Exploring Green Inventory Management through Periodic Review Inventory Systems—A Comprehensive Literature Review and Directions for Future Research

Abstract

With the increasing environmental concerns and legislative pressures, the focus on incorporating ecologically sustainable practices into inventory management systems has grown, leading to the emergence of green inventory management. However, this field is not without its challenges, with numerous conflicting real-world constraints and goals. A comprehensive literature review targeting green inventory management operating under a periodic review inventory system was conducted to identify research gaps and potential directions for future research. Despite the growing interest in the field, this review highlighted the scarcity of relevant studies. Out of the 1272 papers reviewed, only 16 studies, or 1.3%, met the criteria for exploring periodic review inventory systems while simultaneously considering environmental and economic aspects. These studies were further analyzed in detail and categorized according to key classification criteria. The future research directions highlighted the need for additional studies on periodic review inventory systems operating under stochastic market demand in the context of green supply chain management. The standardization of emission calculation methodologies was also emphasized as a crucial step towards aligning inventory management practices with the aim of increasing inventory management efficiency and the related improvement in the environmental performance of supply chains.

1. Introduction

Traditional supply chain management (SCM) focuses on efficiently moving products from suppliers to customers to maximize profit and enhance service levels, with effective inventory management being crucial for both goals. It is often reactive, with companies responding to demand changes or disruptions rather than anticipating them. Each supply chain (SC) area focuses on its specific tasks, with limited communication or collaboration between functions [1]. However, even traditional SCM represents a highly complex problem due to many influential variables, stochastic market processes, issues of the optimal management of production and supplies, and the human factor involved in all levels of decision-making processes. Conversely, modern SCM has significantly evolved. During the quality revolution of the 1980s and the SC revolution of the 1990s, it became clear that successful business practices must integrate environmental principles into operational decision-making [2]. Modern SCM increasingly uses technology and data analytics to optimize operations and improve efficiency, with many companies employing sophisticated software systems for real-time visibility into inventory, production capacity, and delivery schedules [3,4,5]. Green supply chain management (GSCM) incorporates environmentally sustainable principles into every aspect of SCM, with green inventory management being its key process that impacts the environment through the activities of procurement, storage, and transport.

The research motivation and practical significance of studying green inventory management lie in delivering actionable insights and strategies that enable businesses to operate more sustainably. By implementing green practices, companies can achieve a balance between economic performance and environmental responsibility, ensuring long-term success and contributing to global sustainability goals. The focus of this paper is to provide a systematic literature review of green inventory management studies using a periodic review inventory system to offer structured insights from operational, economic, and environmental perspectives. The main contributions are to (a) review the literature; (b) analyze it by the application context and key classification criteria (inventory management system, market demand type, response to excess demand, environmental impact, cost structure, modeling approach, and optimization criteria); and (c) discuss trends and identify research gaps and potential directions for future research.

1.1. Green Supply Chain Management

The growing importance of GSCM both in the literature and practice stems from several factors, including the reinforcement of environmental protection regulations, the diminishing accessibility of natural resources such as energy sources, oil, and gas, coupled with rising prices, the impact of climate change, environmental degradation, and the escalating market demands. Incorporating the ‘green’ aspect into SCM entails recognizing and managing the interconnections and interdependence between SCM and the natural environment. This approach tends to minimize the environmental impact of the supply chain’s activities by reducing greenhouse gas emissions, minimizing waste, conserving natural resources, or promoting the use of renewable energy sources while at the same time creating value for the organization and its stakeholders [2,6]. Managerial practice related to environmental issues in companies no longer implies only limited pollution control measures but instead includes a broader range of management programs, technologies, and decisions [7,8]. For instance, given that the transport and logistics sector contributes to roughly 25% of worldwide emissions, there is widespread interest in discovering methods to enhance the efficiency of supply chains [9]. Operational adjustments and efficient inventory control are efficient ways to mitigate emissions without significant investments in new technologies [10,11,12]. This aligns with the current EU climate targets that require a reduction in net greenhouse gas (GHG) emissions by at least 55% by 2030 compared to 1990 levels [13].

