Environmental impact of warehousing: A scenario analysis for the United States
Abstract and Figures
In recent years, there has been observed a continued growth of global carbon dioxide emissions, which are considered as a crucial factor for the greenhouse effect and associated with substantial environmental damages. Amongst others, logistic activities in global supply chains have become a major cause of industrial emissions and the progressing environmental pollution. Although a significant amount of logistic-related carbon dioxide emissions is caused by storage and material handling processes in warehouses, prior research mostly focused on the transport elements. The environmental impact of warehousing has received only little attention by research so far. Operating large and highly technological warehouses, however, causes a significant amount of energy consumption due to lighting, heating, cooling and air condition as well as fixed and mobile material handling equipment which induces considerable carbon dioxide emissions. The aim of this paper is to summarise preliminary studies of warehouse-related emissions and to discuss an integrated classification scheme enabling researchers and practitioners to systematically assess the carbon footprint of warehouse operations. Based on the systematic assessment approach containing emissions determinants and aggregates, overall warehouse emissions as well as several strategies for reducing the carbon footprint will be studied at the country level using empirical data of the United States. In addition, a factorial analysis of the warehouse-related carbon dioxide emissions in the United States enables the estimation of future developments and facilitates valuable insights for identifying effective mitigation strategies.
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... From the main model from Section 3, we take the constraints that steer the assignment of containers to pickers and buffers, excluding time sequencing constraints. Therefore, constraints (19) and (20) correspond to constraints (2) and (3), constraints (21)-(23) correspond to constraints (12)- (14). We define only the quantity decision variable (24) which corresponds to the decision variable (16). ...
... The search was completed, and all instances were solved optimally. The number of solutions varied from 1 to 4 for the 1st layout (instances [11][12][13][14][15][16][17][18][19][20], from 1 to 6 for the 2nd layout (instances 51-60), and from 1 to 5 for the 3rd layout (instances 91-100). ...
... The average time spent on solving was 7.06 s and varied from 5.13 s to 10.88 s for the 1st layout (instances [11][12][13][14][15][16][17][18][19][20]. The average time spent solving was 3.69 s and varied from 2.56 s to 9.39 s for the 2nd layout (instances 51-60). ...
The necessity for undertaking this research is driven by the prevailing challenges encountered in logistic centers. This study addresses a logistic order-picking issue involving unidirectional conveyors and buffers, which are assigned to racks and pickers with the objective of minimizing the makespan. Subsequently, two variations of a two-step matheuristic approach are proposed as solution methodologies. These matheuristics entail decomposing the primary order-picking problem into two subproblems. In the initial step, the problem of minimizing the free time for pickers/buffers is solved, followed by an investigation into minimizing order picking makespan. An experimentation phase is carried out across three versions of a distribution center layout, wherein one or more pickers are allocated to one or more buffers, spanning 120 test instances. The research findings indicate that employing a mathematical programming-based technique holds promise for yielding solutions within reasonable computational timeframes, particularly when distributing products to consumers with limited product variety within the order. Furthermore, the proposed technique offers the advantages of expediency and simplicity, rendering it suitable for adoption in the process of designing and selecting order-picking systems.
... Among the greenhouse gas emissions in the logistics sector, a share of 11% is attributed to warehousing activities [27]. The consumptions regarding the HVAC systems, lighting, and material handling equipment are the major contributors to warehouse consumption [28]. Amidst the continuous growth in warehouse demand, their energy efficiency and the significance of their consumption are wildly overlooked [29]. ...
In the present chapter, the applications of providing flexibility in buildings and the corresponding commonly employed methodologies are first reviewed. Subsequently, physics-based simulations are performed to model the behavior of a warehouse that is undergoing flexibility events, enabled through the cooling system's setpoint adjustments. The resulting data is employed to train Machine Learning (ML)-based pipelines that predict the event's duration. Accordingly, seven ML algorithms are trained using the obtained features and their estimation performance is compared. The best-performing pipeline is then employed in a feature selection process to find the most relevant features. Results indicate that employing the identified most promising algorithm (random forests) permits predicting the flexibility duration with an average mean absolute percentage error of 6.75%, providing a suitable tool for planning participation in demand response programs or charging electric vehicles.
... A sustainable warehouse layout takes into account all the auxiliary factors that have an impact on the facility, such as product packaging and transportation strategies. The implementation of sustainability practices in warehouses must take into account a number of factors, including the emergence of new technologies, market and competitive pres- sures, new government regulations or policies, supply chain disruptions, the price of raw materials, and a lack of resources [40]. ...
