Assessment of Sustainable Manufacturing in the Moroccan Automotive Industry Using an Integrated MCDM Approach
Rim BAKHAT1*, Manal AMMARI2 , Mohammed CHENTOUF3, Mohammed AMMARI4 and Laila BEN ALLAL5
1Management Logistics and Applied Management Department, Faculty of Law Economics and social Sciences, University of Abdelmalek Essaadi, Tangier, Morocco
*Correspondence: rbakhat@uae.ac.ma
2Research Team of Materials, Environment and Sustainable Development, Faculty of Sciences and Techniques. University of Abdelmalek Essaadi, Tangier, Morocco
Abstract— Environmental protection and energy safety are becoming increasingly associated with the overall performance indicators of the automotive industry. Recently, the sustainable manufacturing is heating the debate regarding the robust solutions that can ensure both economic growth and social prosperity while taking into account the emergence of the ecological concerns. The aim of the present paper is to propose a group of real-world indicators that facilitate the engagement of the sustainability at the business and social level in the automotive industry. As an answer to this challenge, a need remains for developing a systematic approach to quantitevly answer the research questions. A combination of multi-criteria decision-making (MCDM) methodology and sustainable indicators is proposed to evaluate the performance of a multinational firm localized in the North of Morocco. On one hand, the CRITIC technique (CRiteria Importance Through Intercriteria Correlation) is used to compute the weight of the criteria. On the other hand, the VIKOR (VIsekriterijumska optimizacija i KOmpromisno Resenje) technique is utilized to prioritize the manufacturing process in the last year. First, the study starts with appraising the previous methodologies employed in sustainable manufacturing. Then, an assessment of various indicators that impact on the decision-making process of sustainability engagement before and during the apparition of Covid-19 pandemic. The findings demonstrate that the proposed methodology is a crucial sustainable driver to ensure the triple bottom lines in the automotive industry.
Keywords—sustainable manufacturing; CRITIC; VIKOR;
automotive industr; COVID-19.
I. INTRODUCTION
In the past, the manufacturing sector was characterized by an urge to producing the maximum amount possible of goods at a cheaper cost and continuously and reliably [1]. This was driven by the abundance of resources and the scarcity of labour. However, recent global dynamics (depletion of non- renewable natural resources, an increase of manufacturing costs due to customer needs and concurrence, and labour expansion resulting from population growth) have led manufacturers worldwide to rethink their activities at
different supply chain levels by considering a Sustainable Manufacturing (SM) approach regarding different recent initiatives including lean management, green manufacturing, circular economy, and industry 4.0 [2]. Within this context, SM stands as one of the key solutions to achieve economic prosperity, while ensuring environmental stewardship and social wellbeing [3]. SM is traditionally defined as “the creation of manufactured products which use processes that minimize negative impacts, conserve energy and natural resources, are safe for employees, communities and are economically sound” [4] and it was believed that through SM, manufacturers can make for their past environmental damages and social alterations [5]. Historically, the regulatory framework was behind various restrictions that diminish the environmental burdens of manufacturers [6] following various incidents such as the alarming emissions of NOx and CO2 in the 1970s [7]. Yet, it is still debatable to evaluate whether global manufacturers are committed enough to achieve a sustainable transformation of their supply chain. Some studies found that such transitions have failed so far due to the prevailed confusion regarding the concept of sustainability, the absence of organizational frameworks, and the difficulty of measuring sustainability [8]. Currently, various SM strategies are being implemented within manufacturing plants including waste minimisation, material efficiency, resource efficiency and eco-efficiency among others [1]. Nevertheless, simultaneous consideration of the triple main concerns (individuals, benefit, and the planet) is required to achieve SM [10]. Furthermore, the activity sector of a manufacturer can highly influence his involvement in the achievement of SM.
Within this scope, the automotive sector stands as a key candidate to deploy SM through the famous triple helix of innovation model (the cooperation between university, industry and government) [11]. A growing interest in sustainability was triggered after the Dieselgate fraud in 2008 [8]. It is noteworthy to mention that the automotive sector contributes to 5.7% of GDP worldwide [12] and 8% of exports in 2018 [9]. Furthermore, the sector is known for its high
budget allocations to R&D activities exceeding 13.3 billion euros in 2018 [13].
