steps to be followed to adopt the Quality 4.0 paradigm for QI. It also proves the worth of integrating Six Sigma and ML techniques into the “Analyze” stage of the DMAIC cycle for improving defect prediction in manufacturing processes and supporting problem-solving activities for quality managers.

This research paper introduces a first-of-its-kind Quality 4.0 framework – the IQ4.0F. Each step of the IQ4.0F was verified and validated in an original industrial case study set in the automotive industry. It is the first Quality 4.0 framework, according to the SLR conducted, to utilize the principal component analysis technique as a substitute for “Screening Design” in the Design of Experiments phase and K-means clustering technique for multivariable analysis, identifying process parameters that significantly impact product quality. The proposed IQ4.0F not only empowers decision-makers with the knowledge to launch a Quality 4.0 initiative but also provides quality managers with a systematic problem-solving methodology for quality improvement.

With the emergence of the COVID-19 pandemic, the production shortage of personal protective equipment (PPE), such as surgical masks, has become increasingly significant. It is vital to quickly provide high-quality, hygienic PPE during pandemic periods. This comprehensive case study aims to confirm that Kaizen and 5S applications reduce wastage rates and stoppages, which as a result, created a more efficient and sustainable workplace in a small–mediumenterprise (SME) producing PPE in Turkey.

The method for this case is discussed with the help of a flowchart using the DMAIC cycle: D-define, M-measure, A-analyse, I-improve and C-control.

The total stoppages due to fishing line, gripper, piston and yarn welding have decreased by approximately 42.4%. As a result of eliminating wasted time and reduced changeovers, a total of 5,502 min have been saved per month. This increased production of approximately 10.55% per month, led to an addition of 506,184 units.

The use of lean manufacturing (LM), Six Sigma, Lean Six Sigma and continuous improvement methodologies are not common in textile SMEs. Based on the current literature reviewed, to the best of the authors’ knowledge, this is the first comprehensive case study that combines statistical tools, such as hypothesis tests and LM practices, in the production process for a PPE company operating as a textile SME.

Within a Europe highly concerned about becoming a zero net greenhouse gas emission continent, and where the circular economy is strongly promoted as a proper lever in this sense, the present paper aims to raise awareness regarding best practices towards a “green” food retail sector in Romania. In a more specific regard, the “green” practices implemented by the main food retailers acting on the Romanian market are analysed, focusing on what these practices are, how they are measured and how transparent they are.

The paper is based on desk research consisting in the identification and content analysis of the “green”-related information outlined on the food retailers' websites and in their published sustainability reports.

Food retailers are concerned about becoming carbon neutral, implementing the “green” measures outlined in the paper through the lenses of waste management, minimising/eradicating plastic, reducing/eliminating food waste, energy efficiency/saving, water consumption efficiency, protecting biodiversity, transportation efficiency and compliance with standards/obtained certifications/adherence to pacts. In addition to actions, food retailers are concerned about their proper, continuous monitorization, tackling key indicators in four directions: overall impact on the environment, impact on resources, waste impact and transportation impact.

The paper provides a novel, exhaustive best practices guide to encourage the transition to a “greener” activity in the Romanian food retail sector, highlighting both action measures and key assessment indicators.

Facing the diverse needs of large-scale customers, based on available railway service resources and service capabilities, this paper aims to research the design method of railway freight service portfolio, select optimal service solutions and provide customers with comprehensive and customized freight services.

Based on the characteristics of railway freight services throughout the entire process, the service system is decomposed into independent units of service functions, and a railway freight service combination model is constructed with the goal of minimizing response time, service cost and service time. A model solving algorithm based on adaptive genetic algorithm is proposed.

Using the computational model, an empirical analysis was conducted on the entire process freight service plan for starch sold from Xi'an to Chengdu as an example. The results showed that the proposed optimization model and algorithm can effectively guide the design of freight plans and provide technical support for real-time response to customers' diversified entire process freight service needs.

With the continuous optimization and upgrading of railway freight source structure, customer demands are becoming increasingly diverse and personalized. Studying and designing a reasonable railway freight service plan throughout the entire process is of great significance for timely response to customer needs, improving service efficiency and reducing design costs.