1.2. Green Inventory Management

Integrating ecologically acceptable solutions into inventory management systems becomes an important issue for companies nowadays, with the growing tendency to change their environmental performance management from purely reactive to proactive and planned. Compared to the traditional way, modern inventory management systems are characterized by a continuous increase in customer demand and incorporate elements such as just-in-time inventory, mass customization, smaller batch sizes, shorter product life cycles, etc. These factors collectively result in a notable increase in transportation costs and adverse environmental effects [14]. Common practices associated with green inventory management usually include the efficient management of inventory levels, sustainable packaging, reducing transportation emissions, energy-efficient storage, adopting a circular economy approach, etc. [15]. Inventory management in the context of GSCM, according to [2], is presented in Figure 1. It defines inventory management as a part of green manufacturing and remanufacturing within the green operations area.

Over the years, many other concepts of GSCM have been presented, introducing, among others, the activities of green transport, green warehousing, and green packaging, such as [16], shown in Figure 2.

The focus of green inventory management is not solely economic, traditionally measured in terms of costs, but is complemented with an environmental perspective, typically measured in terms of emissions. Such a comprehensive approach is required to prevent suboptimal outcomes and achieve economic emission reductions [17]. This approach, however, implies that a balance between the economic benefits of inventory management and its environmental impact needs to be found and potential trade-offs identified and resolved. As pointed out in [18], inventory control often aims to balance competing objectives, where considerable computational resources are needed to implement the advanced and complex decision models that underpin modern inventory control. Green inventory management requires understanding how inventory decisions impact costs and emissions. The goal is to reduce both, but cost-saving measures may not always lower emissions and vice versa. The challenge is to balance these conflicting objectives, reducing transport emissions without compromising customer service or increasing overall costs. Finding optimal solutions that balance cost and emission reductions is a key hurdle in green inventory management [17].

Despite the growing interest in this field, most of the scientific literature on green or sustainable inventory management is based on simple models such as the EOQ or Newsvendor model and deterministic demand [19,20]. The authors of [15] highlight the lack of studies in the field of sustainable inventory management that consider real and accurate inventory management models, such as periodic review inventory systems, instead of basic analysis. Our research will address this study gap.

1.3. Periodic Review Inventory Systems

Modern warehouse management systems allow for a continuous review and update of the inventories, where orders can be placed continuously. Nonetheless, numerous companies and SCs opt for periodic inventory policies, meaning they review inventory levels at fixed intervals and, if needed, place orders with the supplier. This practice is prevalent in the retail industry because it allows for the simultaneous ordering of a wide array of stock-keeping units (SKUs). These intervals are often fixed and determined by calendar schedules or logistics service providers’ distribution schedules, allowing periodic replenishment orders to be placed daily, weekly, or according to similar timeframes. Among periodic review inventory systems, distinguished models, according to [21,22], are the following:

• Periodic replenishment or Basestock (R, S) system in which the inventory level is reviewed at time points t = 0, R, 2R, …, nR, and an order of (S−x), where x is the actual inventory level, is placed at every review interval R to increase the inventory level to order-up-to level S.
• Periodic review (R, s, Q) system, in which the inventory status is reviewed at time points t = 0, R, 2R, …, nR. As soon as the inventory position is equal to or lower than reorder point s at a review point t, an order of size Q is placed, which will arrive at time t + L, where L denotes the lead time.
• Periodic review (R, s, S) system in which inventory status is reviewed at time points t = 0, R, 2R, …, nR. When the inventory position is equal to or lower than reorder point s at review point t, an order of (S−x), where x is the actual inventory level, is placed. The order will arrive at time t + L.

Among them, the (R, s, S) inventory system is often preferred due to its flexibility and ability to reduce inventory holding costs and minimize the risk of stockouts. In addition, under general conditions, it is optimal because it minimizes the expected total replenishment, holding, and shortage costs. This model represents one of the most widely used inventory management approaches in both practical applications and the academic literature [22,23,24], making it a central point of this research. The (R, s, S) inventory system is frequently used in MRP II and ERP business environments, real-world inventory management for items with higher demand, and when there is a need to coordinate orders for various items [18,25]. Besides its relevance and widespread presence, there are no simple procedures or algorithms for determining the optimal values of the characteristic variables of the (R, s, S) system in real-world conditions [26,27]. Consequently, controlling inventories by subjective assessment, without an algorithmic basis, can result in suboptimal inventory management, increased costs, holding too high or too low inventory levels, and a bullwhip effect. The decision-making process is additionally weighted due to conflicting business objectives, such as fulfilling the highest possible percentage of market demand and operating at the lowest possible cost, a requirement for GHG emissions reduction, storage space reduction, etc. [28].