Additionally, it is widely believed that the automotive sector can apply the famous 6 R’s (recycle, reuse, reduce, refuse, rethink and repair) successfully due to the nature of its supply chain and the innovative nature of manufactured goods [2]. Nevertheless, policy-makers need to be empowered with factual data regarding sustainability assessment of their activities to fully understand its impacts and define respective targets [14]. It was pointed out that the implementation of best practices requires the engagement of market players at different stages with robust actions backed by quantitative findings [15]. Based on the complex nature of SM and its interaction with various dimensions including the three bottom lines (economy, society, and environment) along with technological progress and performance management [16], various assessment techniques were suggested for evaluating SM including developing indicators, assessing a product or process performance (life cycle assessment), and evaluating the environmental impact of a given activity (environmental impact assessment) [17]. Within this framework, sustainability indicators seem to be a sound tool for addressing the above mentioned challenges regarding SM [18] and they can be adopted at various levels of boundary and complexity in order to evaluate sustainability on a global, national, sector, product, company, plant, process, and product levels [19]. In this setting, MCDM techniques provide factual insights to select the most significant factors for decision making regarding SM [15]. Anand et al., [20] have used fuzzy and fuzzy-AHP method to evaluate the sustainability of smart cities. The DEMATEL approach was used by [21] to categorize the key drivers of green manufacturing, while [22] elaborated a DSR- HNS framework to evaluate the sustainability of power generation. Finally, Mohammed et al., [23] proposed a hybrid TPOSIS-MOOM approach for choosing sustainable suppliers.
The present study proposes a systematic approach in multi- criteria decision-making methodology namely, CRITIC and VIKOR techniques to assess the real-world indicators in the automotive industry in Morocco and specifically after the apparition of the COVID-19. Based on relevant literature review, any research has adopted CRITIC-VIKOR model to evaluate the sustainability manufacturing indicators in the automotive sector. Thus, the main contribution of this paper is to provide a framework for implementing the sustainable targets within the automotive supply chain by enabling cross- functional decision-makers to select the key factors and quantify their importance thru highlighting their interactions both inside and outside the plant. As an answer to this challenge, a real-world case study with factual data from an automotive company implemented in Morocco to verify the proposed integrated model and with the aim of identifying key recommendations for enhancing the sustainability of the Moroccan automotive sector in the new circumstances of the pandemic. In this regard, the research questions of the present work are educed based on the descriptive and exploratory research classification. This study is designed to answer a set
of decisive research questions which could be incorporated as follows:
▪ (Q1) How to assess sustainable manufacturing through the case study of the Moroccan automotive industry?
▪ (Q2) What are the dimensions that should cope with in the COVID-19 time to attain sustainability?
The layout of this paper is as follows: Section 2 provides a frame of references regarding our methodology and various techniques that will be used to conduct this study. The case study will be presented in section 3, while the main findings and their interpretation is provided in section 4. Finally, major recommendations regarding SM in the case of the Moroccan automotive sector are provided in the final section right after concluding this work.
II. PROPOSEDMODEL
Multi-Criteria Decision-Making (MCDM) is a structured approach of operation research that contributes to analyse and simplify complex problems in various fields of the study with an appropriate range of techniques and methodologies [24].
The main role of the MCDM approach is to clearly define the problem and then determine realistic alternatives. Besides, it is necessary to also identify the list of the actors involved in the decision-making process within the study, and assess each alternative concerning the range of the criteria [24]. In the present study, the CRiteria Importance Through Intercriteria Correlation (CRITIC) method has been selected as an appropriate MCDM technique for evaluating the criteria weights due to its simplicity in calculating mathematical equations without taking the decision-makers’ preferences into account. On the other hand, the VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) method is selected as an adequate MCDM technique in the present work for assessing the alternatives due to its simplicity in solving the problem of the relative importance for complex real-life situations.
A. CRIITIC technique for indicators weights calculation
‘Suppose that 𝑿 = [𝒙𝒊𝒋]
𝒎×𝒏 represents the initial decision matrix entailing 𝒎 alternatives and 𝒏 criteria, where 𝒙𝒊𝒋 represents the performance measure of the 𝑖th alternative with respect to 𝑗 th criterion ( 𝒊 = 𝟏, 𝟐, 𝟑 … , 𝒎) and ( 𝒋 = 𝟏, 𝟐, 𝟑 … , 𝒏). To attain the weight 𝒘𝒋 of the 𝑗th criterion, there are various notation used during the process of applying the CRITIC method such as 𝒄𝒋 is the amount of the information used in the 𝑗th criterion, 𝝈𝒋 the standard deviation of the 𝑗th criterion, and 𝝆𝒋𝒌 the correlation coefficient between the 𝑗th criterion and 𝑖th criteria. The steps of the CRITIC method application are as follows [25]:’
Step1. Normalization of the initial matrix
𝑋̅ = {
𝑥𝑖𝑗−𝑥𝑗𝑚𝑖𝑛
𝑥𝑗𝑚𝑎𝑥−𝑥𝑗𝑚𝑖𝑛, 𝑖𝑓 𝑗 ∈ 𝐵;
𝑥𝑗𝑚𝑎𝑥−𝑥𝑖𝑗
𝑥𝑗𝑚𝑎𝑥−𝑥𝑗𝑚𝑖𝑛, 𝑖𝑓 𝑗 ∈ 𝑁𝐵;
(1)
‘Where: 𝒙𝒋𝒎𝒊𝒏 = 𝒎𝒊𝒏 (𝒙𝒊𝒋, 𝒊 = 𝟏, 𝟐, … , 𝒎) and 𝒙𝒋𝒎𝒂𝒙= 𝒎𝒂𝒙 (𝒙𝒊𝒋, 𝒊 = 𝟏, 𝟐, … , 𝒎). 𝑩 denotes the beneficial criteria (ideal values) and 𝑵𝑩 denotes the non-beneficial criteria (anti- ideal values).’
