1 Introduction

In recent years, immense attention has been paid to the concepts of sustainable manufacturing, including manufacturing industries, governments, societies, and researchers, among other topics of study. Rapid market changes and competition among manufacturing industries worldwide have driven toward sustainable manufacturing practices to remain stable. Sustainable manufacturing covers all manufacturing operations of a company, the end-users, and all in-between stages (Malek & Desai, 2020) with the focus of achieving sustainable development. The manufacturing sector is one of the world’s highest contributors of carbon emissions and climate change (Liu et al., 2022a, 2022b; Ruberti, 2023; Mahato & Mahata, 2022). Hence, stakeholders have started to pressure manufacturers to shift towards sustainable manufacturing (Huang & Chen, 2022; Ullah et al., 2022), by which sustainable manufacturing can strive for a timely achievement of zero carbon and sustainable development goals (SDGs) targets.

Despite these advantages, industries face challenges in implementing sustainable manufacturing in their industrial operations; those challenges are especially difficult in developing and underdeveloped nations. Several studies (Bhandari et al., 2019; Malek & Desai, 2019, 2021; Tanco et al., 2021; Verma et al., 2022) have sought to explore both the impediments and the success factors for the implementation of sustainable manufacturing in different applications through a variety of geographical contexts. Most manufacturing industries worldwide confront these issues associated with various challenges (Frank et al., 2019). Hence, it is necessary to determine challenges and their role in developing a policy for the adoption of the sustainability manufacturing (Kamble et al., 2018). It is also necessary to identify the influential challenges among common challenges of sustainable manufacturing for its effective implementation. With the support of these discussions, this study presents two initial research questions (RQs):

RQ1: What are the major challenges to implementing sustainable manufacturing in the manufacturing industries?

RQ2: How are these challenges ranked in sustainable manufacturing in the manufacturing industries?

Today, manufacturing industries utilize modern strategies and technologies to improve their processes in the face of serious challenges associated with the implementation of sustainability, especially economic challenges. Recently, manufacturing industries faced great difficulties from the COVID pandemic (Banik et al., 2023; Mezgebe et al., 2023), and in such situations, many industries used technologies to respond to these adverse impacts. Meanwhile, the development and application of Industry 4.0 technologies has been seriously overlooked even by the manufacturing industries(Govindan & Arampatzis, 2023). Modern technologies have created numerous opportunities available for sustainable manufacturing (Calignano & Mercurio, 2023; Ching et al., 2021; Paraschos et al., 2023), but few manufacturing industries have adopted these benefits.

One technology that could play a main role in future manufacturing is smart manufacturing, an emerging technology described as a “coordinated, performance-oriented enterprise, minimizing energy and material usage while maximizing environmental sustainability, health and safety, and economic competitiveness.” An important reason for the fast development of smart /4.0 technologies in processes and operations sustainable manufacturing is their ability to obtain solutions for complex sustainable manufacturing problems at a time when agility, speed, and clarity are of the highest importance (Machado et al., 2020) and alternatively companies can also try circular economy or circular manufacturing (Butt et al., 2023; Govindan, 2022; Govindan et al., 2023; Mahdiraji et al., 2023; Sharma et al., 2023). Several studies (see, for example, Huang, 2022; Psarommatis et al., 2022; Ng et al., 2022) claimed that the smart manufacturing and 4.0 technologies have had positive effects on the manufacturing operations.

The smart manufacturing notion is built upon the advantages of novel technological revolutions like vertical integration, automation, flexibility, energy management, traceability, virtualization,network strategies, and others (Abubakr et al., 2020; Cioffi et al., 2020; Janahi et al., 2022). Accordingly, smart manufacturing is a structured methodology that can be utilized to increase efficiency, improve the production process, and diminish emissions. Many studies (see, for example, Li et al., 2022; Guo et al., 2022; Cao et al., 2023) supported that the goal of smart manufacturing is to achieve zero emission, increase profits of manufacturing factories, and neutralize the danger of accidents in the processes production. Several studies (Ardanza et al., 2019; Krugh & Mears, 2018; de Assis Dornelles et al., 2022; Pozzi et al., 2023) indicated that Industry 4.0 technologies in manufacturing improves trackability, flexibility, and reliability in a system. Integrating smart manufacturing and Industry 4.0 technologies do permanently alter the manufacturing processes for better sustainable management (Olsen & Tomlin, 2020; Li et al., 2022; Su et al., 2023). Despite these advantages, there is no proper study to understand the technologies to eradicate the existing challenges of sustainable manufacturing. Furstenau et al. (2020) argued that there is a notable gap in the sustainable manufacturing literature connected to investigating the solutions and challenges by 4.0 technologies offered in the sustainable manufacturing field to develop a more sustainable production. Based on these discussions, this study proposes the third research question (RQ3) as:

RQ3: How can the smart manufacturing and Industry 4.0 technologies help to mitigate challenges in the manufacturing industries?

To address the above-mentioned research questions, this study has collected the common challenges of sustainable manufacturing from literature review and validated the same through experts’ opinions. Further, the validated challenges have been evaluated based on multi-criteria decision making (MCDM) analysis tools, WASPAS and Best-Worst method (BWM). According to Rezaei (2015) and Chakraborty, (2014), BMW is used to identify key challenges and calculate their weights in order to rank optimal challenges, and WASPAS is an effective tool to rank the alternatives. The input data for both MCDM tools was generated through Iranian case industries. Further based on the ranking of the challenges, respective technologies have been introduced and paired to mitigate those influential challenges of sustainable manufacturing.

