An Integrated method for manufacturing Sustainability assessment in tire industry: a case study in Indonesian

ABSTRACT

This study presents a novel approach for evaluating the performance of sustainable manufacturing by integrating the Triple Bottom Line (TBL) concept. The research incorporates various novel metrics, including machine performance, physical load, and mental load. Additionally, the evaluation of sustainable manufacturing takes into account the workload assigned to each workstation in the production process. This research utilizes the Delphi approach, Analytical Hierarchy Process (AHP), Sustainable Value Stream Mapping (SVSM), and Traffic Light System (TLS). The Delphi method is employed to assess pertinent indicators. Meanwhile, the Analytic Hierarchy Process (AHP) is utilized to evaluate the importance of the TBL and workstation indicators. The SVSM and TLS techniques were employed to chart the TBL indicators. The Manufacturing Sustainability Index (MSI) scores were computed using indicators, workstation weights, and indicator efficiency. Additionally, a case study is provided on the tire sector in Indonesia. The tire industry's MSI findings showed a performance score of 85.52%, signifying the need for improvement in performance. The findings of this study have research implications as they contribute to the advancement of theory in evaluating sustainable manufacturing performance. Furthermore, this study offers managerial recommendations for the tire business to enhance sustainable production performance.

KEYWORDS:

• Sustainable manufacturing
• Lean manufacturing
• Analytical hierarchy process
• performance assessment
• Value Stream Mapping

1. Introduction

The manufacturing industry has long focused on improving economic performance without considering the negative environmental and social impacts (Azapagic, Millington, and Collett 2006; Santoso et al. 2023). However, awareness of the importance of sustainability in manufacturing activities is increasing (Qureshi et al. 2022; Kautzar, Pambudi Tama, and Sumantri 2019). Sustainable manufacturing involves efforts in the production process to optimise company performance while not neglecting environmental factors (such as material consumption, energy, and waste) and social factors (such as workers and communities) (Rizal 2018). One concept that is widely applied to improve the performance of sustainable manufacturing is lean manufacturing involves the Triple Bottom Line (TBL)(Qureshi, Mewada, Alghamdi, Almakayeel, Qureshi et al. 2022, 2022). The Triple Bottom Line (TBL), which includes economic, social, and environmental aspects, is a popular concept of the three pillars of sustainability. This TBL concept is proven to be applied by various industries to improve sustainable manufacturing performance (Thomas et al. 2017). A sustainable manufacturing assessment is an assessment of production activities that aims to reduce negative impacts on environmental and social aspects while improving economic performance on the production line (Jayal et al. 2010). Sustainable manufacturing assessment in the industry plays an essential role in identifying production process areas that need to be improved in the production line. In addition, sustainable manufacturing performance assessment can ensure that companies achieve sustainability goals (Mubin, Marsetiya Utama, and Chandra Nusantara 2022). Therefore, many studies have proposed new procedures for sustainable manufacturing performance assessment that are expected to assess sustainable manufacturing performance to achieve holistic manufacturing sustainability.

In assessing sustainable manufacturing, various studies have been presented involving multiple indicators. However, in previous studies, some shortcomings need to be considered, such as the lack of research involving workload and equipment efficiency factors and the weighting of each workstation in performance assessment (Swarnakar et al. 2021; Utama, Ardiyanti, and Apritha Putri 2022). Previous research focused more on economic, environmental, and social aspects without considering workstation workload, workload, and equipment efficiency (Brown, Amundson, and Badurdeen 2014; Hartini, Ciptomulyono, and Anityasari 2019, 2020; Helleno, de Moraes, and Tadeu Simon 2017; Huang and Badurdeen 2018; Vinodh, Ben Ruben, and Asokan 2016b). Although some studies include workload, as investigated by Mubin et al. (2022), these studies did not involve workstation workload and equipment efficiency assessments. The involvement of workload indicators at workstations is crucial because it can provide an overview of the workload of the workers so that the performance assessment of sustainable manufacturing is more comprehensive. Therefore, to fill the research gap, this study involves workload indicators, equipment efficiency, and weighting at each workstation to assess sustainable manufacturing. Thus, this research is expected to enrich the understanding of production sustainability in the context of workload, equipment efficiency, and weighting at each workstation.

Based on the description in the previous paragraph, most previous studies ignore the workload at each workstation and the load of each workstation. In addition, research on sustainable manufacturing in the tyre industry is still relatively rare. Some studies apply to the Electronic (Brown, Amundson, and Badurdeen 2014), Cosmetic (Helleno, de Moraes, and Tadeu Simon 2017), Furniture (Hartini, Ciptomulyono, and Anityasari 2019, 2020), and Plastic Industry (Mubin, Marsetiya Utama, and Chandra Nusantara 2022). These studies cause a lack of understanding of sustainability performance in the context of the tyre industry. The tyre industry was chosen because it continues to grow due to increasing vehicle sales (Carree and Roy Thurik 2000). In addition, this industry is one of the most energy-consuming industries and produces a lot of waste that needs to be addressed to minimise environmental impacts (Shulman 2019). In addition, in the production process, this industry has ergonomic risks that can harm the company in the long run (Bahardin and Rahman 2018). Therefore, this industry needs to be addressed in depth to improve the performance of sustainable manufacturing. Previous research has not involved workload and equipment efficiency factors in sustainability performance analysis (Faulkner and Badurdeen 2014). Therefore, this research offers a new framework for assessing sustainable manufacturing performance, including weighting each workstation and considering the workload and equipment efficiency factors. In addition, the sustainability manufacturing performance evaluation is also presented in the tyre industry as an application of the proposed framework. The proposed framework for assessing sustainable manufacturing is based on an integrated procedure that includes the Delphi method, AHP, Sustainable Value Stream Mapping (SVSM), and Traffic Light System (TLS). The Delphi method was used to determine relevant indicators in the performance assessment. The AHP procedure assesses each indicator’s weight and the workstation’s weight on each indicator. Furthermore, using the principle of traffic light system, the mapping of sustainability performance based on the efficiency of indicator performance is presented in SVSM. Finally, the Manufacturing Sustainability Index (MSI) is given based on the weight of each indicator, the workstation weight, and the indicator efficiency.