Customers seek items at low prices, excellent quality, and customization in today’s market. Additionally, the need for quick product delivery is rising because of the expansion of e-commerce. Order picking is an essential element of order fulfillment and is regarded as one of the most costly and time-consuming tasks for warehouses. The study aims to create a model for the ordering in logistics that involves the group of racks with assigned one-way conveyor and buffers. The focus of this study is on improving the customer order completion time because of appropriate tasks assigned to pickers. The simulation was performed using the CPLEX solver. The findings highlight the need for picking operations to manage time resources, as well as the importance of sustainable work of order pickers in logistic centers in fostering sustainable performance outcomes.
... use of recent construction materials able to reduce dispersions, loading docks with insulated doors) and alternative construction materials Jumadi and Zailani (2010), Rai et al. (2011), Ries et al. (2017, Perotti et al. (2022) Energy consumption reduction by means of energy efficient heating and lighting systems that also leverage alternative energy sources and sensors/smart metering Lieb and Lieb (2010), Ciliberti et al. (2008), Lin and Ho (2008), Centobelli et al. (2017), Sureeyatanapas et al. (2018), Sellitto et al. (2019), Perotti et al. (2022) Water usage minimization Murphy and Poist (2000), Jumadi and Zailani (2010), Laguir et al. (2021) Adoption of green/energy-efficient material handling equipment Jumadi and Zailani (2010), Meneghetti and Monti (2015), Fichtinger et al. (2015), Ries et al. (2017) Operational practices (e.g. Travel distance optimization, Optimal scheduling of material handling activities and battery charging) Fichtinger et al. (2015), Ries et al. (2017) Green IT Adoption of IT systems to monitor the environmental performance and guide actions for improvement Bartolini et al. (2019) Reverse Logistics Product Reverse Logistics Design for recycling Murphy and Poist (2000), Jumadi and Zailani (2010), Lieb and Lieb (2010) Azevedo et al. (2011), Evangelista (2014 Packaging reuse and recycling Murphy and Poist (2000), Ciliberti et al. (2008), Lieb and Lieb (2010), Jumadi and Zailani (2010), Centobelli et al. (2017) Use of sustainable materials Environmental sustainability monitoring Introduction of reporting systems to monitor sustainability goals and their achievement Azevedo et al. (2011), Abbasi and Nilsson (2016), Nilsson et al. (2017), Martins et al. (2022) Definition of sustainable KPIs, environmental targets and priorities Lieb and Lieb (2010), Nilsson et al. (2017), Huge-Brodin et al. (2020) Research activities and investments for green innovation Colicchia et al. (2013), Abbasi and Nilsson (2016), Sellitto et al. (2019) Offices and employees Incentives programs for environmental suggestions by employees Murphy and Poist (2000), Lieb and Lieb (2010), Martins et al. (2022) Personnel training to increase awareness Murphy and Poist (2000), Lieb and Lieb (2010), Colicchia et al. (2013), Centobelli et al. (2017) Supply chain traceability Purchases tracking through the entire supply Perotti et al., 2022). GLPs also require significant investments, which represent important barriers when dedicated assets must be acquired by companies (Gotschol et al., 2014;Hrovatin et al., 2016). ...
Purpose
There is a growing body of literature discussing the green logistics practices (GLPs) that companies could introduce to reduce the logistics environmental impact. Current approaches also identify several influencing factors within firms that could serve as barriers to, or enablers of, GLPs. However, less is known about the role of extra-firm stakeholders, even though these are crucial to operationalizing green logistics effectively. This study merges current theoretical understanding with empirical evidence to provide a detailed stakeholder analysis of GLPs.
Design/methodology/approach
Using stakeholder theory as a theoretical lens, the authors aimed at offering a mid-range contribution by conducting multiple embedded case studies examining Italian logistics service providers and shippers. GLPs and the related influencing factors were examined as sub-units of analysis within broader companies' environmental sustainability strategies.
Findings
The authors identified cascading effects among factors influencing the adoption of GLPs (e.g. key economic factors are affected by external factors which also influence organizational and collaboration factors). These effects are moderated by interdependencies between primary and secondary stakeholders, and the study highlights the prominent involvement of secondary stakeholders, such as final consumers.