This study effort can be seen as a guideline for the implementation of SM processes to mitigate challenges facing sustainable manufacturing by the adoption of smart manufacturing/Industry 4.0. Accordingly, it is recommended the manufacturing industries engage in smart manufacturing practices. This helps industries maximize synergy between sustainable manufacturing and digitalization for greater profit and economic development. The outcomes could be helpful for policymakers, researchers, and manufacturing industries. Furthermore, Industry 4.0 is capable of remote control, using smart technologies that are necessary during global turbulence such as caused by the COVID-19 pandemic. This revolution accelerates the digital transformation and promotes transportation management, production management, and public safety.

The rest of this study is structured as follows. Section 2 describes existing studies in the field of sustainable manufacturing and the application of smart technologies in sustainable manufacturing. Section 3 details the considered MCDM methods used in the article (BWM and WASPAS). Section 4 presents a case study, which includes different subsections, each determined based on the data gathering purpose, data analysis, and techniques. With the findings of the study, Sect. 5 seeks to answer the considered research questions in addition to developing a recommended framework. Managerial implications and policy recommendations of the study are highlighted in Sect. 6. Section 7 provides a summary of results, limitations, and suggestions for future research.

2 Literature review

To improve an understanding of the current status of sustainable manufacturing and smart manufacturing, this section conducts a state-of-the-art review. A detailed discussion of these existing studies, along with the respective literature gap, follows.

2.1 Sustainable manufacturing

Owing to the popularity and success of sustainable manufacturing, several studies have been published with different streams of applications. To understand the present status, this study emphasizes recent literature published in sustainable manufacturing. Even now, there are many studies that seek to explore the basics of sustainable manufacturing. For instance, Reiff et al. (2021) proposed a novel framework for process planning with the specific application of sustainable manufacturing with the case of laser machine modeling. Alayón et al. (2022) explored different barriers and enablers of sustainable manufacturing adoption with the specific focus on small and medium scale enterprises (SMEs). Yip and To (2021) made a study on barriers of sustainable manufacturing specifically related to stakeholders with the assistance of social network analysis. Bhatt et al. (2020) studied the structure of sustainable manufacturing through bibliometrics and content analysis; their study concluded that the sustainable manufacturing literature often focused on green and lean principles rather than criticality of sustainability. Bastas (2021) reviewed the latest trends and themes involved in sustainable manufacturing technologies; sustainability assessment technologies and sustainability indicators were explored. Kamble et al. (2022) explored the impacts of digital twins in sustainable manufacturing and sought to understand the current trends, implementation frameworks, and future perspectives of successful implementation.

Some studies involved in the assessment of sustainable manufacturing, for instance, Ali et al. (2021) explored the relationship between sustainable manufacturing capabilities and practices with sustainable performances. Holgado et al. (2020) studied the relationship between the maintenance function and sustainable manufacturing; their work highlighted the impacts and contributions of maintenance functions with sustainable plant operations through the consideration of economic, environment, and social benefits. Swarnakar et al. (2022) explored the indicators for sustainability assessment in manufacturing and sought to prioritize the same with an integrated approach.

In addition, this study explored the existing role of environmental regulations to improve the sustainable performances of the firm through sustainable manufacturing. Li et al. (2020) explored existing challenges and different concepts (including technologies, framework features, application effects, and resource optimization) involved in internet-based intelligent sustainable manufacturing. Malek and Desai (2022) studied the role of sustainable manufacturing implementation in the overall organization’s performances. Initially, a hypothesis was framed along with a framework among enablers of sustainable manufacturing and organizational performances. Due to such a high volume of studies on sustainable manufacturing, there are a considerable number of literature review studies. Malek and Desai (2020) reviewed the existing literature published in sustainable manufacturing theme through a systematic literature review. This review includes several functions including sustainable dimensions, data analysis techniques, and focused areas.

As discussed above, several different areas of sustainable manufacturing have been explored. Some studies (Alayón et al., 2022; Li et al., 2020) explored the challenges of sustainable manufacturing, but these studies are limited with providing in-depth practical solutions to address these challenges. To address this limitation, this study explores different ways to mitigate these sustainable manufacturing challenges.

2.2 Smart manufacturing/industry 4.0 in sustainable manufacturing

Recent developments in Industry 4.0 technologies have interested researchers with a manufacturing theme to integrate such technological developments into a sustainable manufacturing paradigm. Most existing studies investigated the relationship between sustainable manufacturing and smart manufacturing through exploring the performances, practices, challenges, and other basic phenomenon. Abubakr et al. (2020) explored the relationship between sustainable manufacturing and smart manufacturing by considering sustainable manufacturing performance measures with Industry 4.0 technologies. Aggarwal et al. (2022) highlighted the relationship between smart and sustainable manufacturing practices with a hypothesis developed to understand the link between the manufacturing competitiveness and top management commitment to those practices. Gholami et al. (2021) made a bibliometric study on sustainable manufacturing 4.0 in which practices and respective pathways were discussed; these pathways are believed to assist managers in shifting towards sustainable manufacturing 5.0 or Industry 5.0.