This research significantly contributes to the development of science, especially in sustainable manufacturing performance assessment. The main contribution of this research is developing a new framework for sustainable manufacturing assessment, which combines the integration procedures of Delphi, AHP, SVSM, and TLS. This framework is expected to provide a comprehensive evaluation of sustainable manufacturing performance. This research’s second contribution is applying the proposed integration procedure to the tyre industry. In this context, this research provides a clear picture of the performance of the tyre industry based on the assessment using the developed framework. By applying this framework, decision-makers in the tyre industry can better evaluate their performance, identify performance indicators that need improvement, and know which workstations need improvement to achieve better sustainable manufacturing performance.

This article has been divided into 7 sections. Section 2 discusses the literature review on sustainability in sustainable manufacturing. Section 3 presents the framework used in this research. Section 4 presents the case study and research data regarding the Sustainability Manufacturing performance assessment. Section 5 presents the results and discussion of this research. Furthermore, in section 6, the implications of this research are outlined. Finally, conclusions and further research suggestions are presented in section 7.

2. Literature review

In this literature review section, a review of several previous studies that have been conducted regarding performance assessment in the context of sustainable manufacturing is performed. Table 1 shows some previous research related to manufacturing performance assessment. The review results show that most studies offer the VSM method as a tool for visualising production line performance. Some studies that use VSM procedures include Banawi and Bilec (2014), Vinodh, Ben Ruben, and Asokan (2016a), and Hartini, Ciptomulyono, and Anityasari (2019). This VSM procedure maps the production line (Dewi, Utama, and Rohman 2021). However, it should be noted that there are still few studies that specifically evaluate the score or value of sustainable manufacturing. Some studies that present performance scores include Huang and Badurdeen (2018), Djatna and Prasetyo (2019), and Mubin et al. (2022). In addition, we note that new research, such as by Hartini, Ciptomulyono, and Anityasari (2020), Swarnakar et al. (2021), and Utama et al. (2022), does not involve indicators of workload and equipment efficiency. In addition, there needs to be a study that considers the weight of each workstation in mapping and assessing sustainable manufacturing performance.

Table 1. Literature review.

Author	VSM	SMI	Dimension			Workstation Weight	Workload	Equipment efficiency	Tools	Application
			Economic	Social	Environment
Paju et al. (2010)	√	-	√	-	-	-	-	-	LCA, VSM	-
Banawi and Bilec (2014)	√	-	√	-	√	-	-	-	VSM, LCA,	Manufacturing
Brown et al. (2014)	√	-	√	√	√	-	-	-	VSM	Electronic
Faulkner and Badurdeen (2014)	√	√	-	√	√	-	-	-	Sus - VSM	Manufacturing
Vinodh et al. (2016a)	√	-	√	√	√	-	-	-	VSM and LCA	Manufacturing
Helleno et al. (2017)	√	-	√	√	√	-	-	-	VSM	Cosmetic
Huang and Badurdeen (2018)	-	√	√	√	√	-	-	-	AHP	Electronic
Garza-Reyes et al. (2018)	√	-	-	-	√	-	-	-	PDCA and VSM	Manufacturing
Purnama (2018)	√	-	√	√	√	-	-	-	VSM, Fuzzy – AHP, Valsat	Batik industry
Ahmad and Yew Wong (2019)	-	-	√	√	√	-	-	-	Delphi	Food manufacturing
Djatna and Prasetyo (2019)	√	√	√	√	√	-	-	-	SVSM, (LCA), FMEA	Manufacturing
Hartini et al. (2019)	√	-	√	√	√	-	-	-	LC-VSM	Beverage Company
Prasetyo and Adi Wicaksono	√	√	√	√	√	-	-	-	Sus - VSM	Furniture
Soltani et al. (2019)	√	-	√	√	√	-	-	-	VSM, AHP and TOPSIS	Manufacturing Process
Bait et al. (2020)	√	-	√	√	√	-	-	-	Simulation, VSM	Manufacturing
Hartini et al. (2020)	√	√	√	√	√	-	-	-	Delphi, AHP, VSM	Furniture
Zhu et al. (2020)	√	-	√	-	√	-	-	-	VSM, Kaizen	Manufacturing
Patalas-Maliszewska et al. (2022)	√	-	√	√	√	-	-	-	AHP, Fuzzy. Decision Tree	Manufacturing
Salvador et al. (2021)	√	-	√	-	√	-	-	-	VSM and LCA	Manufacturing
Swarnakar et al. (2021)	√	-	√	√	√	-	-	-	VSM	Manufacturing
Castiglione et al. (2022)	√	-	√	-	√	-	-	-	MEIO	Manufacturing
Utama et al. (2022)	√	√	√	√	√	-	-	-	Delphi, Dematel – ANP, VSM	Furniture
Mubin et al. (2022)	√	√	√	√	√	-	√	-	AHP, VSM	Plastic Industry
This research	√	√	√	√	√	√	√	√	Delphi, AHP, SVSM	Tyre Manufacturing

Furthermore, most previous studies only used general economic, social, and environmental indicators. However, there is a shortcoming in using the three dimensions together, which causes the assessment of sustainable manufacturing performance not to be comprehensive. Therefore, this study proposes using workload and equipment efficiency indicators as part of a comprehensive sustainable manufacturing performance assessment. This research also develops a new approach based on workstation load in each sustainable manufacturing performance indicator. Therefore, this research proposes an integrated procedure that includes Delphi, AHP, and SVSM methods. In addition, this research also makes a significant contribution by applying the proposed approach to a case study in the tyre industry. This industry is rarely used in sustainable manufacturing performance assessments. The assessment results in the tyre industry are expected to provide an overview of decision-makers to improve the performance of sustainable manufacturing production lines.