All of the above emphasizes the need for a systematic, multicriteria analysis of the impacts and interdependences of the key variables of a periodic review inventory system in the conditions of stochastic market demand to determine the possibilities for optimal green inventory management. Despite the widespread presence in the practice, actuality, and importance of the topic, we find that research papers that study periodic review inventory systems simultaneously considering economic and environmental aspects are scarce.

2. Key Classification Criteria in Review on Green Inventory Management with Periodic Review Inventory Systems

We initially analyzed the relevant literature to identify the main topics for reviewing green inventory management with periodic review inventory systems. We adopted key classification criteria for this research, as specified below.

2.1. Inventory Management System

The choice of inventory management system depends on the specific characteristics of the business, the product, and the nature of demand. Each common inventory management system, such as continuous or periodic review models, single-period models, (Q, R) models, etc., has strengths and weaknesses depending on the business specifics. Therefore, trade-offs between costs, service levels, and complexity need to be considered when selecting the appropriate one. This paper focuses on periodic review inventory systems, as they consider numerous elements relevant to modern SCs: the distribution type of market demand, mean value and variations in market demand, realistic lead time and review period, inventory management service performance measures, and others [29].

2.2. Type of Market Demand and Response to Excess Demand

In real-world scenarios, demand is typically characterized by uncertainty. For this reason, maintaining stock is a crucial strategy to mitigate uncertainty and ensure customer satisfaction. The significance of stochastic demand models in inventory management is widely acknowledged. Still, most studies incorporating sustainability into inventory control primarily focus on deterministic demand inventory models [17,19]. This research aims to tackle this study gap and focuses on papers that consider stochastic demand.

One of the essential inventory management strategies refers to how the company approaches excess demand and temporary stockouts. It can either be backordered and urgently replenished or treated as lost sales. Although backordering is predominantly present in the relevant literature, according to research [30], only 15% of the customers in a real-life setting in an out-of-stock situation postpone the purchase and wait for the product to be available again. The lost sales environment is ubiquitous in highly competitive sectors like retail, service, spare parts, and online sales [30,31,32]. Bijvank and Vis [33] introduced the approach where models with a penalty cost for lost sales are referred to as cost models and models with a service level constraint as service models. The authors of [33,34,35] indicate that since customer satisfaction is often a differentiation strategy among competitors, and with shortage costs particularly hard to evaluate in practice correctly, service-based requirements are more common in the real-life business sector. Despite the extensive literature devoted to the cost minimization problem, there is a widespread acknowledgment that evaluating penalty costs, especially the cost associated with losing customer goodwill, is often challenging [36]. This challenge largely contributes to the widespread popularity of using service-level measures in practical applications.

2.3. Environmental Impact Considered

Inventory management can have a significant environmental impact linked to ordering, purchasing, transportation, warehousing, and setting up goods, mainly through energy consumption, transportation emissions, and waste generation. When considering the impact of inventory management choices on emissions, ref. [17] highlights that emissions are linked to energy usage. Consequently, decisions that impact the energy consumption in SCs affect its emissions. The authors of [37,38] recognized a strong correlation between carbon emissions and the environmental performance of supply chains, finding that the frequency and method of deliveries, as well as the type and quantity of stored inventory, greatly influence carbon emissions. Additionally, noticing the presence of elevated emissions levels can be significant as it may indicate a lack of efficiency in the process [39]. According to the World Economic Forum [40], approximately 90% of logistics and transport activities emissions can be attributed to freight transportation, while the remaining 10% is associated with operating logistics buildings. If there are products that do not need specific storage conditions, such as temperature control or refrigeration, the emissions generated from transportation would exceed those from storage and backorders. In such instances, reducing the frequency of shipments can reduce transportation emissions without significantly increasing emissions from storage and backorders [41,42]. Waste management in the context of green inventory management implies implementing strategies to minimize waste generation, promote reverse logistics, recycling, and reuse, and reduce the environmental impact of inventory-related activities. Factors like demand variability, choice of the inventory management system, and service level constraint can significantly affect waste generation or reduction and the environmental impact [43].