‘Step2. Compute the correlation coefficient between the criteria’
‘During the process of the criteria weight’s calculation,
‘the standard deviation’ and ‘the correlation between the criteria’ are both taken into an account [26]. The weight of the 𝑗th criterion are calculated with the use of equation 2 and 3 [27]:’
𝝆𝒊𝒋= ∑𝒎𝒊=𝟏(𝒓𝒊𝒋− 𝒓̅𝒋)(𝒓𝒊𝒋− 𝒓̅𝒋)
√∑𝒎𝒊=𝟏(𝒓𝒊𝒋− 𝒓̅𝒋)𝟐∑𝒎𝒊=𝟏(𝒓𝒊𝒋− 𝒓̅𝒋)𝟐
(2)
Step3. Calculate the criteria weights
𝑐𝑗= 𝜎𝑗∑(1 − 𝜌𝑖𝑗), (3)
𝑚
𝑖=1
𝑤𝑗= 𝑐𝑗
∑𝑚 𝑐𝑗 𝑖=1
(4)
‘Where: 𝜎𝑗 denotes the standard deviation of the 𝑗th criterion and 𝝆𝒊𝒋 presents the correlation coefficient between the 𝑗th and 𝑖th criteria.’
B. VIKOR technique for plant assessement
‘This method feasibly ranks the alternatives list by mutualising ‘the measure of closeness to the ideal alternatives’
[28].The multi-criteria closeness measure for alternative ranking was developed by applying ‘𝑳𝑷-metric’ as an aggregating function in a compromise programming method [29]. However, an MCDM approach is very often presented in a matrix format, where the columns refer to the set of criteria in the study, and rows indicate the list of the main alternatives.
The ‘𝑳𝑷-metric’ of the VIKOR method is explained in the following equation:’
𝐿𝑝,𝑖= {∑ [𝑤𝑗(𝑥𝑗∗− 𝑥𝑖𝑗)
(𝑥𝑗∗− 𝑥𝑗−)
⁄ ]
𝑛 𝑝
𝑗=1
}
1⁄𝑝
(5)
‘Where: 𝟏 ≤ 𝒑 ≤ +∞; 𝒊 = 𝟏, 𝟐, … , 𝒎. 𝑳𝟏,𝒊 denotes ‘the maximum group utility’, and 𝑳∞ refers to ‘the minimum individual regret’. The steps of the VIKOR method application are as follows [30]:’
Step1. Construct of the initial matrix
‘The list of the alternatives 𝐼 are represented as (𝑨𝟏, 𝑨𝟐, … , 𝑨𝒎) that are assessed by 𝒏 number of criteria (𝑪𝟏, 𝑪𝟐, … , 𝑪𝒏).Where, 𝒙𝒊𝒋 denotes the performance rating of the 𝑖𝑡ℎ alternative 𝑨𝒊 with regard to the 𝑗𝑡ℎ criterion 𝑪𝒋, as illustrated in the following matrix:’
𝑪𝟏 𝑪𝟐 𝑪𝟑 ⋯ 𝑪𝒏
𝑫 = 𝑨𝟏
𝑨𝟐
𝑨𝟑
⋮ 𝑨𝒎 [
𝒙𝟏𝟏 𝒙𝟏𝟐 𝒙𝟏𝟑 ⋯ 𝒙𝟏𝒏
𝒙𝟐𝟏 𝒙𝟐𝟐 𝒙𝟐𝟑 ⋯ 𝒙𝟐𝒏 𝒙𝟑𝟏 𝒙𝟑𝟐 𝒙𝟑𝟑 ⋯ 𝒙𝟑𝒏
⋮ ⋮ ⋮ ⋱ ⋮
𝒙𝒎𝟏 𝒙𝒎𝟐 𝒙𝒎𝟑 … 𝒙𝒏𝒎]
(6)
Step2. Compute the normalized values
𝒇𝒊𝒋= 𝒙𝒊𝒋
√∑𝒏𝒋=𝟏𝒙𝒊𝒋𝟐
, 𝒊 = 𝟏, 𝟐, … , 𝒎; 𝒋 = 𝟏, 𝟐, … , 𝒏 (7)
‘Step3. Define the best values 𝒙𝒋∗ and the worst values 𝒙𝒋−
of all the elements Where 𝒙𝒋∗= 𝐦𝐚𝐱
𝒊 𝒇𝒊𝒋 and 𝒙𝒋−= 𝐦𝐢𝐧
𝒊 𝒇𝒊𝒋 for beneficial and non-beneficial criteria.’