3. Proposed framework

This section introduces the proposed framework for performance assessment in sustainable manufacturing practices. Figure 1 illustrates the proposed framework, which consists of two main stages: evaluation of relevant indicators and assessment of sustainable manufacturing performance. The first stage involves the review of relevant indicators, which are based on the TBL lean manufacturing indicators. The TBL indicators used are based on lean manufacturing indicators. Using lean manufacturing indicators is important in assessing sustainable manufacturing performance to improve production line performance (Geoff, Pawloski, and Standridge 2010). Lean manufacturing eliminates waste, optimises value streams, and improves operational efficiencyBen Ruben, Vinodh, and Asokan 2019; Saetta and Caldarelli 2020). By applying lean principles, production lines can achieve sustainability goals more effectively, including waste reduction, resource savings, and improved sustainable manufacturing performance (Vinodh, Ben Ruben, and Asokan 2016a). In addition, by using lean manufacturing indicators as a performance evaluation tool, companies can measure performance in achieving sustainability goals and identify areas for improvement.

Figure 1. Proposed framework for sustainable manufacturing performance.

Flow chart for Proposed Framework to assessment sustainable manufacturing performance.

Read the detailed description of this figure

Long Description: The flow chart for assessing sustainable manufacturing performance is divided into two stages: selecting relevant indicators and assessing sustainable manufacturing. Several steps at the selection relevant indicator stage include identifying lean and sustainable manufacturing indicators, assessing indicator relevance by experts, and determining relevant indicators based on WA and LC. The sustainable manufacturing assessment stage includes determining the formula and calculation of indicator efficiency, mapping efficiency with sustainable VSM, weighting indicators and workstations with AHP, determining the performance of each TBL dimension, and finally, calculating the manufacturing sustainability index.

To select relevant indicator, the proposed framework offers the Delphi method to select appropriate indicators based on expert judgement regarding the indicators in sustainable manufacturing. The indicator relevance assessment questionnaire is based on a scale of 1–5, where 1 indicates irrelevance, and 5 indicates very high relevance. Experts’ assessment results of sustainable manufacturing indicators are calculated using the Weight Average (WA) and Level of Consensus (LC) methods. The sustainable manufacturing indicators are relevant for assessing sustainable manufacturing performance if the indicator produces a WA value ≥ 4.0 and LC ≥ 0.7 (Feil, de Quevedo, and Schreiber 2015; Miller 2001). Equations (1)(1) $WA=\frac{\sum SRi}{Nr}$(1) and (2(2) $LC=\frac{FNR}{Nr}$(2) ) present the formulas for calculating Weight Average (WA) and Level of Consensus (LC) in detail. The variable $SRi$ refers to the relevance assessment score of the i-th respondent, and $FNR$ refers to the number of respondents who gave relevant answers. The total number of respondents is represented by the symbol $Nr$.

(1) $WA=\frac{\sum SRi}{Nr}$(1)

(2) $LC=\frac{FNR}{Nr}$(2)

An efficiency assessment of each indicator and Sustainable Value Stream Mapping (SVSM) was conducted in the next stage of the proposed framework. These results are used for MSI assessment. Therefore, the selected indicators were weighted using the AHP method. In addition, the AHP method was also used to give weight to each workstation in each production indicator. Through pairwise comparisons, the weight for each indicator and workstation is determined using a value scale from 1 to 9. The 1–9 scale indicates the same level of importance between up to one element is absolutely more important (Amallynda, Anray Tama Hidayatulloh, and Marsetiya Utama 2022; Teguh, Marsetiya Utama, and Faisal Ibrahim 2022; Ibrahim, Laurensia, and; Utama 2021). In this research, pairwise comparisons AHP is based on Focus Group Discussion (FGD) of expert opinions to agree on the level of importance of indicators and work stations.

The stages of the AHP method go through several steps, including the pairwise comparison stage, matrix normalisation, and calculation of eigenvalue and eigenvector. The AHP stage ends with consistency checking (Utama 2021, 2021; Utama et al. 2021). At the pairwise comparison stage, pairwise comparisons are made between each indicator in the hierarchy. The results of this comparison are represented in matrix $A$ according to Equation 3(3) $A=\left[\begin{array}{cc}\begin{array}{cc}{a}_{11}& a_{12}\\ a_{21}& a_{21}\end{array}& \begin{array}{cc}\cdots & a_{1n}\\ \cdots & a_{2n}\end{array}\\ \begin{array}{cc}⋮& ⋮\\ a_{n1}& a_{n2}\end{array}& \begin{array}{cc}\ddots & ⋮\\ \cdots & a_{nn}\end{array}\end{array}\right]$(3) , then matrix $A$ is normalised into matrix $A_1$ according to Equation 4(4) $A_1=\left[\begin{array}{cc}\begin{array}{cc}a{ }_{11}^{\prime }& a{ }_{12}^{\prime }\\ a{ }_{21}^{\prime }& a{ }_{21}^{\prime }\end{array}& \begin{array}{cc}\cdots & a{ }_{1n}^{\prime }\\ \cdots & a{ }_{2n}^{\prime }\end{array}\\ \begin{array}{cc}⋮& ⋮\\ a{ }_{n1}^{\prime }& a{ }_{n2}^{\prime }\end{array}& \begin{array}{cc}\ddots & ⋮\\ \cdots & a{ }_{nn}^{\prime }\end{array}\end{array}\right]$(4) which is calculated using Equati on (5). Matrix $A_1$ is then used to calculate the eigenvalue $(W_i)$ and eigenvector ($W$) according to Equation 11(11) $CR=\frac{CI}{RandomIndex\left(RI\right)}$(11) to Equation 11(11) $CR=\frac{CI}{RandomIndex\left(RI\right)}$(11) . The eigenvalue of the pairwise comparison between indicators is marked as${\lambda }_{max}$. To ensure the consistency of the pairwise comparison matrix, the Consistency Index (CI) and Consistency Ratio (CR) was calculated according to Equation 11(11) $CR=\frac{CI}{RandomIndex\left(RI\right)}$(11) and Equation 11(11) $CR=\frac{CI}{RandomIndex\left(RI\right)}$(11) . If the CR value is more than 10%, the pairwise comparison matrix is considered consistent in the AHP method. The indicator weights obtained from the AHP method are then used as importance scores in the MSI.