2.4. Cost Structure

This research focuses on the key categories of costs commonly discussed in the inventory management literature, particularly inventory costs, encompassing costs related to ordering (procuring) items and holding them on stock [44], together with transportation costs associated with the physical distribution of inventory to the warehouse, another facility, or customer [45,46]. Costs for not satisfying market demand on time or out-of-stock costs assume costs due to stockout situations, such as shortage costs, penalties for lost sales, and backordering costs [47]. Depending on the research scope and the author’s methodology, various cost structures can be defined. For instance, transportation costs can be neglected [22] or treated as either fixed or variable components of ordering costs [18], etc.

2.5. Modeling Approach and Optimization Criteria

The techniques employed to achieve the findings identified in the literature search can be classified into three main groups: empirical or analytical methods such as case studies, field surveys, and experiments [48]; mathematical modeling techniques that include linear programming, dynamic programming, Markov chains, regression analysis, and similar methods [49]; and finally, simulation methods, which include simulation experiments, scenario modeling, and sensitivity analysis [50].

The primary research goals of optimizing inventory control policies and parameters include meeting operational service requirements, reducing costs, and addressing multi-objective challenges like minimizing costs and emissions or total inventory costs while adhering to carbon emission constraints [51,52].

Extensive research on studies from the field of multi-echelon inventory management in the conditions of stochastic market demand presented in work [53] emphasizes the importance of multicriteria approach research, where demand fulfillment and cost optimization are reconciled together with included sustainability measures.

3. Review Methodology

The first step of this study was to define the research objective, which is to identify study gaps and potential areas for future research in green inventory management according to the periodic review inventory system. To the best of the author’s knowledge, a focused review and analysis of the relevant literature on the periodic review inventory systems and (R, s, S) inventory model in particular, with environmental and cost considerations, has not yet been conducted.

To explore the relevant academic literature on the research topic, a broad search was conducted in prominent databases and common search engines such as ScienceDirect, Web of Science, Scopus, IEEE Explore, and Google Scholar, focusing on research articles and conference papers published from 2008 to 2023. Books, book chapters, encyclopedias, and PhD theses were excluded from further analysis. The search was conducted with various combinations of the following keywords and Boolean operators: inventory AND (“periodic review” OR “(R, s, S)”) AND (system OR model OR policy OR control OR management) AND (emission OR “greenhouse gas” OR GHG OR “carbon emission” OR “transport emission” OR environmental OR waste) AND (cost OR costs). Figure 3 presents the research methodology used for the literature review, with previously described review steps.

Figure 4 shows the paper selection process during which an initial screening of 1272 selected papers based on their titles and keywords was conducted, aiming to exclude those not relevant to the research subject. Following this screening process, 514 articles were further analyzed by reading their abstracts and subject areas. The subject area is green inventory management; therefore, articles focusing merely on production management, operations research, inventory route optimizations, green transportation, and similar topics were excluded from further analysis. In step 3, after a detailed reading of 138 papers independently by two authors, 16 matched the research criteria.

The key classification criteria, presented in detail in Section 2, are used in the final content analysis of the selected articles.

Table 1. Key classification criteria for reviewed publications.

