# Sustainable Manufacturing Evaluation Based on Enterprise Industry 4.0 Technologies

^{1}

^{2}

^{*}

## Abstract

**:**

_{2}) is an optimal sustainable manufacturer in two ranking methods otherwise, manufacturer 4 (A

_{4}) is the worst sustainable manufacturer. The contribution of this work is to propose a hybrid MCDM with an uncertainty theory of neutrosophic for sustainable manufacturer selection based BDA-BCT with 6R. Sensitivity analyses were carried out to show the decision’s flexibility in various scenarios. Finally, the consequences for management viewpoints were considered.

## 1. Introduction

- A novelty idea in this paper is applying 6R with the integration of BCT and BDA to achieve sustainability. This integration is explained in Section 3.
- A committee of experts is formed to evaluate the most influential criteria of the two integrated methodologies. Criteria are evaluated based on N-BWM to obtain the criteria’s weights are used in N-TOPSIS and N-COPRAS for ranking manufacturers.
- The least and most important criteria required in N-BWM are determined via N-DEMATEL through values of (${\mathrm{R}}_{\mathrm{i}}-{\mathrm{C}}_{\mathrm{j}}$).
- N-TOPSIS is used to evaluate and rank the alternatives of manufacturers who adopt the idea in point one. The most sustainable manufacturer is selected.
- The evaluation and selection of sustainable manufacturer-based N-TOPSIS are compared with another method of MCDM represented in N-COPRAS.

## 2. Historical Insights on Research-Relevant Concepts

## 3. Methodology

#### 3.1. Manufacturing Lifecycle Based on I4.0 Technologies and 6R

**RQ-1.**

**RQ-2.**

**RQ-3.**

**RQ-4.**

**RQ-5.**

#### 3.2. Decision Framework Based on Neutrosophic Theory

**Stage 1: Deciding and evaluating the most effective criteria**

**Step 1:**Form a committee of experts and specialists interested in this domain.**Step 2:**Evaluate the criteria based on a questionnaire approach to collect data. The interview is another approach used by the expert panel to obtain and analyze information for assessing the influence of criteria on achieving sustainable manufacturing.

**Stage 2: Using the subjective MCDM method to determine the weights of criteria**

**Figure 5.**Description of determined criteria of the utilization of 6R-based BCT-BDA based on [29].

**Step 3:**Determine the best (i.e., most important) and worst (i.e., least important) criteria. DEMATEL method united with single-valued triangular of neutrosophic theory as N-DEMATEL is applied as follows:**3.1****3.2**- Produce pairwise comparison matrices based on the relation between criteria by DMs, as shown in matrix ${X}^{ex}$ in Equation (4). Then deneutrosophic these matrices as in Equation (5):$${X}^{ex}=\left(\begin{array}{ccc}{r}_{11}^{ex}& {r}_{12}^{ex}\cdots & {r}_{1n}^{ex}\\ \vdots & \ddots & \vdots \\ {r}_{m1}^{ex}& {r}_{m2}^{ex}\cdots & {r}_{mn}^{ex}\end{array}\right)$$$$\mathrm{s}\left({\mathrm{r}}_{ij}\right)=\frac{\left({l}_{ij}+{m}_{ij}+{u}_{ij}\right)}{9}\ast \left(2+\mathrm{T}-\mathrm{I}-\mathrm{F}\right)$$
**3.3**- Perform aggregation according to Equation (6). Construct a direct relationship for matrix $Z$ as formed in Equation (7).$${x}_{ij}=\frac{{{\displaystyle \sum}}_{ex=1}^{ex}{r}_{ij}}{ex}$$$$Z=\left(\begin{array}{ccc}{x}_{11}& {x}_{12}\cdots & {x}_{1n}\\ \vdots & \ddots & \vdots \\ {x}_{m1}& {x}_{m2}\cdots & {x}_{mn}\end{array}\right).$$
**3.4**- Utilize Equations (8) and (9) to normalize the direct relation matrix $Z$.$$Nor=K\ast X$$$$K=\frac{1}{ma{x}_{1\le i\le n}({{\displaystyle \sum}}_{j=1}^{n}{x}_{ij})}\left(i,j=1,2,\dots ,n\right),$$
**3.5**- Produce total relation matrix T as in Equation (11) by using identity matrix I as formed in Equation (10).$$I=\left(\begin{array}{ccc}1& \cdots & 0\\ \vdots & \ddots & \vdots \\ 0& \cdots & 1\end{array}\right)$$$$T=Nor{\left(I-Nor\right)}^{-1}$$
**3.6**- Determine the most effective criterion with the highest value of (${\mathrm{R}}_{\mathrm{i}}-{\mathrm{C}}_{\mathrm{j}}$). Determine the least important/desired criterion with the least value of (${\mathrm{R}}_{\mathrm{i}}-{\mathrm{C}}_{\mathrm{j}}$) among the eight criteria.