Several studies applied Industry 4.0 technologies to promote sustainable manufacturing in their considered case operations. Watson et al. (2021) proposed a methodology by which machine learning and sensors might be unified as an intelligent sensors system that promotes sustainable food and drink manufacturing. Park et al. (2022) proposed an effective way through deep learning to detect defects that occur in manufacturing processes. This study argued that the proposed method improves sustainability in manufacturing with the case of a disposable gas lighter manufacturing process. Yun et al. (2022) addressed the sustainability challenges with the introduction of CPS-enabled demand response strategy. Their real time CPS-enabled system provides long term production schedule information to assist managers in supply demand response. Danishvar et al. (2021) discussed the scheduling problem in which multi-objective batch-based flow shop scheduling optimization was proposed; the proposed model was evaluated with a case of AI-driven sustainable manufacturing. Kumar et al. (2021) explored the potential opportunities of involving big data analytics with the application of sustainable manufacturing to understand its strategic factors for efficient operations.

Due to the recent influx of studies, some literature review studies have emerged. He and Bai (2021) made a review on a new concept of sustainable intelligent manufacturing with the consideration of digital twins; the digital twin was discussed with sustainable manufacturing perspectives. In addition, this study explored the supporting systems of sustainable intelligent manufacturing by analyzing the intelligent manufacturing equipment, systems, and services. Ching et al. (2022) made a review on different applications of Industry 4.0 technologies in sustainable manufacturing. At the end of the review, this study suggested 15 sustainability functions of sustainable manufacturing to which Industry 4.0 technologies could contribute.

So far, several studies (Abidi et al., 2022; Sharma et al., 2021a, 2021b; Cinar et al., 2020) sought to explore smart technologies in sustainable manufacturing as a tool to improve sustainable productivity. However, all these studies either focus on a single technology or they make a general argument on the challenges that accompany technologies. There is not a single in-depth study that presents all key challenges of sustainable manufacturing and appropriate individual mitigating Industry 4.0 technologies. In addition, this study is distinct from existing studies in two important areas. First, this study focuses on sustainable manufacturing challenges with the concern of the global uncertainties created by the recent COVID-19 pandemic. Secondly, the case context selected to consider the challenges and its mitigating technologies is Iran. No single study currently exists to fill these research gaps.

3 Methods

To achieve the study goals, a two-stage methodology has been adopted. The first stage includes “Best-Worst Method”, and the second stage applies WASPAS. The MCDM literature proposes different techniques for weight calculation, including AHP, dominance, MAXIMIN, and permutation technique (Sotoudeh-Anvari, 2022; Zavadskas et al., 2014). The adoption of each approach is directly related to the type of problem it pursues. However, these techniques need to be extensively reviewed by decision-makers, and their views may result in scattered judgments. Each technique has some unique characteristics. Also, it is helpful to consider the number of decisions needed to arrive at a special judgment. Hence, to address such conditions, Rezaei (2015) offered BWM to solve the above-mentioned gap. The results suggest that BWM can outperform AHP in four areas: conformity, smallest violation, whole deviation, and consistency (Rezaei, 2015).

Generally, the main characteristics of the BWM are as follows: it needs few “pairwise comparisons” and it presents more consistent outcomes. Given the advantages of BWM over AHP defined in the literature, the BWM was chosen for this article. A comparison is drawn between BWM and AHP to confirm the choice of BWM. However, it is worth noting that the purpose of this article is not to compare the outcomes of these two techniques. The BWM method ensures the consistency of the findings and permits extensive applications in different fields.

A variety of techniques to rank alternatives are available according to their attributed weights: TOPSIS, VIKOR, MOORA-G and SWARA, among others (Simanaviciene et al., 2012; Vasegaard et al., 2022; Zarbakhshnia et al., 2020). For this purpose, WASPAS was chosen as the most suitable approach due to its solution accuracy and ranking criteria; a further criterion is that it is affected by the value of its control parameter. The goal of this paper is to overcome the challenges of sustainable manufacturing implementation during COVID-19 pandemic. Clearly, it is difficult to offer solutions that can eliminate all real-world challenges. Hence, efficient approaches suggest ways to reduce the current challenges. These methods are explained in detail here and their significance and stages are discussed.

3.1 Best-worst method (BWM)

This paper uses BWM, which addresses a significant challenge associated with the case industries and their weight criteria. There are some MCDM techniques for measuring the weight of criteria. One MCDM technique, analytical hierarchy process (AHP), is frequently used to assess the weights of the criteria. Also, new research has introduced a novel technique called BWM as a substitute to AHP for determining the weight of criteria. The AHP has several restrictions that are improved by the BWM method (Rezaei, 2015). In addition, BWM has received increasing scholarly attention for the following reasons: inconsistency in comparisons and lengthy pairwise comparisons. This approach uses two vectors to specify the “weight of criteria” instead of pairwise comparison. It helps improves the reliability of findings and offers convenient analytical processes for decision making. This method has several advantages and various applications discussed by numerous studies (Govindan et al., 2022a, 2022b; Haqbin et al., 2022; Rezaei, 2016). In light of its importance, BMW has been adopted in this paper to determine and rank the main challenges of sustainable manufacturing along with their weights by adhering to the following steps (Rezaei, 2015).