Authors and Publication Year	Journal	Inventory Management Aspect Considered			Environmental Impact Considered			Cost Component Considered				Model Approach	Optimization Criteria
		PRIS Type	Type of Demand	Response to Excess Demand	Source of GHG or Carbon Emissions	Waste		I Cost	T Cost	OS Cost	Other
						Type	Method of Waste Control or Reduction
Nativi and Lee, 2012 [54]	Int. J. of Production Economics	(R, s, S)	stochastic, Poisson D	backorder	N/A	material from the end-user market	reverse logistics, recycling, reuse	✓	✓	✓	setup, production	MM, S	environmental benefit, costs
Ramadhan and Simatupang, 2012 [55]	Procedia—Social and Behavioral Sciences	other	stochastic, normal D	lost sales, SL control	N/A	EP	optimized perishable I management	✓	N/A	✓	outdating	MM, S	total cost
Mallidis et al., 2014 [56]	Transportation Research Part E: Logistics and Transportation Review	other	stochastic, normal D	SL control	T, operation of distribution center	N/A	N/A	✓	✓	✓	operational	analytical, MM	emissions, costs
Tang et al., 2015 [41]	Int. J. of Production Economics	other	stochastic, normal D	backorder	T, storage I, backordering	N/A	N/A	✓	✓	✓	N/A	analytical, MM	emissions, costs
Rajendran and Ravindran, 2017 [57]	Computers & Industrial Engineering	other	stochastic, normal D	backorder	N/A	EP	optimized perishable I management	✓	N/A	✓	outdating	empirical, MM, S	total cost
Akhtari et al., 2019 [58]	Biosystems Engineering	(R, s, S), other	not defined	lost sales	T, operational processes	N/A	N/A	✓	✓	N/A	operational	empirical, S	demand fulfillment, emissions, costs
Li et al., 2019 [59]	Sustainability	other	stochastic, normal D	backorder	T, I	N/A	N/A	✓	N/A	✓	carbon	MM, S	I cost, SL, carbon cost
Gao et al., 2020 [47]	Computers & Industrial Engineering	other	stochastic, normal D	backorder	T	N/A	N/A	✓	✓	✓	carbon	MM, S	total I cost with carbon emission constraint
Liu and Lin, 2020 [60]	Mathematics	other	stochastic, normal, general D	SL control	T	EP	reverse logistics	✓	✓	✓	GHG emission, returning	MM, S	delivery route, order quantities, reordering point, SL, review interval, max I level
Noh et al., 2020 [61]	Sustainability	other	deterministic or fuzzy	backorder	ordering, holding I	N/A	N/A	✓	N/A	✓	carbon tax	MM, S	total cost
Ramandi and Bafruei, 2020 [62]	Computers & Industrial Engineering	other	stochastic, normal D	backorder, SL control	T	N/A	N/A	✓	✓	✓	penalty/subsidy related to the emissions level	MM, S	profit, emissions
Žic and Žic, 2020 [28]	Advances in Production Engineering & Management	(R, s, S)	stochastic, normal D	lost sales, SL control	T	N/A	N/A	✓	✓	✓	N/A	MM, S	I levels, costs, emissions
Kwak, 2021 [24]	Sustainability	other	stochastic, normal D	backorder	T, holding I	N/A	N/A	✓	✓	✓	indirect (sustainability-related)	S	total cost
Momeni et al., 2022 [63]	J. of Cleaner Production	other	stochastic, normal D	lost sales, SL control	N/A	EP	reverse logistics, regeneration of EP	✓	N/A	✓	disposal, production	MM, S	profit, SL
Buisman and Rohmer, 2023 [43]	Sustainability Analytics and Modeling	other	stochastic, negative binomial D	lost sales, substitution, SL control	N/A	EP, packaging materials	sales management, recycling, incineration, landfill of packaging materials	✓	N/A	N/A	N/A	S	profit, environmental impact
Drent et al., 2023 [64]	European J. of Operational Research	other	stochastic, random	backorder	T	N/A	N/A	✓	✓	✓	N/A	MM, S	total I cost with carbon emission constraint

I—inventory, PRIS—periodic review inventory system, D—distribution, SL—service level, T—transport, OS—out-of-stock, EP—expired products, MM—mathematical modeling, S—simulations.

presents a detailed comparison of the relevant publications based on these classification criteria.

4. Review and Discussion

Among the 1272 papers selected in the initial literature search by the relevant keywords, only 16, or 1.3%, fulfilled the research criteria for exploring periodic review inventory systems while considering environmental and economic aspects within green inventory management. This confirmed the initial presumption of this domain’s lack of significant research.

Table 2. Journal statistics for green inventory management with periodic review inventory systems.

Journal	No. of Papers
Computers & Industrial Engineering	3
Sustainability	3
International Journal of Production Economics	2
Advances in Production Engineering & Management	1
Biosystems Engineering	1
European Journal of Operational Research	1
Journal of Cleaner Production	1
Mathematics	1
Procedia—Social and Behavioral Sciences	1
Sustainability Analytics and Modeling	1
Transportation R. Part E: Logistics and Transportation Review	1

presents the distribution of papers that met the abovementioned research criteria per journal.

Although this research covered the period from 2008 to 2023, the first papers that satisfied all criteria were [54,55], published in 2012. The distribution of publications per year and period is shown in Figure 5. Most of the research (75%) was conducted from 2016 to 2023, indicating a growing interest in this scientific domain. From a journal’s point of view, 50% of all papers were published in three dominant magazines: Sustainability (papers [24,57,59]), Computers & Industrial Engineering (papers [46,55,60]), and International Journal of Production Economics (papers [41,47]). The rest of the papers were published in eight magazines or conference proceedings.

