An Iterative Design Method from Products to Product Service Systems—Combining Acceptability and Sustainability for Manufacturing SMEs

Manufacturing small- and medium-sized enterprises (SMEs) play a crucial role in the economic development and resource consumption of most regions. Conceptually, a product-service system (PSS) can be an effective way to improve the sustainability of manufacturing SMEs. However, the construction of PSSs requires enterprises to integrate a large number of product and service resources. Moreover, current PSS design methods mostly construct a new set of highly service-oriented PSS solutions based on customer needs while seldom considering the combination of acceptability and sustainability for manufacturing SMEs at the initial stage of design, which may lead to the difficulties in applying PSS solutions beyond enterprise integration capacity or result in the waste of existing product resources. Instead of constructing a new PSS solution, this paper proposes the treatment of existing product modules as the original system. The PSS solution is iteratively constructed with the upgrade of the original system in a gradual way, which is driven by systematic performance (this process can be suspended and repeated). Phased iterative design solutions can be applied by manufacturing SMEs according to their development needs. The analytic hierarchy process (AHP), Lean Design-for-X (LDfX), design structure matrix (DSM), and Pearson correlation coefficient (PCC) are combined in an iterative design process from customer needs and system performances to PSS solutions. The feasibility of the proposed method is verified through the iterative design case from electric pallet trucks to warehousing systems. It is proved that this method is more sustainable and easier to be accepted by manufacturing SMEs than existing PSS design methods through in-depth interviews with entrepreneurs.

Machine learning application for sustainable agri-food supply chain performance: a review

The agri-food supply chain consists of activities in “farm-to-fork” order, including agriculture (i.e., land cultivation and crop production), production processes, packaging, warehousing systems, distribution, transportation, and marketing. Data analytics hold the key to ensuring future food security, food safety, and ecological sustainability. While emerging ‘smart’ technologies such as the internet of things, machine learning, and cloud computing can change production management practices. The current study presents a systematic review of machine learning (ML) applications in the agri-food supply chain. This framework identifies the role of ML algorithms in providing real-time analytical insights to assist proactive data-driven decision-making processes in the agri-food supply chain. It also guides researchers, practitioners, and policymakers on successful management to increase the productivity and sustainability of agri-food.

AN INTEGRATED DECISION-MAKING MODEL FOR EFFICIENCY ANALYSIS OF THE FORKLIFTS IN WAREHOUSING SYSTEMS

In the logistics world, special attention should be given to warehousing systems, cost rationalization, and improvement of all the factors that affect efficiency and contribute to smooth functioning of logistics subsystems. In real time industrial practice, the issue of evaluating and selecting the most appropriate forklift involves a complex decision-making problem that should be formulated through an efficient analytical model. The forklifts efficiency plays a very important role in the company. The forklifts are being used on a daily basis and no logistical processes could be done without them. Therefore, it has been decided to determine their efficiency, which will contribute to the optimization of the process in this logistics subsystem. This study puts forward an integrated forklift selection model using Data Envelopment Analysis (DEA), Full Consistency Method (FUCOM) and Measurement Alternatives and Ranking According to the Compromise Solution (MARCOS) methods. Five input parameters (regular servicing costs, fuel costs, exceptional servicing costs, total number of all minor accidents and damage caused by forklifts) and one output parameter (number of operating hours) were first identified to assess efficiency of eight forklifts in a warehousing system of the Natron-Hayat company using the DEA model. This step allows sorting of efficient forklifts which are subsequently evaluated and ranked using FUCOM and MARCOS methods. A sensitivity analysis is also performed in order to check reliability and accuracy of the results. The findings of this research clearly show that the proposed decision-making model can significantly contribute to all spheres of business applications.

The impact of Logistics 4.0 on corporate sustainability: a performance assessment of automated warehouse operations

PurposeThe purpose of this study is to gain a better understanding of the impacts of Logistics 4.0 initiatives (focusing on automated warehousing systems) on the economic, environmental and social dimensions of firms' sustainability performance. To achieve this objective, a new framework for the assessment of sustainable warehousing in the 4.0 era is developed.Design/methodology/approachThe framework, developed via the item-objective congruence index, Q-sort method and interviews with experts, is employed to assess performance changes through management interviews in two warehousing companies after the implementation of automation technologies.FindingsMost aspects of both companies' sustainability performance are considerably improved (e.g. productivity, accuracy, air emission, worker safety and supply chain visibility); however, the outcome for some criteria might be worsened or improved depending on each company's solutions and strategies (e.g. increasing electricity bills, maintenance costs and job losses).Practical implicationsThe findings provide insight into the effective implementation of warehousing technologies. The proposed framework is also a valid and reliable instrument for sustainability assessment for warehousing operators, which companies can utilise for self-assessment.Originality/valueThis paper contributes to establishing a body of literature that explores the previously unclarified effects of Logistics 4.0 on firms' sustainability performance. The proposed framework, which captures critical concerns of corporate sustainability and technological adaptation, is also the first of its kind for warehouse performance assessment.