Stage 1 Establish the decision criteria.

This step includes “a set of criteria \(\left\{{c}_{1}, {c}_{2},\dots \dots \dots {c}_{n}\right\}\) for ‘n’ number of attributes that are assessed in this study. However, in some cases, other options (alternatives) are considered.

Stage 2 Identify and determine the worst (least desirable—W) and the best (most desirable—B) criteria. This step also includes the decision-makers’ response but with no comparison or rank.

Stage 3 Determine preferences among criteria. In this step, a scale of 1 to 9 is used to identify the best criterion. It is calculated and shows “the resulting vector from the best to other vectors.”

$${A}_{B} =\left\{{a}_{B1}, {a}_{B2},\dots \dots {a}_{Bj}\dots {a}_{Bn}\right\}$$

where \({a}_{Bj}\) indicates the priority of the best criterion B over criterion j. Therefore, it is evident that.

$${a}_{BB}=1$$

Stage 4 Specify preferences among criteria. In this step, a scale of 1 to 9 is used to identify the worst the criterion for others. It is calculated and displays “the resulting vector from the others to worst.”

$${A}_{w}={\left({a}_{1w},{a}_{2w},....,{a}_{nw}\right)}^{T}$$

where \({a}_{jw}\) indicates the priority of the criterion j over the worst factor W. Therefore, it is evident that \({a}_{WW}=1\).

Stage 5 Compute the “optimal weights.”

This stage calculates the weights of study attributes, indicating that the highest absolute differences for all js are reduced of the next set of \(\left\{\left|{w}_{B}-\left.{{a}_{Bj}w}_{j}\right|,\left|{w}_{j}-{a}_{jw}\left.{w}_{W}\right|\right.\right.\right\}\). Also, the min and max pattern can be formulated as

$$\mathit{min} \underset{j}{\mathrm{max}}\left\{\left|{w}_{B}-\left.{{a}_{Bj}w}_{j}\right|,\left|{w}_{j}-{a}_{jw}\left.{w}_{W}\right|\right.\right.\right\}$$

Subject to

$${\sum }_{j}{w}_{j}=1$$
(1)

\({w}_{j}\ge 0\) for all j.

By transformation through “a linear programming problem,” this above model can be solved.

Min ξ.

Subject to

$$\left|{w}_{B}\right.-{a}_{Bj}\left.{w}_{j}\right|\le {\upxi }\text{ , for all j}$$
$$\left|{w}_{j}\right.-{a}_{jw}\left.{w}_{W}\right|\le {\upxi }\text{ , for all j}$$
$${\sum }_{j}{w}_{j}=1$$
$${w}_{j}\ge 0, \mathrm{for}\text{ all j.}$$
(2)

This step finds the optimal weights of the assessed criteria using the above two equations.

3.2 WASPAS method

This MCDM technique combines the weighted sum-product pattern (WPM) and weighted sum model (WSM) to make decisions (Zavadskas et al., 2012), and it prioritizes alternatives by combining optimal criteria and calculating them based on the outcomes of these two models. WASPAS includes expert responses to obtain the weight of criteria and to evaluate the consistency of alternative rankings. It is often suggested as the most suitable MCDM technique by many researchers (Chakraborty et al., 2014; Al-Barakati et al., 2022; Pamucar et al., 2022). Several studies have applied this method successfully to different areas ranging from engineering, management, supply chain, and more. Zavadskas et al. (2012) introduced the following stages to WASPAS method.

Stage 1 For benefit criteria,

$$ \tilde{x}_{ij} = \frac{{x_{ij} }}{{\max_{i} {\text{ x}}_{ij} }} ;i{\text{ = 1,...,m, j = 1,}}...{\text{,n}} $$
(3)

Stage 2 For non-benefits criteria,

$$ \tilde{x}_{{ij}} = \frac{{\min _{i} x_{{ij}} }}{{x_{{ij}} }};i{\text{ = 1}}, \ldots ,{\text{m}},{\text{j = 1}},...{\text{,n}} $$
(4)

where \({\widetilde{x}}_{ij}\) shows the value of normalized \({x}_{ij}\).

Accordingly, the two measures of optimal criteria in WASPAS are as follows.

Stage 3 Compute “the total relative importance” of the WSM option from the following equation:

$${Q}_{i}^{(1)}={\sum }_{j=1}^{m}{\widetilde{x}}_{ij}{w}_{j}$$
(5)

where \({w}_{j}\) indicates the weight (relative significance) and importance (weight) of \({j}^{th}\) measures.