‘Step4. Calculate ‘the maximum group utility value’ 𝑆𝑖
and ‘the minimum individual regret value’ by applying the following equations:’
𝑺𝒊= 𝑳𝟏,𝒊 = ∑ 𝒘𝒋(𝒙𝒋∗− 𝒙𝒊𝒋)
(𝒙𝒋∗− 𝒙𝒋−)
⁄ ,
𝒏
𝒋=𝟏
(8)
𝑹𝒊= 𝑳∞,𝒊= 𝐦𝐚𝐱
𝒋 [∑ 𝒘𝒋
(𝒙𝒋∗− 𝒙𝒊𝒋)
(𝒙𝒋∗− 𝒙𝒋−)
⁄
𝒏
𝒋=𝟏
] (9)
‘Step5. Calculate the value 𝑄𝑖 by using the following equation:’
𝑸𝒊= 𝒗(𝑺𝒊− 𝑺∗)
(𝑺−𝑺∗) + (𝟏 − 𝒗)(𝑹𝒊− 𝑹∗)
(𝑹−− 𝑹∗), (10)
‘Where: the value of 𝒗 = 𝟎. 𝟓 in the present paper, 𝑺−= 𝐦𝐚𝐱𝒊 𝑺𝒊, 𝑺∗= 𝐦𝐢𝐧
𝒊 𝑺𝒊, 𝑹−= 𝐦𝐚𝐱
𝒊 𝑹𝒊, and 𝑹∗= 𝐦𝐢𝐧
𝒊 𝑹𝒊. After obtaining the results from this equation, the alternatives are ranked, with respect to the 𝑺, 𝑹 and 𝑸 values, in decreasing order.’
Step6. There are two conditions to verify the obtained results [30]:
1st Condition is based on the calculation of ‘the acceptable advantage’:
𝑸(𝑨𝟐) − 𝑸(𝑨𝟏) ≥ 𝑫𝑸, (11)
𝑫𝑸 = 𝟏
(𝒎 − 𝟏) (12)
‘Where: 𝑨𝟏 represents the alternative with the first position, and 𝑨𝟐 the alternative with the second position in the ranking.
2nd Condition is based on the calculation of ‘the acceptable stability in decision- making’: The alternative with the first position are ranked as the best choice by the use of 𝑺(𝑹) which must be higher than the alternative with the second position.’
III. RESULTSANDDISCUSSION
In the present work as the dimensions for the proposed model evaluation: Environmental Indicators (D1), Economic
Indicators (D2), and Social Indicators (D3). Besides, each dimension entails three sub-criteria and they vary from (C1- C11), (C12-C16), and (C17-C22) as explained in Figure 1.
Revealing the impact of the Covid-19 on the automotive industry in Morocco, the present study has focused on the variation in the last nine months just right after the apparition of the pandemic. However, the numbers of months are considered as alternatives ranging from September 2020 (A1) to January 2020 (A9). Therefore, the proposed model has been verified within a multinational company implemented in the North of Morocco. The latter is regarded as one of the most powerful organizations in the automotive industry. The data has been collected from the cross-functional decision- maker (Finance, Production, Human Resources, Health Safety and Environment department) with a work experience ranging from 12 to 18.