Development of Automatic Storage Retrieval System for Variable Loads

Automated Storage and Retrieval Systems (ASRS) have been widely used in warehousing systems to speed up load movements and save storage space. ASRS is an integrated system that is equipped with a controller and arm for the collection and storage of goods. This paper discusses the results of developing a system for taking and storing goods for various loads. The prototype element consists of a mechanism for retrieving, placing and application for data collection into the database. In this research, the design and development of ASRS was carried out to be applied in the storage of products of various sizes which is suitable for small size industries. The development process includes investigating features that have been developed in the ASRS, operating procedures, hardware selection and software development in accordance with the mechanism designed. Numerical control which moves the carrier element with high resolution is applied to be able to place the load in a changing position. Development and testing is carried out to ensure the performance of the tool runs well and the data storage that includes the identification and size of the load can be recorded properly.

BI warehousing system based on big data

In the wave of informatization, the data generated by enterprise operations has increased rapidly, prompting the intelligent development of enterprise warehousing systems. In the development of BI warehousing systems, the application of big data technology can promote the rapid development of business intelligence warehousing systems. The application of big data technology in the BI warehousing system can improve the service quality of the data intelligence of the warehousing system. Based on data, it provides support for corresponding decision-making, thereby improving the enterprise data management system. Therefore, this article mainly conducts research and analysis on the construction of BI warehousing system under the application of big data technology, and aims to provide a certain reference value for similar events in the future through a detailed explanation of the current situation of BI warehousing system construction and big data technology application.

Task Scheduling Problem of Double-Deep Multi-Tier Shuttle Warehousing Systems

Double-deep multi-tier shuttle warehousing systems (DMSWS) have been increasingly applied for store-and-retrieval stock-keeping unit tasks, with the advantage of a reduced number of aisles and improved space utilization. Scheduling different devices for retrieval tasks to increase system efficiency is an important concern. In this paper, a Pareto optimization model of task operations based on the cycle time and carbon emissions is presented. The impact of the rearrangement operation is considered in this model. The cycle time model is converted into a flow-shop scheduling model with parallel machines by analyzing the retrieval operation process. Moreover, the carbon emissions of the shuttle in the waiting process, the carbon emissions of the lift during the free process, and the carbon emissions of the retrieval operation are considered in the carbon emissions model, which can help us to evaluate the carbon emissions of the equipment more comprehensively during the entire retrieval task process. The elitist non-dominated sorting genetic algorithm II (NSGA-II) is adopted to solve the non-linear multi-objective optimization function. Finally, a real case is adopted to illustrate the findings of this study. The results show that this method can reduce carbon emissions and improve system efficiency. In addition, it also help managers to reduce operational costs and improve the utilization of shuttles.

Adoption of Automatic Warehousing Systems in Logistics Firms: A Technology–Organization–Environment Framework

The adoption of automatic warehousing systems, a type of green technology, has been an emerging trend in the logistics industry. In this study, we develop a conceptual model using a technology–organization–environment framework to investigate the factors which influence logistics firms to adopt green technology. Our model proposes that the adoption of green technology is influenced by perceived advantage, cost, technological turbulence, business partner influence, firm size, firm scope and operational performance. The objective of this study is to identify the conditions, as well as the contributing factors, for the adoption of automatic warehousing systems in logistics firms. Data were collected from 98 firms in China, and structural equation modeling with partial least squares is adopted to analyze the data. The results suggest that high perceived relative advantage, firm size, cost, firm scope, operation performance, technological turbulence and influence of business partners are important factors affecting IT adoption in small businesses. Therefore, decision support should be provided for enterprises from the three aspects of technology, organization and environment to improve the adoption of automatic warehousing systems.

Formulations for joint order picking problems in low-level picker-to-part systems

This article introduces several mathematical formulations for the joint order picking problem (JOPP) in low-level picker-to-part warehousing systems. In order to represent real warehousing environments, the proposed models minimize performance measures such as travel distance, travel time and tardiness, considering multi-block warehouses, due dates, and multiple pickers. The number of constraints and decision variables required for each proposed model is calculated, demonstrating the complexity of solving medium and long-sized problems in reasonable computing time using exact methods. The proposed models can be followed as a reference for new solution methods that yield efficient and fast solutions.