Stage 4 Calculate the “total relative importance” of \({j}^{th}\) option for WPM using the following equation:

$${Q}_{i}^{(2)}={\prod }_{j=1}^{m}({\widetilde{x}}_{ij}{)}^{{w}_{j}}$$
(6)

Then, a joint total “criterion of weighted aggregation of additive and multiplicative methods” has been provided (Zavadskas et al., 2012):

$$ Q_{i} = 0.5Q_{i}^{\left( 1 \right)} + 0.5Q_{i}^{\left( 2 \right)} = 0.5 \left( {\mathop \sum \limits_{j = 1}^{m} \tilde{x}_{ij} w_{j} + \mathop \prod \limits_{j = 1}^{m} (\tilde{x}_{ij} )^{{w_{j} }} } \right) $$
(7)

Stage 5 Determine the total relative impotence of i th option in this Equivalent:

$$ Q_{i} = \lambda Q_{i}^{\left( 1 \right)} + \left( {1 - \lambda } \right)Q_{i}^{\left( 2 \right)} = \lambda \mathop \sum \limits_{j = 1}^{m} \tilde{x}_{ij} w_{j} + \left( {1 - \lambda } \right)\mathop \prod \limits_{j = 1}^{m} (\tilde{x}_{ij} )^{{w_{j} }} $$
(8)

\(\left( {\lambda = 0,~0.1,.....,1.0} \right)\)

The options are rated in terms of Qi values. This method is converted into two parts including WPM (λ = 0) and WSM (λ = 1).

4 Case illustration

There is a paucity of literature that identifies challenges of sustainable manufacturing and offers solutions using the “smart manufacturing/Industry 4.0” technologies. In addition, recent research has mainly concentrated on the supply chain and sustainability. Hence, this article surveys the challenges of sustainable manufacturing in manufacturing industries mounted by several global uncertainties, especially with detergents, food, mineral powders, and office machinery industries in Iranian context. We also consider challenges brought on by the COVID-19 pandemic. For this research, a particular search pattern has been selected to identify the challenges of sustainable manufacturing. The overall flow of the study is described in Fig. 1. This pattern includes data gathering strategies, the choice of case studies, and the distribution of questionnaires. The sustainable manufacturing has attracted slight attention recently, but it has not been examined in manufacturing sections. Thus, it calls for deep analysis of the notion. In this paper, the case study method has been used to shed light on the issue.

Fig. 1
figure 1

The overall flow of the current study

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4.1 Data collection

In the first section, the theoretical basis is presented. The problem is associated with the challenges of sustainable manufacturing. Also, data are collected from secondary and primary sources to determine the current challenges. The information was collected from the following sources:

  • From August 2020 to January 2021, the questionnaires were distributed among experts and managers of Iran's manufacturing industries. Initially the questionnaires were emailed to 70 experts and managers. They filled out a survey questionnaires and ranked 20 challenges in different manufacturing industries. The results are based on the discussion of experts and authors, and review of literature. The questionnaires address key challenges concerning the sustainable manufacturing. According to the questionnaires, 20 key challenges were gathered and applied in this study.

  • Secondly, we conducted a systematic literature review in Scopus and Web of Science databases to identify SM challenges. This search keywords included “sustainable”, “manufacturing”, “sustainable manufacturing in Iran”, “challenges”, “Sustainable”, “Industry 4.0”, “smart manufacturing”, and “manufacturing industries”. Moreover, the secondary data was collected by searching the literature, including theses, proposals, media, and key papers. The papers publicly accessible in the domain of manufacturing industries were the main source of data collection. At this step, we compared data about the identification of sustainable manufacturing challenges during one of the recent global uncertainty COVID-19 pandemic.

  • Also, experts were presented with a list of 20 challenges gathered from the relevant papers, literature, and ideas of specialists in this field. Table 1 shows key challenges of sustainable manufacturing identified in the study. Some challenges were determined with the assistance of specialists. This study suggests solutions by relating the “smart manufacturing/Industry 4.0 technologies” to existing challenges to mitigate them in Iranian manufacturing industries.

Table 1 Sustainable manufacturing challenges identified from existing studies and experts’
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4.2 Case analysis

Sustainable manufacturing in production organizations can be a novel avenue of research. Recently, manufacturing companies have attempted to integrate several strategies to gain advantages in environmental, economic, and social dimensions. From different manufacturing industries in this study, we selected relevant industries as case studies for in-depth analysis of this field. Also, this article aims to investigate the methodology used in these industries to examine the attitude of manufacturing industries concerning SM challenges during a global uncertainty like the COVID-19 pandemic in Iranian SMEs. At first, we explored total activities, operations, and all sections in manufacturing processes. Then, we collected information about the challenges of sustainable manufacturing. The manufacturing industries are selected from the pool based on their interests and their accessibility. Five industries were selected under each production sector, and one industry from each sector was selected for the study. To contact the manufacturing industries selected, we arranged Skype meetings for discussion with experts and then emailed questioners to subjects. These experts had in-depth insights into the challenges of sustainable manufacturing and could assist practitioners. Moreover, the specifications of selected industries are shared in Table 2.

Table 2 Profile of selected experts and SMEs industries in Iran
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4.3 Methods application

This article analyzes key challenges to sustainable manufacturing through two methods: BWM and WASPAS. The set of data are provided based on the case studies conducted by distributing questionnaires among manufacturing industry’s experts and managers. For this purpose, we prepared two different sets of questionnaires related to these methods. The details of each method and their application are described below.

4.3.1 “Best-worst method” (BWM)

Given the importance of BMW, as described earlier, this article utilizes the MCDM method to assess key challenges. The first step was data collection in which 20 challenges were determined. This paper also evaluates challenges facing the selected industries. In addition, to identify some key challenges of this method, we classified the worst and best challenges of sustainable manufacturing. The intermediate steps involved in BWM are listed in Tables 3, 4, and 5. Then, the relevant criteria are displayed based on their global weights in Table 6. The final set of key challenges are indicated from the perspective of manufacturing industries in Table 2. The ranking of each criteria dimension has been highlighted in Fig. 2.