Figure 1: Sustainability Indicators Assessment. Source: own elaboration
The application process of the CRITIC method for evaluating the criteria weights of the most commonly impacted indicator is as follows: First, the initial decision matrix is explained in Table 1. Second, the normalized decision matrix is developed by applying equation 1 and it is presented in Table 2. The normalization step takes the criteria sort into account, where ‘total sales, produced hours, operating profits, investment, number of employees, operators efficiency,’ belong to the beneficial criteria category meanwhile, the rest criteria belong to the cost criteria category. The obtained values of the standard deviations are as follows: 𝝈𝒋= (0.301; 0.314; 0.374; 0.314; 0.277; 0.402; 0.500;
0.500; 0.500 0.500; 0.294; 0.325; 0.303; 0.295; 0.500; 0.416; 0.332;
0.390; 0.449; 0.408; 0.323). Third, the calculation of the correlation coefficient values is presented in Table 3. Finally,
the criteria weights are calculated by applying equations 3 and 4 as explained in Table 4. The criteria weights are combined with the VIKOR method equations to complete the procedure of assessing the alternatives. The calculated weights are considered as input in the VIKOR method. The normalized decision matrix is constructed with the help of equation 7 as illustrated in Table 5. In this step, the criteria sorts (beneficial and non-beneficial) are considered as well. Afterwards, the group utility functions are computed with the use of equation 5. Table 6 exhibits the obtained values of 𝑆𝑖 and 𝑅𝑖, using equations 8-9, and of 𝑄𝑖 applying equation 10. Finally, the ranking, in decreasing order, of the alternatives is given in Table 6 by using three different ranking performances 𝑆𝑖 𝑅𝑖, and 𝑄𝑖.
Table 1: Initial Decision matrix
Table 2: Normalized Decision matrix
(C1) C2) (C3) (C4) (C5) (C6) C7) (C8) (C9) (C10) (C11) (C12) (C13) (C14) (C15) (C16) (C17) (C18) (C19) (C20) (C21) (C22) (A1) 0.83 1.00 0.41 1.00 0.00 0.00 1.00 1.00 1.00 1.00 1.00 0.76 0.87 0.40 0.14 1.00 0.08 0.00 0.73 0.00 0.26 1.00 (A2) 1.00 0.70 0.94 0.70 0.76 0.51 1.00 1.00 1.00 1.00 1.00 0.39 0.44 0.41 0.23 1.00 0.24 0.69 0.77 0.10 0.44 0.97 (A3) 0.79 0.76 0.56 0.76 0.54 0.00 1.00 1.00 1.00 1.00 1.00 0.65 0.57 0.29 0.20 1.00 0.00 0.88 1.00 0.10 0.19 0.93 (A4) 0.00 0.00 0.00 0.00 0.64 0.46 1.00 1.00 1.00 1.00 1.00 0.53 0.81 0.46 1.00 0.00 0.63 0.30 0.90 0.15 0.70 0.95 (A5) 0.84 1.00 1.00 1.00 0.85 1.00 1.00 1.00 1.00 1.00 1.00 0.33 0.35 0.16 0.18 0.00 1.00 0.74 0.00 0.59 1.00 0.93 (A6) 1.00 1.00 1.00 1.00 1.00 0.98 1.00 1.00 1.00 1.00 1.00 0.00 0.00 0.00 0.27 0.00 1.00 0.87 0.10 0.98 1.00 0.00 (A7) 0.80 0.81 0.19 0.81 0.61 0.17 0.00 0.00 0.00 0.00 0.00 0.51 0.60 0.20 0.14 0.00 0.03 1.00 1.00 0.98 0.00 0.99 (A8) 0.73 0.92 0.29 0.92 0.55 0.05 0.00 0.00 0.00 0.00 0.00 1.00 1.00 1.00 0.00 0.00 0.04 0.97 1.00 0.97 0.11 0.99 (A9) 0.89 0.69 0.33 0.69 0.66 0.70 0.00 0.00 0.00 0.00 0.00 0.79 0.95 0.72 0.05 0.00 0.10 0.77 0.92 1.00 0.99 0.96 (C1) C2) (C3) (C4) (C5) (C6) C7) (C8) (C9) (C10) (C11) (C12) (C13) (C14) (C15) (C16) (C17) (C18) (C19) (C20) (C21) (C22) (A1) 