In the proposed framework, 16 indicators are offered, with the efficiency formula for each indicator presented in detail in Table 2. This table shows the steps to be followed to assess the efficiency of each indicator. Using this formula, the assessment of indicator efficiency aims to provide an objective and systematic evaluation. In addition, a formula for calculating MSI is also proposed in this proposed framework. This index is a critical metric in evaluating the sustainability level of manufacturing processes. The procedure used to calculate this index is presented in detail in Table 3. The table describes the steps to calculate the manufacturing sustainability index, which includes various essential aspects such as indicator weights, workstation weights for each indicator, and indicator efficiency.

Table 2. Indicator Formulas.

Indicator	Input	Formulas	Reference
Time	TE = Time Efficiency VAT = Time of value-added activities TT = Total Time NVAT = Non-value-added activity time n = number of process stages	$TE=\frac{VAT}{TT}$ $VAT=\sum _{i=1}^n\left(VATi\right)$ $NVAT=\sum _{i=1}^n\left(NVATi\right)$ $TT=\hspace{0.17em}VAT+NVAT$	Vinodh et al. (2014)
Cost	CE = Cost efficiency VAC = Costs on value-added activities NVAC = Costs on non-value-added activities TC = Total cost n = number of process stages	$CE=\frac{VAC}{TC}$ $VAC=\sum _{i=1}^n\left(VACi\right)$ $NVAC=\sum _{i=1}^n\left(NVACi\right)$ $TC=\hspace{0.17em}VAC+NVAC$	Hartini, Ciptomulyono, and Anityasari (2020)
Quality	QE = Quality Efficiency ND = Total Defects TM = Total Material	$QE=1-\hspace{0.17em}\left(\frac{ND}{TM}\right)$	Hartini, Ciptomulyono, and Anityasari (2020)
Inventory	IE = Inventory Efficiency NI = Total inventory TM = Total material	$IE=\frac{NI}{TM}$	Hartini, Ciptomulyono, and Anityasari (2020)
Equipment efficiency	A = Availability Rate LT = Loading Time TD = Total Downtime P = Performance Rate IC = Ideal Cycle time O = Output OP = Operating Time Q = Quality Rate TP = Total Product DP = Defect Product	$A=\hspace{0.17em}\frac{\left(LT-TD\right)}{LT}\hspace{0.17em}x\hspace{0.17em}100\mathrm{\%}$ $P=\left(\frac{IC\hspace{0.17em}x\hspace{0.17em}O}{OP}\right)x100\mathrm{\%}$ $Q=\frac{\left(TP-DP\right)}{TP}x100\mathrm{\%}$ $OEE=A\hspace{0.17em}x\hspace{0.17em}P\hspace{0.17em}x\hspace{0.17em}Q$	Nakajima (1988)
Material Consumption	ME = Material Efficiency VAM = Material in value-added process TM = Total Material NVAM = Material in non-value-added process n = number of process stages	$ME=\frac{VAM}{TM}$ $VAM=\sum _{i=1}^n\left(VAMi\right)$ $NVAM=\sum _{i=1}^n\left(NVAMi\right)$ $TM=\hspace{0.17em}VAM+NVAM$	Vinodh et al. (2014)
Energy Consumption	EE = Energy Efficiency VAE = Value-added energy use NVAE = Non-value-added energy use n = number of process stages	$EE=\frac{VAE}{TE}$ $VAE=\sum _{i=1}^n\left(VAEi\right)$ $NVAM=\sum _{i=1}^n\left(NVAEi\right)$ $TE=\hspace{0.17em}VAE+NVAE$	Vinodh et al. (2014)
Water Consumption	EW = Water Efficiency VAW = Value-added Water Consumption NVAW = Non-value-added water consumption n = number of process stages	$EE=\frac{VAW}{TW}$ $VAW=\sum _{i=1}^n\left(VAWi\right)$ $NVAW=\sum _{i=1}^n\left(NVAWi\right)$ $TW=\hspace{0.17em}VAW+NVAW$	Faulkner and Badurdeen (2014)
B₃ Consumption	BB = Raw materials that are hazardous TB = Total weight of hazardous raw materials FN = Normal Factor (normal weight of the product)	$BB=\frac{TB}{FN\hspace{0.17em}}\hspace{0.17em}x\hspace{0.17em}100\mathrm{\%}$	Rizal (2018)
Land Use	LT = Land Covered LA = Building Covered Area TL = Total Manufacturing Area	$LT=\frac{LA}{TL}\hspace{0.17em}x\hspace{0.17em}100\mathrm{\%}$	Rizal (2018)
Mental Load	NASA Task Load Index (NASA TLX) MLIE= Mental load index efisiensi	$MLIE=1-\left(\frac{ScoreNASA\mathrm{T}\mathrm{L}\mathrm{X}}{56}\right)$	Simanjuntak (2010)
Physical Load Index	Physical Load Index (PLI) PLIE = Physical load index efficiency	$PLIE=1-\left(\frac{Score\mathrm{P}\mathrm{L}\mathrm{I}}{100}\right)$	Faulkner and Badurdeen (2014) & Hollmann et al. (1999)
Satisfaction Level	SL = Satisfaction Level TO = Number of employee turnover NE = total number of employees	$SL=1-\hspace{0.17em}\frac{TO}{NE}$	Hartini, Ciptomulyono, and Anityasari (2020)
Safety Level	RE = Safety Level NR = Number of activities at risk Nac = total activities	$RE=1-\hspace{0.17em}\frac{NR}{Nac}$	Hartini, Ciptomulyono, and Anityasari (2020)
Healthy Level	HE = Healthy level NA = number of absent employees NE = total number of employees	$HE=1-\hspace{0.17em}\frac{NA}{NE}$	Hartini, Ciptomulyono, and Anityasari (2020)
Employee Training	E_HRD = Employee training level NT = number of employees who participated in training NE = total number of employees	$E\mathrm{\_}HRD=\frac{NT}{NE}$	Hartini, Ciptomulyono, and Anityasari (2020)

Table 3. Manufacturing Sustainability Index formula.