Table 3 Best-to-Others vector of main-criteria
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Table 4 Others-to-Worst vector of main-criteria”
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Table 5 Major criteria weights
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Table 6 Category and sub-categories Best-Worst weights
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Fig. 2
figure 2

Ranking the main criteria of SM

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Also, the other outputs of Best-to-Others vector and Others-to-Worst vector sub-criteria are not presented to avoid filling the paper with several outputs tables. As noted above, only the main criteria of SM are included. The final ratings of the main and sub-challenges are displayed in Table 6 and the rankings of sub-criteria of SM is shown in Fig. 3.

4.3.2 Decision making and WASPAS

The final set of challenges was determined by the BWM method. Then, challenges were analyzed using the WASPAS technique. The usage of this method are described as follows:

  1. (a)

    Construct the decision matrix as shown in Table 7.

  2. (b)

    Construct the normalized decision making matrix using Eqs. (3) and (4).

  3. (c)

    Compute the “ total relative importance” of the options by Q (1) and Q (2), which are obtained from Eqs. (5) and (6).

  4. (d)

    Compute the WASPAS value by aggregating Q (1) and Q (2) from Equation (7).

  5. (e)

    Calculate and rate the options according to “decreasing values” from Equation (8), as displayed in Table 8.

Table 7 The decision matrix
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Table 8 Rank alternatives
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Based on the WASPAS, the ranking for the considered industry alternatives has been highlighted in Table 8 and Fig. 4. From Table 8 and Fig. 4, it can be easily evident that the food industry faces more challenges for the implementation of sustainable manufacturing. However, Tables 2 and 9 depict SM challenges and the results of their analysis. Then, these challenges are briefly explained and linked to new technologies, as shown in Table 9.

Table 9 Related to the challenges of SM to the new technologies
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5 Discussion and recommendation framework

As stated earlier, the main purpose of this article was to present the existing SM condition in Iran and discuss important challenges to its implementation in the manufacturing industries. This work offers an extensive discussion of findings because it provides profound insights into the challenges of sustainable manufacturing during the disruptive global uncertainty caused by the COVID-19 pandemic in the Iranian context. MCDM techniques have been extensively utilized for sustainable manufacturing. In this study, two new MCDM techniques, hybrid BWM and WASPAS techniques, have been applied to identify the challenges of sustainable manufacturing. The upcoming discussions are based more on COVID pandemic related sustainable manufacturing challenges, by which the industries in future can be resilient with other global uncertainties.

This paper addresses SM problems and challenges, which have received considerable attention so far, along with challenges of implementing SM processes in Iranian manufacturing industries. Based on the content analysis of reviewed articles, this article identifies key challenges of SM. In our analysis, we explored organizational, governmental, and technological aspects of this issue. Hence, we can now answer the research questions raised in the introduction of this paper.

Q1. What are the major challenges to implementing SM in the manufacturing industries?

To answer the first research question, we have listed challenges in Table 2. The review of literature suggested that uncertainty, and sudden changes during the COVID-19 pandemic in manufacturing industries, generated different challenges to the implementation of sustainable manufacturing. As such, 20 challenges were identified in the literature review. The authors argue that governments and policies have played an important role in the reduction of SM challenges, especially during COVID-19 outbreak. The key challenges were identified during the review, some of which are presented here. The challenges listed in Table 2 can also be divided into three categories (organizational, governmental, and technical aspects). From an environmental standpoint, lack of governmental support in the form of regulations and policies (GC14) and absence of subsidies and incentives (GC16) are key challenges highlighted by authors (Malek & Desai, 2019). The most widely cited challenges from technological perspective included lack of technology (ICT, IOT) to implement SM in manufacturing industries (TC17) and failure to project potential resources flows (TC20). These challenges were mentioned by Ngu et al. (2020). Furthermore, weak industrial infrastructures (OC11) and uncertain return on investment (sanctions/inflation) (OC10) are as other major challenges to SM implementation from an organizational perspective (see Pathak and Singh, 2019; Malek & Desai, 2019).

Q2. How can the smart manufacturing and Industry 4.0 technologies help to mitigate the challenges in the manufacturing industries?

The implementation of sustainable manufacturing processes is difficult and complex due to different organizational, economic, and governmental rules/standards adopted by each country. The SM processes are still improving. Some researchers have shown that the application of smart manufacturing and 4.0 Industry technologies can reduce challenges to SM. They also assist manufacturing industries to expand their economy.