12340 0 24600 0 614523 2501 0 300 421.56072 0 0.7755 9306000 304 772000 261000 48660160 0.16 0.02 39 3209 0.07 0.90 (A2) 0 5100 2440 5100 185153 1609 0 300 127.0129 0 0.7755 5540000 155 780000 235000 48660160 0.14 0.01 33 3242 0.06 0.88 (A3) 15620 4125 18580 4125 305668 2498 0 300 209.68962 0 0.7755 8235000 198 550000 243000 48660160 0.18 0.01 0 3242 0.08 0.84 (A4) 73820 17020 41840 17020 251182 1694 0 300 172.30948 0 0.7755 7034000 282 875000 0 43020000 0.07 0.02 15 3257 0.03 0.86
(A5) 11780 0 0 0 131301 755 0 300 90.07866 0 0.7755 4947000 121 305000 248000 43020000 0.00 0.01 143 3400 0.00 0.84
(A6) 0 0 0 0 46587 783 0 300 31.96074 0 0.7755 1541000 0 0 222000 43020000 0.00 0.01 129 3529 0.00 0.00
(A7) 15000 3280 34060 3280 268975 2212 3 600 184.52028 19.64345 1.551 6808000 211 389000 261000 43020000 0.17 0.01 0 3529 0.10 0.90 (A8) 19720 1320 29580 1320 300165 2408 3 600 205.90976 19.64345 1.551 11811000 350 1913000 304000 43020000 0.17 0.01 0 3524 0.09 0.90 (A9) 8020 5200 27860 5200 237027 1287 3 600 162.59572 19.64345 1.551 9650000 333 1370000 290000 43020000 0.16 0.01 11 3535 0.00 0.87
C1-C4 (Kg), C5 (Kwh), C6 (m3), C7 (1000m3), C8 (L), C9-C11 (kWh), C12-(EU), C13(Hrs.), C14-C16 (EU), C17,C18,C20,C21,C21 (%)
Table 3: Correlation between the values
(C1) C2) (C3) (C4) (C5) (C6) C7) (C8) (C9) (C10) (C11) (C12) (C13) (C14) (C15) (C16) (C17) (C18) (C19) (C20) (C21) (C22) (C1) 0 0.14 0.32 0.14 0.87 0.83 1.11 1.11 1.11 1.11 1.11 1.20 1.39 1.20 1.86 0.73 1.10 0.61 1.31 0.71 0.97 1.28 (C2) 0.14 0 0.43 0.00 1.06 0.95 1.10 1.10 1.10 1.10 1.10 1.08 1.30 1.17 1.86 0.87 1.01 0.71 1.42 0.67 1.08 1.26 (C3) 0.32 0.43 0 0.43 0.50 0.41 0.49 0.49 0.49 0.49 0.49 1.66 1.79 1.52 1.32 0.78 0.46 0.79 1.77 1.03 0.57 1.50 (C4) 0.14 0.00 0.43 0 1.06 0.95 1.10 1.10 1.10 1.10 1.10 1.08 1.30 1.17 1.86 0.87 1.01 0.71 1.42 0.67 1.08 1.26 (C5) 0.87 1.06 0.50 1.06 0 0.22 0.96 0.96 0.96 0.96 0.96 1.67 1.67 1.33 0.87 1.52 0.35 0.37 1.50 0.53 0.42 1.55 (C6) 0.83 0.95 0.41 0.95 0.22 0.00 0.77 0.77 0.77 0.77 0.77 1.71 1.62 1.41 0.86 1.48 0.15 0.84 1.79 0.66 0.08 1.55 (C7) 1.11 1.10 0.49 1.10 0.96 0.77 0 0.00 0.00 0.00 0.00 1.55 1.53 1.58 0.53 0.50 0.48 1.51 1.50 1.74 0.72 1.29 (C8) 1.11 1.10 0.49 1.10 0.96 0.77 0.00 0 0.00 0.00 0.00 1.55 1.53 1.58 0.53 0.50 0.48 1.51 1.50 1.74 0.72 1.29 (C9) 1.11 1.10 1.32 1.10 0.96 0.77 0.00 0.00 0.00 0.00 0.00 1.55 1.53 1.58 0.53 0.50 0.48 1.51 1.50 1.74 0.72 1.29 (C10) 1.11 1.10 0.49 1.10 0.96 0.77 0.00 0.00 0.00 0 0.00 1.55 1.53 1.58 0.53 0.50 0.48 1.51 1.50 1.74 0.72 1.29 (C11) 1.11 1.10 0.49 1.10 0.96 0.77 0.00 0.00 0.00 0.00 0 1.55 1.53 1.58 0.53 0.50 0.48 1.51 1.50 1.74 0.72 1.29 (C12) 1.20 1.08 1.66 1.08 1.67 1.71 1.55 1.55 1.55 1.55 1.55 0 0.05 0.14 1.27 0.88 1.78 1.12 0.25 1.04 1.52 0.27 (C13) 1.39 2.17 1.79 1.30 1.67 1.62 1.53 1.53 1.53 1.53 1.53 0.05 0 0.15 1.05 0.99 1.69 1.29 0.25 1.07 1.40 0.26 (C14) 1.20 1.17 1.52 1.17 1.33 1.41 1.58 1.58 1.58 1.58 1.58 0.14 0.15 0.00 1.19 1.09 1.56 0.99 0.39 0.86 1.26 0.48 (C15) 1.86 1.86 1.32 1.86 0.87 0.86 