Of the 16 papers examined, each took into account stochastic demand setting, apart from the studies by [61], which assumed deterministic demand assumptions with constant reorder and can-order levels, and [58], where demand was not explicitly delineated.

While the primary focus of this review centers on periodic review inventory systems, several of the examined papers also explore comparative analyses with alternative inventory models. For instance, refs. [54,55] contrast periodic review with continuous review inventory systems, while ref. [60] investigates reordering point inventory systems. Additionally, refs. [43,58] compare periodic review inventory systems with variable and fixed order quantities. Only the works [28,54,58] study (R, s, S) inventory system.

As noted, backordering emerges as the prevailing strategy for handling excess demand in the pertinent literature. Our review reaffirms this observation, as visible from Figure 6, with backordering featured in 56% of the selected papers, particularly in works [24,41,47,54,57,59,61,62,64]. Service level control is assumed in 44% of the studies, i.e., in works [28,43,55,56,60,62,63]. Although relevant for real-world applications, and in particular highly competitive business sectors, the lost sales environment is among the least represented scenarios, present in the works of [28,43,55,58,63]. Product substitution was analyzed only in the work [43].

GHG or carbon emissions are the prevalent environmental impact factor under examination, except in the studies [43,54,55,57,60,63]. In these instances, the environmental focus shifts to waste generation, explicitly addressing the disposal of expired products into the environment, exploring their potential for reuse and recycling, or waste reduction through optimized inventory management.

Studies analyzing environmental impact through emissions released mostly take into consideration emissions coming from transport activities, as visible in Figure 7, as in the works of [24,28,41,47,56,58,59,60,62,64]. Other emission sources are related to inventory holding or storage, as referenced in the works [24,41,59,61], or can be connected to various operational processes.

Different approaches to calculating transport emissions are found in the reviewed studies. In several studies, such as [24,47,54], the method of emission calculations is not specified. In another group of studies, emissions are quantified related to the fuel consumption and the vehicle load but also by using various approaches; the authors of Ref. [56] assume fixed (determined) values of emissions based on the fuel type for full and empty vehicles; the authors of Ref. [41] defined the fuel consumption of an empty vehicle and fuel consumption factor per item of delivered good, with the assumption that the average fuel consumption per item is lower when the truck is fully loaded and travels at the same speed; in the work of [58], transport emissions are calculated by using total transportation time and assumed fuel consumption the truck when traveling for 1 h; the authors of Ref. [47] used data from ‘the experience’, etc.

In work [28], European Standard EN 16258 [65] is used, defining the methodology for calculating and declaring the energy consumption and GHG emissions of transport services. In [64], calculations were conducted according to the NTM methodology.

All the examined studies address the economic aspect of SCM. The majority of these papers encompass all three primary cost components, specifically inventory costs, transportation costs, and penalty or out-of-stock costs, as evident in the works of [24,28,41,47,54,56,60,62,64] and visible in Figure 6. The authors of [24,47,59,60,61,62] acknowledge other cost elements as additional expenses associated with the environmental impact of a company’s inventory and SC operations in the form of a carbon tax, carbon cost, or similar. Governments or organizations often impose this cost to incentivize businesses to reduce their carbon emissions and environmental footprint. Furthermore, we acknowledge setup or operational costs, as evidenced in studies such as [54,56,58]. Additionally, production costs are recognized, as seen in [54,63], or costs related to the return or disposal of expired products, as indicated in [55,57,60,63].

Regarding the research method used, as expected, most of the studies use numerical simulations together with mathematical models. Simulation modeling is widely acknowledged as the most suitable approach for testing and optimizing complex real-world systems. Modern supply chains are systems of high complexity with many opposing influencing factors, and precisely designed simulation experiments are used to examine impacts, explore possibilities, and find optimal solutions and correctly select, understand why, and transfer research results to real-world business systems [66]. Moreover, simulation models usually represent a suitable approach when the relations among components do not conform to simple equations or the equation is unknown [67]. Consequently, simulations are utilized to achieve results in 88% or 14 out of 16 reviewed papers. In contrast, mathematical modeling is employed in 81% or 13 out of 16 papers, and analytical methods are used in 13%, i.e., 2 out of 16 papers.