Originality/value
This paper contributes to better understanding how and why companies adopt GLPs, emphasizing the wide set of stakeholders involved and illustrating how different stakeholders impact on GLPs adoption by affecting a set of influencing factors. By combining insights from the available literature with contemporary empirical data, the authors emphasize how Logistics Service Providers (LSPs) and shippers can no longer address the adoption of GLPs as “focal companies”, but only as part of a “focal network of interconnected stakeholders”, all of them influencing GLPs adoption.
Purpose
The purpose of this paper is to propose a framework of green strategies as a combination of energy-efficiency measures and solutions towards environmental impact reduction for improving environmental sustainability at logistics sites. Such measures are examined by discussing the related impacts, motivations and barriers that could influence the measures' adoption. Starting from the framework, directions for future research in this field are outlined.
Design/methodology/approach
The proposed framework was developed starting from a systematic literature review (SLR) approach on 60 papers published from 2008 to 2022 in international peer-reviewed journals or conference proceedings.
Findings
The framework identifies six main areas of intervention (“green strategies”) towards green warehousing, namely Building, Utilities, Lighting, Material Handling and Automation, Materials and Operational Practices. For each strategy, specific energy-efficiency measures and solutions towards environmental impact reduction are further pinpointed. In most cases, “green-gold” measures emerge as the most appealing, entailing environmental and economic benefits at the same time. Finally, for each measure the relationship with the measures' primary impacts is discussed.
Originality/value
From an academic viewpoint, the framework fills a major gap in the scientific literature since, for the first time, this study elaborates the concept of green warehousing as a result of energy-efficiency measures and solutions towards environmental impact reduction. A classification of the main areas of intervention (“green strategies”) is proposed by adopting a holistic approach. From a managerial perspective, the paper addresses a compelling need of practitioners – e.g. logistics service providers (LSPs), manufacturers and retailers – for practices and solutions towards greener warehousing processes to increase energy efficiency and decrease the environmental impact of the practitioners' logistics facilities. In this sense, the proposed framework can provide valuable support for logistics managers that are about to approach the challenge of turning the managers' warehouses into greener nodes of the managers' supply chains.
Rapid industrialization and global supply chains have magnified industrial activities' carbon footprint, resulting in environmental hazards. Several countries have magnified their efforts to reach carbon neutrality goals. The increased attention to carbon-neutral goals has been enabled by United Nations (UN) calls for a carbon-neutral economy. In this context, supply chains will have greater responsibilities to reduce the network's carbon footprint. To achieve carbon neutrality goals, decision-makers must be well-informed and facilitated with suitable theoretical frameworks. In literature , the adoption of intelligent technologies is suggested to construct efficient and smart supply chains. However, there are uncertainties regarding the impact of this transformation on the carbon neutrality goal. The present study caters to the uncertainties by discussing its potential impact on carbon neutrality goals and how to use adopted technologies to achieve the same. A list of 11 potential strategies is identified and modelled to provide a strategic roadmap for smart supply chains' adoption. The proposed framework aims to facilitate carbon neutrality goals. The findings indicate that decision-makers must pay significant attention to circular economy practices. Additionally, green transportation is an impactful area with significant potential to support carbon neutrality goals. Critical insights have been extracted regarding stakeholders' training and process innovation. The study findings can help decision-makers achieve carbon neutrality goals. K E Y W O R D S carbon neutrality, carbon reduction, Industry 4.0, smart supply chain, strategies, sustainability
Within logistics operations, sustainable warehousing has achieved increasing attention among academics and practitioners. Practitioners like Logistics Service Providers (LSPs) have started to perceive the need for measuring the environmental performance of their logistics hubs and searching for practices and solutions towards greener warehousing processes. Besides, a rising number of academic contributions have emerged addressing sustainable warehousing, especially from a conceptual viewpoint. However, empirical evidence is still lacking on the assessment of warehouse environmental performance, and very few studies offer an in-depth discussion on the quantification of operational greenhouse gas (GHG) emissions at logistics hubs. This paper aims at addressing this research gap. Based on an extensive international market study the paper discusses some preliminary results on energy efficiency and GHG emissions at logistics hubs. Specifically, an initial international benchmark between Italy and Germany is offered in terms of consumption and emission figures split by features of the logistics hub, thus paving the way to the definition of an initial set of relevant GHG emission indicator values that can be taken into account for measuring the sustainability performance of European logistics hubs.KeywordsSustainable warehousingDecarbonisationCarbon footprint
Background: With the continuing growth of warehouses globally, there is an increasing need for sustainable logistics solutions in warehousing, but research linking warehouse management systems (WMS) and sustainability is lacking. Methods: A systematic literature review and bibliometric analysis were conducted in Scopus and Web of Science databases from 2006 to 2022 to investigate academic knowledge of WMS contributing to warehouses’ social and environmental sustainability. Results: Findings revealed only 12 topic-relevant articles from 2013 to 2022, primarily published recently. More recent articles have received more citations than earlier published works. The articles were from multiple research fields, such as business economics, engineering, computer science, and social sciences, with only one article on environmentally sustainable technologies. The top keywords were “warehouse management system”, “internet of things”, “industry 4.0” and “supply chain”. Only six articles had environmental sustainability terms in the keywords. Findings show more discussions about social rather than environmental sustainability. Most studies suggest integrating WMS with other systems to support sustainability efforts in warehousing. Conclusions: The study addressed a gap in academic literature regarding WMS and sustainability. Research findings added knowledge of practical activities to achieve warehouse operations and performance sustainability and proactively reduce warehouse operations’ environmental and social impacts.