Dimension	Input	Formulas
Economic Index	${\mathrm{W}}_{\mathrm{i}}$ = weight of indicator-i ${\mathrm{E}}_{\mathrm{i}}$ = score of economic indicator-i $\mathrm{i}$ = total economic indicators ${\mathrm{W}}_{\mathrm{j}}$= weight of workstation-j ${\mathrm{E}}_{\mathrm{j}}$ = score on workstation-j $\mathrm{j}$ = total workstations	$E\mathrm{\_}I=\sum _{i=1}^n{W}_i\hspace{0.17em}.\hspace{0.17em}{E}_i$ Where $E_i=\hspace{0.17em}\sum _{j=1}^n{W}_j\hspace{0.17em}.\hspace{0.17em}{E}_j$
Environmental Index	${\mathrm{W}}_{\mathrm{i}}$ = weight of indicator-i $V_{\mathrm{i}}$ = score of environmental indicator-i $\mathrm{i}$ = total environmental indicators ${\mathrm{W}}_{\mathrm{j}}$= weight of workstation-j $V_{\mathrm{j}}$ = score on workstation-j $\mathrm{j}$ = total workstations	$L\mathrm{\_}I=\sum _{i=1}^n{W}_i\hspace{0.17em}.\hspace{0.17em}{V}_i$ Where $V_i=\hspace{0.17em}\sum _{j=1}^n{W}_j\hspace{0.17em}.\hspace{0.17em}{V}_j$
Social Index	${\mathrm{W}}_{\mathrm{i}}$ = weight of indicator-i $S_{\mathrm{i}}$ = score of social indicator-i $\mathrm{i}$ = total social indicators ${\mathrm{W}}_{\mathrm{j}}$= weight of workstation-j $S_{\mathrm{j}}$ = score on workstation-j $\mathrm{j}$ = total workstations	$S\mathrm{\_}I=\sum _{i=1}^n{W}_i\hspace{0.17em}.\hspace{0.17em}{S}_i$ Where $V_i=\hspace{0.17em}\sum _{j=1}^n{W}_j\hspace{0.17em}.\hspace{0.17em}{S}_j$
Manufacturing Sustainability Index (MSI)	E_I = Economic Index L_I = Environmental Index S_I = Social Index α = weight of the economic dimension β = weight of the environmental dimension ^γ = weight of social dimension	$MSI=\left(\alpha .\mathrm{E}\mathrm{\_}\mathrm{I}\right)+$ $\left(\beta .\mathrm{L}\mathrm{\_}\mathrm{I}\right)+\left(\gamma .\mathrm{S}\mathrm{\_}\mathrm{I}\right)$

(3) $A=\left[\begin{array}{cc}\begin{array}{cc}{a}_{11}& a_{12}\\ a_{21}& a_{21}\end{array}& \begin{array}{cc}\cdots & a_{1n}\\ \cdots & a_{2n}\end{array}\\ \begin{array}{cc}⋮& ⋮\\ a_{n1}& a_{n2}\end{array}& \begin{array}{cc}\ddots & ⋮\\ \cdots & a_{nn}\end{array}\end{array}\right]$(3)

(4) $A_1=\left[\begin{array}{cc}\begin{array}{cc}a{ }_{11}^{\prime }& a{ }_{12}^{\prime }\\ a{ }_{21}^{\prime }& a{ }_{21}^{\prime }\end{array}& \begin{array}{cc}\cdots & a{ }_{1n}^{\prime }\\ \cdots & a{ }_{2n}^{\prime }\end{array}\\ \begin{array}{cc}⋮& ⋮\\ a{ }_{n1}^{\prime }& a{ }_{n2}^{\prime }\end{array}& \begin{array}{cc}\ddots & ⋮\\ \cdots & a{ }_{nn}^{\prime }\end{array}\end{array}\right]$(4)

(5) $a{ }_{ij}^{\prime }=\frac{a_{ij}}{{\sum }_{i=1}^n{a}_{ij}}fori,j=1,2,3,\dots n$(5)

(6) $W=\left[\begin{array}{c}{W}_1\\ \begin{array}{c}{W}_2\\ ⋮\\ W_n\end{array}\end{array}\right]$(6)

(7) $W_i=\frac{{\sum }_{i=1}^{n}a{ }_{ij}^{\prime }}{n}$(7)

(8) $W{ }^{\mathrm{\prime }}=AW=\left[\begin{array}{c}W{ }_1^{\prime }\\ \begin{array}{c}W{ }_2^{\prime }\\ ⋮\\ W{ }_n^{\prime }\end{array}\end{array}\right]$(8)

(9) ${\lambda }_{max}=\frac{1}{n}\left(\frac{{W{ }^{\mathrm{\prime }}}_1}{W_1}+\frac{{W{ }^{\mathrm{\prime }}}_2}{W_2}+\dots +\frac{{W{ }^{\mathrm{\prime }}}_n}{W_n}\right)$(9)

(10) $CI=\hspace{0.17em}\frac{{\lambda }_{max}-n}{n-1}$(10)

(11) $CR=\frac{CI}{RandomIndex\left(RI\right)}$(11)

Furthermore, this proposed framework offers the TLS procedure to be applied in mapping the efficiency of sustainable manufacturing indicators in SVSM. The purpose of TLS embedded in SVSM is to make it easier for decision-makers to assess the indicators of sustainable manufacturing on the production line. This visualisation makes it easier for decision-makers to pay attention to indicators and workstations that require improvement. The sustainable manufacturing indicators are classified into three colours: red, yellow, and green. If the efficiency value of a shrinkable manufacturing indicator is less than 60%, the indicator is marked red. The red mark indicates that the performance of the sustainable manufacturing indicator needs to be improved immediately. Meanwhile, if the efficiency score of the sustainable manufacturing indicator is 60% to 90%, the indicator is marked in yellow. The yellow mark indicates that the indicator’s performance needs to be improved to achieve optimal results. However, if the efficiency value of the indicator is more than 90%, the indicator is marked green. The green mark indicates that the indicator performs well and achieves the desired results.