Moreover, smart manufacturing and Industry 4.0 technologies enhance manufacturing competitiveness by minimizing time/costs and raising market shares in the face of challenges. Table 9 lists modern technologies utilized to overcome SM challenges. These technologies can also be used to overcome a variety of challenges noted by the authors. It then identifies important SM challenges that call for the application of powerful approaches such as smart manufacturing and “Industry 4.0 technologies” to mitigate these kinds of challenges in the industries (Huang, 2022). Given the importance of this issue, this article adopts these technologies to mitigate sustainable manufacturing problems related to the approved cases of COVID-19. Smart manufacturing offers multiple advantages associated with new technological advances such as vertical integration, automation, flexibility, energy management, traceability, virtualization, etc. (Abubakr et al., 2020; Cioffi et al., 2020). In general, integrating these technologies and sustainable manufacturing challenges can provide a broad range of beneficial practices and solutions to different manufacturing industries. These technologies include “cyber-physical system, big data, cloud computing, internet of things” and more (Krugh & Mears, 2018). They can present innovative techniques to alleviate SM challenges. These technologies have remote control ability, utilizing smart technologies that are well-suited for COVID- 19 pandemic. Table 9 presents a list of these technologies.

Q3. How are these challenges ranked in sustainable manufacturing in the manufacturing industries?

To answer this research question, this paper explains these methods, their significance, and stages in three sections. The MCDM literature has proposed different techniques for the calculation of weights such as AHP, Dominance, Maxi Min method, and Permutation technique, to mention a few (Yadav et al., 2017). The adoption of each approach depends on the type of the problem. However, these techniques need to be reviewed by a number of decision-makers, which may result in scattered judgments. Each technique has its unique characteristic. Also, it is necessary to consider the number of decisions required to reach a certain judgment. Thus, Rezaei (2015) offered BWM to solve the above-mentioned gap. According to their findings, BWM outperforms AHP in four dimensions of conformity, weak violation, whole deviation, and consistency (Rezaei, 2015). Generally, the main characteristics of the BWM are the need for few “pairwise comparisons” and consistent outcomes. Given the obvious advantages of BWM over AHP discussed in the literature, we have chosen BWM in this article. A comparison is drawn between BWM and AHP to confirm the choice of BWM. However, the purpose of this article is not to compare the outcomes of these two techniques. It is because the BWM method ensures consistency in the findings and has extensive application in different fields. Also, there are different techniques to rank options (alternatives) according to the attributed weights such as TOPSIS, VIKOR, SWARA, or others (Simanaviciene et al., 2012). Therefore, WASPAS was selected as the most suitable approach in this paper due to its specific utilization, solution accuracy, and ranking criteria, which are affected through the value of its control parameters. This paper employs effective approaches to rank the SM challenges and alternatives. Moreover, these methods are explained in detail along with their significance and stages.

In the following, the weights of challenges are measured using BWM. Initially, the results of analysis are presented to be evaluated for validity. The outcomes are consistent with the existing literature and feedback from experts. In the last step, the implications of findings are discussed. Table 6 displays and ranks challenges using BWM. The rankings are based on the weight of each item. As depicted in Table 6, ‘lack of support from the government by enacting regulations and policies’ (C14) is the main challenge and ‘the absence of subsidies and incentives’ (C16) is the least obstacle to the sustainable manufacturing processes. The third important challenge is ‘the lack of proper technology (ICT, IOT) to implement SM in manufacturing industries’ (C17). The cause group consists of the following barriers: C14 > C16 > C17 > C20 > C11 > C18 > C19 > C10 > C12 > C15 > C9 > C8 > C2 > C13 > C1 > C3 > C6 > C4 > C5 > C7.

After measuring the weights of challenges, the ranking of options was obtained using the WASPAS method. This is an effective MCDM technique, which as discussed in Section 2–2, is used to rank alternatives. Moreover, this method combines the weighted product model and the sum model. The different phase’s methodology applied shows SM challenges in production industries. The resulting ranking of alternatives was obtained: A2 > A3 > A4 > A1. The relevant solutions to SM challenges are presented in Table 9.

In general, integrating these technologies and sustainable manufacturing can provide a wide range of valuable practices and solutions to different manufacturing industries. It includes technologies to gather, store, analyze, transfer, and correctly control and monitor information systems during the manufacturing processes. These technologies provide innovative techniques to mitigate the manufacturing challenges. These technologies are capable of remote control, using smart technologies that are suitable during COVID-19 pandemic. This revolution accelerates the digital transformations.

The framework for overall mitigation of sustainable manufacturing challenges, utilizing the above three dimensions, permits manufacturing industries in different fields to overcome important challenges to the SM such as poor IT infrastructures, delayed supply of materials and essentials, lack of trust between workforces, weak organizational culture to implement sustainable manufacturing practices, and so forth. However, each of the aforementioned dimensions is only obtained by specified applications, and that result warrants further research.

Fig. 3
figure 3

Ranking the sub-criteria of SM

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Fig. 4
figure 4

Ranking SM alternatives

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The general framework derived from the above studies and their suggestions is provided in Fig. 5. The adoption of the proposed framework can help mitigate the sustainable manufacturing challenges by these technologies, which further improves these challenges. These technologies help remove traditional strategies, prolong the beneficial life of the manufacturing equipment, reduce the unplanned downtime them, improve the production processes, decrease the consumption of resources and energy, and reduce costs. Also, these technologies exert a huge impact on the implementation of manufacturing industries’ practices from diverse organizational, governmental, and technological aspects that have been investigated in this article.

Fig. 5
figure 5

The framework for mitigation of sustainable manufacturing challenges

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6 Implications and recommendations

6.1 Implications for researchers and practitioners

There is scant research on sustainable manufacturing from these aspects, but this trend is changing. Many studies concentrate on a few aspects of the subjects related to SM. This article explores major practical and theoretical contributions to this field. The implications of this study for practitioners and scholars are presented below.