0.53 0.53 0.53 0.53 0.53 1.27 1.05 1.19 0 1.14 0.60 1.44 0.99 1.42 0.77 1.06 (C16) 0.73 0.87 0.78 0.87 1.52 1.48 0.50 0.50 0.50 0.50 0.50 0.88 0.99 1.09 1.14 0 1.44 1.38 0.77 1.79 1.41 0.75 (C17) 1.10 1.01 0.46 1.01 0.35 0.15 0.48 0.48 0.48 0.48 0.48 1.78 1.69 1.56 0.60 1.44 0 1.04 1.89 0.89 0.23 1.62 (C18) 0.61 0.71 0.79 0.71 0.37 0.84 1.51 1.51 1.51 1.51 1.51 1.12 1.29 0.99 1.44 1.38 1.04 0 0.99 0.31 1.08 1.21 (C19) 1.31 1.42 1.77 1.42 1.50 1.79 1.50 1.50 1.50 1.50 1.50 0.25 0.25 0.39 0.99 0.77 1.89 0.99 0 1.13 1.69 0.38 (C20) 0.71 0.67 1.03 0.67 0.53 0.66 1.74 1.74 1.74 1.74 1.74 1.04 1.07 0.86 1.42 1.79 0.89 0.31 1.13 0 0.80 1.35 (C21) 0.97 1.08 0.57 1.08 0.42 0.08 0.72 0.72 0.72 0.72 0.72 1.52 1.40 1.26 0.77 1.41 0.23 1.08 1.69 0.80 0 1.48 (C22) 1.28 1.26 1.50 1.26 1.55 1.55 1.29 1.29 1.29 1.29 1.29 0.27 0.26 0.48 1.06 0.75 1.62 1.21 0.38 1.35 1.48 0
Table 4: Criteria weights 𝑪𝒋 𝒘𝒋 % 𝑪𝒋 (C1) 20.18 6.07 0.03 3.39 (C2) 20.54 6.44 0.04 3.59 (C3) 17.73 6.63 0.04 3.70 (C4) 20.54 6.44 0.04 3.59 (C5) 20.27 5.61 0.03 3.13 (C6) 19.36 7.77 0.04 4.34 (C7) 18.46 9.23 0.05 5.15 (C8) 18.46 9.23 0.05 5.15 (C9) 19.29 9.65 0.05 5.38 (C10) 18.46 9.23 0.05 5.15 (C11) 18.46 9.23 0.05 5.15 (C12) 24.48 7.19 0.04 4.01 (C13) 25.80 8.38 0.05 4.68 (C14) 23.83 7.21 0.04 4.02 (C15) 22.24 6.57 0.04 3.66 (C16) 20.38 10.19 0.06 5.68 (C17) 19.24 8.00 0.04 4.46 (C18) 22.41 10.07 0.06 5.62 (C19) 25.46 9.92 0.06 5.53 (C20) 23.63 10.62 0.06 5.92 (C21) 19.41 7.92 0.04 4.42 (C22) 23.69 7.66 0.04 4.27 Table 5: Normalized decision matrix
(C1) C2) (C3) (C4) (C5) (C6) C7) (C8) (C9) (C10) (C11) (C12) (C13) (C14) (C15) (C16) (C17) (C18) (C19) (C20) (C21) (C22) (A1) 0.01 0.00 0.02 0.00 0.03 0.04 0.00 0.00 0.05 0.00 0.00 0.01 0.01 0.02 0.03 0.00 0.04 0.06 0.02 0.06 0.03 0.00 (A2) 0.00 0.01 0.00 0.01 0.01 0.02 0.00 0.00 0.01 0.00 0.00 0.02 0.03 0.02 0.03 0.00 0.03 0.02 0.01 0.05 0.02 0.00 (A3) 0.01 0.01 0.02 0.01 0.01 0.04 0.00 0.00 0.02 0.00 0.00 0.01 0.02 0.03 0.03 0.00 0.04 0.01 0.00 0.05 0.04 0.00 (A4) 0.03 0.04 0.04 0.04 0.01 0.02 0.00 0.00 0.02 0.00 0.00 0.02 0.01 0.02 0.00 0.06 0.02 0.04 0.01 0.05 0.01 0.00 (A5) 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.03 0.03 0.03 0.03 0.06 0.00 0.01 0.06 0.02 0.00 0.00 (A6) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.05 0.04 0.03 0.06 0.00 0.01 0.05 0.00 0.00 0.04
0.02 0.02 0.04 0.00 0.00 0.00 0.04 0.00
The ranking results of the three different calculations obtained by the integrated CRITIC-VIKOR are given in Table 6 and explained as follows. The 7th alternative (or March 2020) is the most impacted month by the COVID-19, and it can be assigned as the worst manufacturing action and economic situation that the automotive company has experienced since the pandemic outbreak, meanwhile, the 2nd alternative (or August 2020) is the less impacted month and that goes back to the production shut down. Moving to the verification of the model application, the results of both conditions are as follows:
▪ 1st Condition : the acceptable advantage is not satisfied 𝑄(𝐴1) − 𝑄(𝐴2) = -0.054 ≥ 𝐷𝑄 = 0.125.