The TLS principle is also applied in the assessment of MSI. By using TLS in classifying MSIs, manufacturing companies can monitor their sustainable performance more effectively and know the implementation of sustainable manufacturing holistically. This result makes it easier for decision-makers to assess the level of sustainable manufacturing performance of all activities in the production line. With this performance, decision-makers can determine improvement strategies and actions needed to achieve sustainable manufacturing performance.

4. Case study and Application

In this research, a case study was conducted in a tyre industry in Malang, Indonesia, to apply the proposed procedure. To make a product the production process in this tyre industry has ten stages. The stages include raw material receiving, mixing, filtering, extrusion, cooling, splicing, curing, quality control, packaging, and warehousing. Five tyre industry experts conducted an independent assessment based on the Delphi method to assess the relevant indicators of sustainable manufacturing. The results of this assessment are presented in Table 4. After that, a Focus Group Discussion (FGD) was conducted with the participation of 5 experts to conduct a comparative evaluation of indicator weights and workstation weights in pairs. Through the collaboration of experts in this assessment, a more consistent and accurate evaluation of indicator and workstation weights in the context of the tyre industry is expected.

Table 4. Results of recapitulation of each indicator.

Dimension	Indicators	Relevance
		1	2	3	4	5	WA	LC
Economic	Time	0	0	0	4	1	4,2	1
	Cost	0	0	0	1	4	4,8	1
	Quality	0	0	1	3	1	4	0,8
	Inventory	0	0	1	3	1	4	0,8
	Equipment efficiency	0	0	1	2	2	4,2	0,8
Environment	Material Consumption	0	0	0	2	3	4,6	1
	Energy Consumption	0	0	0	2	3	4,6	1
	Water Consumption	0	0	2	3	0	3,6	0,6
	B₃ Consumption	0	0	4	1	0	3,2	0,2
	Land Use	2	3	0	0	0	1,6	0
Social	Mental Load	0	0	0	3	2	4,4	1
	Physical Load Index	0	0	0	5	0	4	1
	Satisfaction Level	0	0	0	1	4	4,8	1
	Safety Level	0	0	2	2	1	3,8	0,6
	Healthy Level	0	0	0	5	0	4	1
	Employee Training	0	0	2	3	0	3,6	0,6

The relevance assessment of each indicator uses a Delphi questionnaire that has been distributed to 5 experts in the company. The results of the relevance assessment are presented in Table 4. In addition, this section also conducted pairwise comparisons for each indicator and each workstation. Furthermore, five experts conducted a Focus Group Discussion (FGD) to conduct pairwise comparisons based on each TBL aspect, indicator, and workstation. The results of pairwise comparisons of TBL dimensions are presented in Tables 5–8.

Table 5. Pairwise comparison of TBL dimensions.

	Economic	Environment	Social
Economic	1,00	1,00	1,00
Environment	-	1,00	1,00
Social	-	-	1,00

Table 6. Pairwise comparison of economic dimension indicators.

	Time	Cost	Quality	Inventory	Equipment efficiency
Time	1,00	1/5	3,00	3,00	1,00
Cost	-	1,00	5,00	3,00	2,00
Quality	-	-	1,00	1,00	1/3
Inventory	-	-	-	1,00	1/3
Equipment efficiency	-	-	-	-	1,00

Table 7. Pairwise comparison of environmental dimension indicators.

	Material Consumption	Energy Consumption
Material Consumption	1,00	3,00
Energy Consumption	-	1,00

Table 8. Pairwise comparison of social dimension indicators.

	Mental Load	Physical Load	Satisfaction Level	Health Level
Mental Load	1,00	5,00	3,00	3,00
Physical Load	-	1,00	5,00	5,00
Satisfaction Level	-	-	1,00	1,00
Health Level	-	-	-	1,00

5. Results and Discussion

5.1 Indicators and workstations weights

In this study, the weighting of each TBL dimension is based on relevant indicators. It shows that the weight on each TBL dimension, which includes economic, social, and environmental, in the assessment of sustainable manufacturing, is 0.333. This result indicates that the industry considers the three dimensions of TBL equally crucial in achieving manufacturing sustainability. This study’s results align with Kuhlman and Farrington (2010) and Hartini, Ciptomulyono, and Anityasari (2020), which state that the three dimensions of TBL should have equal importance in assessing sustainable manufacturing performance. Companies can improve their sustainable manufacturing performance by balancing the weight of the economic, social, and environmental dimensions.

The results of pairwise comparisons of the consistency ratio of each TBL dimension and TBL dimension indicators are also presented. The consistency ratio results for pairwise comparisons of TBL dimensions show a consistency ratio value 0. Meanwhile, the consistency ratio values for pairwise comparisons of economic, environmental, and social dimension indicators are 0.07438, 0, and 0.065437, respectively. These results show that pairwise comparisons of each TBL dimension and TBL dimension indicators are consistent because the consistency ratio value is below 0.1.

Furthermore, the indicator weight of each dimension can be seen in Table 9. The results showed that the sustainable manufacturing Material Consumption indicator in the environmental dimension has the highest weight of 0.250. It shows that the material consumption indicator is crucial in sustainable manufacturing. It is because material consumption is the leading cause of negative environmental impacts. Meanwhile, the Mental Load indicator produces an importance weight score of 0.187. It shows that the mental workload of workers affects sustainable manufacturing performance because mental workload impacts the company’s performance in the long run. In addition, the Cost indicator also has a significant contribution with a weight of 0.147. It shows that economic aspects remain an important consideration in sustainable manufacturing. These results provide insight into the importance of material consumption, mental workload, and cost in achieving sustainability goals in the manufacturing process. Based on these findings, the material consumption indicator in sustainable manufacturing deserves attention because it can reduce costs, waste, and carbon dioxide emissions generated from the production process. With good material consumption performance efficiency, the manufacturing industry can increase the production line’s efficiency, reducing the negative impact on the environment (Fiksel 2006). In addition, indicators of worker mental load also need attention in the manufacturing industry. An ideal mental load for workers can increase productivity and impact the company’s financial performance in the long run (Mubin, Marsetiya Utama, and Chandra Nusantara 2022). Meanwhile, cost indicators in sustainable manufacturing should also receive attention because they can help reduce production costs and improve efficiency due to non-value-added activities (Utama, Ardiyanti, and Putri 2022). The manufacturing industry is expected to produce good, sustainable manufacturing performance values by paying attention to these critical indicators and not neglecting others.