There has been considerable research on the environmental impact of supply chains but most of this has concentrated on the transport elements. The environmental impact of warehousing has received relatively little attention except within the context of distribution networks. A high proportion of total warehouse emissions emanate from heating, cooling, air conditioning and lighting and these aspects are largely related to warehouse size. This in turn is greatly influenced by inventory management, affecting stockholding levels, and warehouse design, affecting the footprint required for holding a given amount of stock. Other emissions, such as those caused by material handling equipment, are closely related to warehouse throughput and equipment choice. There is a substantial gap in the literature regarding this interaction between inventory and warehouse management and its environmental impact. The purpose of this paper is to contribute to filling this gap. Therefore, an integrated simulation model has been built to examine this interaction and the results highlight the key effects of inventory management on warehouse-related greenhouse gas emissions. In particular, it is found that decisions on supply lead times, reorder quantities, and storage equipment all have an impact on costs and emissions and therefore this integrated approach will inform practical decision making. Additionally, it is intended that the paper provides a framework for further research in this important area.
Production ramp-up is a critical step in the life cycle of a new product, and efficiently managing ramp-ups is a key to business success and market leadership. To support the planning of ramp-ups in practice, researchers have developed decision support models in the past that help to solve problems that arise during the ramp-up phase, such as lot sizing, the assignment of workers to workplaces or the determination of the capacity of the production equipment. Decision support models for production ramp-up typically consider the specific characteristics of this phase, such as uncertainty, growth in demand, worker learning or imperfect production processes. The aim of this paper is to provide a comprehensive overview of decision support models for production ramp-up and to identify areas where more research is needed. First, the paper develops a conceptual framework of production ramp-up by categorising typical planning problems and process characteristics of the ramp-up phase. Secondly, a systematic literature review with a focus on mathematical planning models for the ramp-up phase is conducted. The analysis shows that various decision support models that help to realise an efficient production ramp-up exist, but that there are still many opportunities for future research in this area.
An optimisation model for the sustainable design of refrigerated automated storage and retrieval systems is proposed, which takes into account specific features of the food supply chain, such as temperature control. Rack configuration as well as surfaces and volumes of the cold cell are conjointly optimised in order to minimise the total yearly cost of the automated storage facility, introducing energy requirements both for refrigeration and picking operations explicitly, other than investment costs. Crane plus satellite systems are modelled in order to enable deep lane solutions and space savings, as suitable for cold storage. The model allows a deep analysis of the impact of supply chain decision variables, such as the facility location, the storage temperature and the incoming product temperature on costs, energy use and carbon dioxide emissions, so that storage facilities attributes for supply chain design models can be properly assessed to re-optimise the whole cold chain. The design problem is modelled and solved by Constraint Programming in order to easily manage non-linear functions.
Environmental impacts, such as GHC emissions, have been introduced to supply chain management as an additional parameter to traditional cost, lead-time and on-time delivery. Supply chain management represents a significant source of decisions affecting the eco-efficiency of many products. This paper analyses cases from the food industry, mainly order-picking, transportation, warehousing, and distribution aspects from the greening point of view. Three case examples of decisions in supply chain design in the food industry are considered. The results show dependencies between performance measures. Finally, a framework of decisions and their impact on performance is presented.