  • Based on the above recommendations and discussions, a general framework was developed (as shown in Fig. 5). This introduced framework can help diminish SM challenges from a manufacturing point of view and achieve the sustainable manufacturing goals. The presented objectives exhibit that the implementation of sustainable manufacturing has a huge effect on practices manufacturing industries by targeting organizational, governmental, and technical aspects and utilizing these technologies to mitigate challenges.

  • These new technologies can offer new digitalization strategies from a manufacturing perspective. In general, the integration of Industry 4.0 technologies and smart manufacturing technologies can present a wide range of advantageous activities such as cloud manufacturing, digitalization, and so forth which can help improve practices related to the manufacturing industries. This study provides an exhaustive set of solutions presented by different scholars to mitigate SM challenges.

  • The continuous alteration of manufacturing needs has provoked managers and industries to identify main challenges to the efficient implementation of SM. This article offers an extensive list of challenges related to sustainable manufacturing during COVID-19 epidemic through various a review of research on this subject.

  • It is difficult for manufacturing industries to implement all solutions practically. Therefore, in the present article, we have made a connection between solutions and challenges, which will help industries concentrate on high-intensity solutions and, finally, on the execution of other solutions.

  • Managers in manufacturing industries are constantly in search of innovative ways to improve their sustainable manufacturing processes. For this purpose, the concept of “smart manufacturing and Industry 4.0” has gained significance recently. Furthermore, the literature suggests facilitators that can assist implement sustainable manufacturing processes in the industries. The list of smart manufacturing and Industry 4.0-based solutions can be helpful for researchers in this field. Also, it provides new frameworks and finally improves the acceptance of SM.

  • Most of the frameworks presented in the literature have been confirmed using the case study approach. However, the current study employs a case study approach based on the MCDM to increase the applicability of the expanded framework.

  • The results of this study provide the managers of manufacturing industries with in-depth insights into challenges and solutions related to their ranking using the hybrid BMW and WASPAS techniques.

  • The literature has proposed some frameworks for increasing the acceptance of SM. Still, in terms of framework, various studies have explored challenges of a special frame structure. However, there is a lack of framework linking SM challenges to its solutions. Thus, the framework explained in the current study can be of great value to the managers of manufacturing industries to promote SM acceptance.

6.2 Recommendations for policy makers

The acceptance of sustainable manufacturing in the manufacturing industries will mark the future of industries. These technologies have helped manufacturing industries offer sustainable products to users with based on environmental considerations, improving manufacturing processes with the digitalization of companies. Therefore, the government should promote strategies and policies favoring the acceptance of “Industry 4.0 and smart manufacturing.” The policymakers must grant subsidies to the manufacturing industries for the implementation of the manufacturing processes and activities. Such initiatives will support the interest of manufacturing industries that have embraced green technology prompting them to produce their products by reflecting on smart manufacturing and Industry 4.0. The policymakers are recommended to hold educational courses to raise awareness about this subject and educate industries and customers about how to improve the performance of manufacturing practices. The final rankings of challenges and the solutions provided in this article can be used by other manufacturing industries to expand effective actions and strategies that could help enhance their economy.

7 Conclusion

The main innovation of this article is its exploration of key challenges to SM in the manufacturing industries and its enriching the body of literature on SM challenges in the manufacturing industries in Iran. Informed by the research goals, this study carries out a comprehensive literature review to investigate the important sustainable manufacturing challenges and connect them to Industry 4.0 and smart manufacturing technologies to reduce SM challenges. A unique set of 20 SM challenges and solutions is obtained from a review of literature and ideas of specialists, and the results are analyzed by techniques such as BMW and WASPAS. The results of case analysis indicate that organizational, governmental, and technical challenges explain SM implementation failures especially with the focus of a major global uncertainty of COVID-19. In this study, 20 common challenges were reviewed by BWM, among which “the lack of support from the government by enacting regulations and policies” (C14) and the absence of a “review of human resources” plans/policies for social sustainability” (C7) posed the greatest and least challenges to the sustainable manufacturing, respectively. After measuring the weights of challenges, the options were ranked. Further these challenges were mapped with their respective mitigating Industry 4.0 challenges; for instance, the Internet of things technologies provide innovative opinions (knowledge sharing/information exchange) for presenting new services to improve sustainable manufacturing operations. Finally, based on these findings and its relevant discussions, a recommended framework has been proposed. Despite of having several contributions, this study presents a set of SM challenges and solutions by investigating the literature and the view of specialists. Thus, it is recommended that researchers undertake a large-scale review to enhance the list drafted in this article. It is further necessary to use structural modeling technique, analytical network process, and assessment laboratory to describe structural relations between agents. The expanded framework can be expanded to other sub-categories of sustainable manufacturing to improve its applicability. The framework generated in this investigation is examined for its applicability across Iranian industries. From the authors’ viewpoint, if this framework is used for developing economically, it won't be wrong. But it needs to modify applicability before actual implementation. The challenges and solutions introduced in the present article are particularly related to the promotion of environmental and economic aspects. Still, manufacturing industries are recommended to seek advice/information from specialists in developed countries to determine the country-specific challenges and investigate the possible solutions.