Table 9. Indicator weight of each dimension.

Dimension	Wight	Indicators	Wight Indicators	Global Wight
Economic	0.333	Time	0.19	0.063
		Cost	0.44	0.147
		Quality	0.07	0.023
		Inventory	0.09	0.030
		Equipment efficiency	0.21	0.070
Environment	0.333	Material Consumption	0.75	0.250
		Energy Consumption	0.25	0.083
Social	0.333	Mental Load	0.56	0.186
		Physical load index	0.26	0.087
		Satisfaction Level	0.12	0.040
		Health Level	0.06	0.020

Furthermore, the results of the weighting of each workstation on each indicator are shown in Table 10. The results of this weighting show that each workstation has a different weight for each indicator. This variation is due to the weight based on the workload in each indicator. With the variation of workstation weights on each other indicator, decision-makers can quickly find out which workstations require more critical attention. In addition, managers and decision-makers can quickly identify workstations that contribute significantly to sustainable manufacturing performance. With this information, managers can quickly identify workstations on the production line that need improvement so that improvement strategies can be decided quickly. Therefore, the weighting of workstations on each indicator helps assess sustainable manufacturing performance.

Table 10. Indicator weight of each workstation.

Indicators	Raw material received	Mixing	Filtering	Extruding	Cooling	Splicing	Curing	Quality Control	Packing	Warehouse
Time	0,03	0,15	0,05	0,09	0,02	0,18	0,34	0,05	0,07	0,03
Cost	0,28	0,15	0,05	0,06	0,02	0,21	0,05	0,02	0,11	0,05
Quality	0,27	0,07	0,08	0,07	0,02	0,1	0,16	0,15	0,05	0,04
Inventory	0,35	0,15	0,06	0,05	0,03	0,09	0,12	0,07	0,05	0,02
Equipment efficiency	-	0,17	0,11	0,07	0,04	0,24	0,36	-	-	-
Material Consumption	-	0,26	0,12	0,13	0,09	0,09	0,13	0,04	0,07	0,07
Energy Consumption	-	0,53	0,13	0,16	0,06	0,09	0,03	-	-	-
Mental Load	0,03	0,11	0,05	0,04	0,07	0,15	0,11	0,17	0,2	0,08
Physical Load	0,21	0,2	0,05	0,03	0,05	0,04	0,07	0,08	0,1	0,17
Satisfaction level	1
Health Level	1

The industry needs to focus on the curing process in the time indicator. This process takes a large amount of time, so it requires attention. In the Cost, Quality, and Inventory indicators, the Raw material received workstation needs more attention in this indicator. It is very reasonable because this workstation needs to ensure good quality raw materials so that the weight of cost, quality, and inventory is higher than other workstations. In the Material Consumption and Energy Consumption indicators, the Mixing workstation has the greatest weight because this workstation requires more significant consumption of raw materials and energy than other workstations. Meanwhile, the mental load indicator on the Splicing workstation accounts for the highest weight because this workstation requires a higher mental load than other workstations. Finally, the Physical Load indicator at the Raw material received workstation generates the highest weight because employees require high effort in loading and unloading raw materials.

5.2 Sustainable value stream mapping

This section presents the production line mapping based on SVSM and it can be seen in Figure 2 It shows that two indicators of mental load and physical load have less efficiency value. In the mental load indicator, two workstations have an efficiency value below the standard, namely in the curing process with an efficiency of 58.48% and packing with an efficiency of 56.90%. Furthermore, in the physical load index indicator, there is one workstation with an efficiency value of 58.42%, also categorised as a less-than-optimal value. These findings indicate that social performance, primarily mental and physical load indicators in tyre companies, still needs to be improved. The problem of mental and physical load indicators in the social dimension of tyre companies needs to be an essential concern for decision-makers to improve their sustainable manufacturing performance. Proposed improvements that can be made include redesigning workstations at workstations that produce low efficiency. In addition, managers need to identify the causes of high mental and physical burdens on workers. An increased workload on workers can reduce the performance of sustainable manufacturing. Several measures include training to minimise workload, improve work facilities, and use technology to reduce physical and mental load. With these efforts, it is expected that the sustainable manufacturing performance of the tyre industry will improve.

Figure 2. SVSM for a case study on the tire Industry.

Performance overview of each indicator and production process stage based on TLS presented in SVSM.

Read the detailed description of this figure

Long Description: An overview of the performance of each indicator and each stage of the production process. Based on TLS, 6 indicators need to be improved: time, inventory, equipment efficiency, mental load, physical load, and health level indicators. Three workstations need immediate improvement on mental and physical load indicators, including mixing, curing, and packing.

5.3 Sustainable manufacturing performance score

The section presents the sustainable manufacturing performance score given in the MSI index. The assessment results based on MSI show that the sustainable manufacturing performance of the tyre industry has a score of 85.52%. This MSI score indicates that the sustainable manufacturing performance of this industry is categorised as lacking, marked in yellow on the TLS. Meanwhile, the distribution of performance on TBL dimensions that include economic, social, and environmental dimensions can be seen in Figure 3. It shows that environmental performance has the highest score, followed by economic and social dimensions. The environmental dimension has the highest performance, implying that the tyre industry has paid attention to environmental issues such as material and energy consumption indicators. Meanwhile, performance on the social dimension in this company still has a lower score than other dimensions such as environment and economy. This result indicates that this tyre company pays less attention to social dimensions such as mental load, physical load, health level, and employee satisfaction level. Improvements in the social dimension of mental and physical health and employee satisfaction levels must be followed up immediately. By improving sustainable manufacturing indicators in the social dimension, sustainable manufacturing performance is expected to improve.