Approximately 13% of the reviewed studies, or 2 out of 16 papers with an ‘empirical’ model approach, delve into field studies or case research, which is a relatively low representation, considering that it is a highly prevalent issue in real-world business environments. When examining inventory management issues in the SC context, researchers primarily focus on factors such as profit, costs, and service levels [46].

However, this literature review demonstrates that within the domain of GSCM, in addition to the conventional objective of minimizing total costs, there is a growing emphasis on simultaneously reducing adverse environmental impacts alongside economic benefits. This shift in focus is also evident in the optimization criteria in the analyzed papers, as specified in Table 1.

5. Concluding Remarks on Findings and Directions for Future Research

An extensive survey of the relevant literature on green inventory management with a periodic review inventory system was conducted to gain structured knowledge on its operational, economic, and environmental aspects. The literature was reviewed and analyzed by the application context and key classification criteria (inventory management system, market demand type, response to excess demand, environmental impact, cost structure, modeling approach, and optimization criteria) to identify trends and study gaps in the research field. In addition to these contributions, this section will highlight the main findings of the research and suggest some directions for future studies.

The focus of green inventory management is traditionally measured in terms of costs but is complemented with an environmental perspective. Therefore, a balance between the economic benefits of inventory management and its environmental impact needs to be identified and considered. This approach tends to minimize the environmental impact of the supply chain’s activities by reducing greenhouse gas emissions, minimizing waste, and conserving natural resources, simultaneously adding value to the organization and enhancing the efficiency of supply chains, operational adjustments, and efficient inventory control to present ways to mitigate harmful emissions without significant investments in new technologies.

The first noteworthy conclusion from this research is that most existing research on green inventory management predominantly relies on simple inventory models, specifically the EOQ model and the Newsvendor model, which cannot accurately simulate real-world conditions. Moreover, periodic review inventory systems are still under-researched, with no straightforward algorithms or formulas available to determine the optimal values of key variables in real-world SC operations. This is particularly visible in the fact that only 1.3% of the papers analyzed operational and managerial decisions in GSCM, along with periodic review inventory systems, environmental impact, and the SC’s costs. In addition, only 3 out of 16 papers analyzed the (R, s, S) model of the periodic review inventory system in a GSC environment (see Figure 6). Furthermore, although relevant for practical applications, and in particular highly competitive business sectors, there is a lack of studies that incorporate the lost sales as a response to excess demand. Future research studies should include more green inventory management models using inventory policies under stochastic market demand and a lost sales environment, as these better simulate real-world conditions.

Addressing the abovementioned research gaps would benefit not only scholars in the field but also managers dealing with the complexities of modern supply chains. The decision-making process becomes increasingly challenging when conflicting real-world goals and constraints, such as service- or cost-related objectives, and limited resources come into play. Most traditional inventory formulas fail to account for these complexities. Moreover, subjective decision-making leads without using numerical experiments, and adequate statistical analysis leads to increased costs and risks, together with profit reduction. Since inventory management efficiency is associated with the SC’s environmental performance, this aspect shows promising potential for improvement. We recognize the need to determine and quantify the relations between the key parameters of the periodic review inventory systems, and the (R, s, S) inventory model in particular, and the environmental performance indicators measured through GHG emissions within the modern SC.

Finally, this literature review confirmed the lack of studies using standardized methods to calculate emissions related to inventory management operations, particularly transport activities. Knowing precise emissions (and cost data) is necessary to implement any green inventory management technique effectively, but it is also a necessary prerequisite for accurate reporting that legislation mandates. Therefore, it is crucial to standardize the measurement and calculation of emissions across various industries, companies, and activities such as production, transportation, and warehousing to enable this process.

These conclusions are consistent within the larger context. Integrating environmentally friendly and resource-efficient processes, a fundamental principle of modern SCM and Industry 5.0, aligns with the European Union’s emission reduction objectives and its transition toward a more sustainable and low-carbon economy. The EU has committed to achieving climate and energy targets under the Paris Agreement, intending to reduce net GHG emissions by at least 55% by 2030 compared to 1990 levels. These ambitious objectives are set across diverse sectors, including industrial processes, as components of the EU’s comprehensive environmental policies, which demand a systematic and integrated approach. This research aims to contribute to this goal.