▪ 2nd Condition: the acceptable stability is satisfied:
The results of the worst to the best ranked in 𝑺𝒊. Table 6: Results of the Integrated Model with v = 0.5
In this research, an integrated approach based on the CRITIC and VIKOR methods has been applied for multi- criteria evaluation of sustainable manufacturing in the Moroccan automotive industry. The CRITIC method has been used to identify objective weights for sustainable criteria. The vital role of this method is to eliminate the subjectivity of the collected data. The VIKOR method has been used to prioritize the alternatives based on the classical utility function. The MCDM approach is very sensitive to any alteration when an alternative or parameter is added to the list of the evaluation.
The obtained results show that the criterion (C20), which refers to the number of employees, holds the heaviest weight. It is noticed that the calculation process has not been influenced by the insertion of additional parameters as it occurs in the case of some other MCDM methods.
An organization has multiple objectives that can be grouped into several categories:
▪ Performance objectives: revenue growth, market share, profitability, liquidity, debt.
▪ The objectives linked to the commercial development of the company: choice of clientele, choice of product range, choice of markets, choice of positioning of the firm on the market.
▪ The objectives linked to the financial structure of the company and its financing: decision regarding the payment of dividends, choice in terms of self- financing, choice in terms of debt policy.
▪ The objectives in terms of research and development and innovation: budget allocated to research and development, choice in terms of the technical quality of products, policy of creating new products.
▪ The social objectives: policy of recruitment, training, qualification, employee participation.
▪ The operational objectives: realization of cost savings, improvement of quality, improvement of productivity and profitability, management of stocks, regularity of deliveries.
Without being exhaustive, a decision-maker must also identify the main objectives of the organization and understand how they apply to him at the local level. It is a way for him to know where to focus his effort. The motivations of manufacturers to become more proactive in their environmental performance are increasingly to reduce costs:
energy material inputs, as well as the costs of waste disposal, have increased in over the past decade as limited resources dwindle.
IV. CONCLUSION
The present work has documented 22 indicators under the environmental (11), economic (5), and social (6) dimensions of sustainability. According to the obtained weights calculated by Critic method, the number of employees and the investments indicators holds the heaviest weights which refer to the main sources of organization expenditures. This analysis reveals that the executive managers cannot place more focus on the environmental indicators and put more effort on sustainably policy-making to reduce the waste especially in this critical financial situation of manufacturing activities in terms of COVID-19. Moreover, the ranking results confirm that the company is getting through the second cycle of the economic crisis that started in September 2020. On an overall basis, the sustainability indicators assessment of an automotive company was valuable especially in the first stages of the project to reveal the weakness and strengths of the adopted policies. Hence, a need remains for developing practical measures and implement adequate decision-makings to step out from this crucial situation without making any undue damages. As research limitation, this work has used a limited set of indicators/alternatives that have been taken from the real-world data rather than literature, and the proposed integrated model has been applied in a sole case institute. For the future research study, the scope of the study will be more open to a wide range of indicators and alternatives as well as taking other plants into account to observe the diversification of the obtained results.
Future research can compare the proposed model with other established approaches and apply it to solve different problems in industry such as 3PL provider evaluation, project selection, location selection, personnel selection.
Distance of PIS
(𝑺𝒊) Ran
g
Distance of NIF
(𝑹𝒊) Ran
g (𝑸𝒊) Ran g
Alternatives
(A1) 0.431 4 0.059 1 0.7462 2
(A2) 0.312 8 0.053 8 0.0325 9
(A3) 0.359 6 0.053 8 0.1163 8
(A4) 0.431 5 0.057 2 0.5448 5
(A5) 0.293 9 0.057 2 0.2998 7
(A6) 0.312 7 0.057 2 0.3337 6
(A7) 0.573 1 0.057 2 0.7998 1
(A8) 0.505 2 0.057 2 0.6781 3
(A9) 0.471 3 0.057 2 0.6164 4
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