Figure 3. Index performance for each TBL Dimension.

Performance overview of each TBL with performance in the economic dimension of 30.09, environmental of 32.87, and social of 22.57.

In this study, social aspects have received less attention in the tyre industry, even though these aspects have a very significant role in evaluating sustainable manufacturing performance. These social aspects not only have a positive impact on working conditions but also have the potential to support the long-term sustainability of the company (Ortiz‐de‐Mandojana and Bansal 2016). Employees are a valuable asset to the company, and maintaining job satisfaction levels is key to maintaining quality performance. In addition, a comfortable work environment that supports individual development will create a pleasant atmosphere, increasing employee morale and loyalty to the company (Basalamah and As’ad 2021). Employees working in optimal conditions also have a long-term impact on improving the quality and performance of the company. Conversely, if employees experience mental and physical stress due to inadequate working conditions, this will hurt their health (Kolstrup et al. 2013). Therefore, companies need to pay special attention to employees’ physical and mental health to improve performance in continuous manufacturing. In addition, employees who are satisfied with a work environment that supports their development will be more likely to contribute creatively and innovatively. Social aspects such as employee well-being and satisfaction should not be overlooked in the face of sustainability challenges (Haque 2021).

6. Implications

This section presents the research implications of sustainable manufacturing assessment that have an essential impact based on academic and managerial perspectives. Theoretically, this research contributes to theory development in sustainable manufacturing performance assessment. Meanwhile, from a managerial perspective, this research directly impacts managers/decision-makers in assessing sustainable manufacturing performance in the tyre industry. A detailed description is presented in the next section.

6.1 Academic implication

Based on theoretical implications, this finding significantly impacts the understanding of comprehensively assessing sustainable manufacturing. By involving sustainable manufacturing indicators based on the three dimensions of TBL, this research substantially contributes to assessing sustainable manufacturing performance. A proposed framework for evaluating sustainable manufacturing is offered to improve previous research’s weaknesses that ignore each workstation’s weight. The proposed integrated method provides a more comprehensive approach to assessing sustainable manufacturing performance. In addition, this research also innovates by involving equipment efficiency and physical and mental workload indicators that are rarely applied in the assessment of sustainable manufacturing. Therefore, the proposed framework can enrich the knowledge in evaluating sustainable manufacturing.

6.2 Managerial implication

Meanwhile, on a managerial basis, the implications of this research are valuable for managers and decision-makers in improving the performance of sustainable manufacturing in the tyre industry. The research findings show that the social dimension, especially the Mental and Physical Load indicators, is inefficient. The low performance of these indicators can be seen at the packing, curing, and mixing workstations. Therefore, to improve performance in the social dimension, especially the Mental and Physical Load indicators, the tyre industry is advised to implement Standard Operating Procedures to reduce Mental and Physical Load. In addition, other efforts, such as providing restrooms for workers and increasing work concentration, are proposed to improve this indicator. The company needs to set realistic targets for workers without putting excessive pressure on them that can burden mental and physical load. It can hurt workers and the company in the long run. To overcome the Physical Load problem, the company needs to use ergonomic work procedures such as tools on the filling machine and mixing machine to reduce the physical load of workers. By implementing these proposed improvements, the company is expected to improve the performance of sustainable manufacturing shrinkage in the tyre industry.

7. Conclusion

In conclusion, this study introduces a new approach integrating Delphi, AHP, and SVSM techniques to evaluate sustainable manufacturing performance. The proposed framework encompasses indicator selection via the Delphi method, determination of indicator and workstation weights using AHP, efficiency calculations for each indicator, and visualisation of efficiency outcomes using SVSM and TLS. Through this research, the manufacturing sustainability score can be determined by considering the weighting and performance of each indicator. Furthermore, applying this framework in assessing sustainable manufacturing performance within the Indonesian tyre industry yielded a notable result of 85.52%, as indicated in yellow. However, there is room for improvement in the tyre industry’s sustainable manufacturing practices.

This research also has some implications. This research has shed light on a promising methodology for evaluating sustainable manufacturing performance. The successful application in the tyre industry underscores the potential applicability of this framework in other manufacturing sectors. Additionally, identifying areas for improvement suggests that targeted interventions can lead to enhanced sustainability practices within the industry.

Nevertheless, it is imperative to acknowledge the limitations of this study. It primarily relies on case studies within the tyre industry and does not account for potential interdependencies between sustainable manufacturing indicators. This oversight points to a crucial avenue for future research, namely, incorporating indicator dependency relationships in assessing sustainable manufacturing performance. Moreover, the scope of this research could be broadened by conducting case studies across diverse manufacturing industries and considering a wider array of sustainable manufacturing indicators. This expansion would contribute to developing a more comprehensive and robust procedure for evaluating sustainable manufacturing practices.

Authors contributions

All authors have contributed equally to the implementation of the research, the analysis of the results, and the writing and editing of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

All data generated or analysed during this study are included in this article.

Additional information

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Notes on contributors

Shanty Kusuma Dewi

Shanty Kusuma Dewi is currently a Lecturer and a Researcher in the Industrial Engineering Department at the University of Muhammadiyah Malang Indonesia. Her research interests include quality engineering, quality management, sustainable manufacturing and modelling. She can be contacted at email: [email protected]

Rini Febrianti

Rini Febrianti was undergraduate student in the Industrial Engineering Department at the University of Muhammadiyah Malang Indonesia. Her research interests include quality management and sustainable manufacturing. She can be contacted at email:[email protected]

Dana Marsetiya Utama

Dana Marsetiya Utama is currently a Lecturer and a Researcher in the Industrial Engineering Department at the University of Muhammadiyah Malang Indonesia. His research interests include optimization engineering, sustainable manufacturing, scheduling, and modeling. He can be contacted at email: [email protected]