**Step 4:**Determine the judgments and preferences of the expert panel by using Table 2 to evaluate the best ${\mathrm{criterion}}_{B}$ over other ${\mathrm{criteria}}_{j}$. Ref. [41] explained the relation between best to other criteria as $\tilde{criterio{n}_{B}}=\left(\tilde{criterio{n}_{B1}},\dots ,\tilde{criterio{n}_{B8}}\right)$.**Step 5:**Determine the judgments and preferences of the expert panel by using the scale in [41] to evaluate ${\mathrm{criteria}}_{j}$ over least desired/important ${\mathrm{criterion}}_{W}$. Ref. [40] explained the relation between other criteria to ${\mathrm{criterion}}_{W}$ as $\tilde{criterio{n}_{W}}=\left(\tilde{criterio{n}_{1W}},\dots ,\tilde{criterio{n}_{8W}}\right)$.**Step 6:**Perform deneutrosophic process to convert the evaluations and judgments in steps 4 and 5 for the expert panel from NTS to crisp values based on Equation (5). Then, aggregate the judgments of the expert panel according to Equation (6).**Step 7:**Utilize Equations (12) and (13) to find the optimal weights for determining criteria.$$\begin{array}{c}minma{x}_{j}=\left\{\left|\frac{{w}_{B}}{{w}_{j}}-{\mathrm{criteria}}_{Bj}\right|,\left|\frac{{w}_{j}}{{w}_{w}}-{\mathrm{criteria}}_{jw}\right|\right\}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hfill \\ s.t\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hfill \\ {{\displaystyle \sum}}^{}{w}_{j}=1\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hfill \\ {w}_{j}\ge 0forallj\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hfill \end{array}$$$$\begin{array}{c}minma{x}_{j}\mathit{\text{is converted to a linear model as}}:\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hfill \\ min{\epsilon}^{L}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hfill \\ s.t\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hfill \\ \left|{w}_{B}-{\mathrm{criteria}}_{Bj}{w}_{j}\right|\le {\epsilon}^{L},forallj\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hfill \\ \left|{w}_{j}-{\mathrm{criteria}}_{jw}{w}_{w}\right|\le {\epsilon}^{L},forallj\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hfill \\ {{\displaystyle \sum}}^{}{w}_{j}=1\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hfill \\ {w}_{j}\ge 0forallj\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hfill \end{array}$$

**Stage 3: Ranking manufacturers who adopt 6R based on I4.0 technologies for sustainability**

**Step 8:**Form a committee of DMs to perform interviews and questionnaires for evaluating a set of alternatives.**Step 9:**Construct a decision matrix based on the preferences of the committee for alternatives according to NTS in [40].**Step 10:**Repeat procedure 3.2 of step 3 in stage 2 to deneutrosophicate constructed decision matrices in the previous step for alternatives. Also, repeat 3.3 in stage 2 to aggregate deneutrosophicate matrices.**Step 11:**Normalize aggregated decision matrix according to Equation (14).$$Nor{m}_{ij}=\frac{{x}_{ij}}{\sqrt{{{\displaystyle \sum}}_{j=1}^{m}\left({x}^{2}{}_{ij}\right)}}$$**Step 12**: Producing the weighted decision matrix as in Equation (15).$$w{z}_{ij}={w}_{i}\ast Nor{m}_{ij}$$**Step 13:**Compute positive ideal solution and negative ideal solution based on Equations (16) and (17), respectively.$${A}^{*}=\left(w{z}_{1}{}^{*},w{z}_{2}{}^{*},\dots ,w{z}_{n}{}^{*}\right),w{z}_{j}{}^{*}=ma{x}_{i}\left\{w{z}_{ij}\right\}$$$$\overline{A}=\left(w{z}_{1}{}^{-},w{z}_{2}{}^{-},\dots ,w{z}_{n}{}^{-}\right),w{z}_{j}{}^{-}=mi{n}_{i}\left\{w{z}_{ij}\right\}$$**Step 14:**Compute the distance between the positive ideal solution and negative ideal solution to each alternative via Equations (18) and (19) respectively.$${d}_{i}{}^{*}={\displaystyle \sum}_{j=1}^{n}d\left(w{z}_{ij},w{z}_{j}{}^{*}\right)$$$${d}_{i}{}^{-}={\displaystyle \sum}_{j=1}^{n}d\left(w{z}_{ij},w{z}_{j}{}^{-}\right)$$**Step 15:**Determine the ranking and arrangement of alternative by calculating the closeness coefficient (${\mathrm{CC}}_{\mathrm{i}}$) based on Equation (20). The most sustainable alternative/manufacturer has the highest value of ${\mathrm{CC}}_{\mathrm{i}}$.$${\mathrm{CC}}_{\mathrm{i}}=\frac{{d}_{i}{}^{-}}{{d}_{i}{}^{*}+{d}_{i}{}^{-}}$$

**Stage 4: Ranking manufacturers based on N-COPRAS.**

**Step 16:**Repeat Step 8 in Stage 3 for cooperating committee of DMs for evaluating alternatives.**Step 17:**Follow Steps 9 and 10 in Stage 3 to develop an aggregated decision matrix for DMs as in Equations (6) and (7).**Step 18:**Normalize an aggregated decision matrix based on Equation (21).$$\mathrm{NormAgg}={\left[{\mathrm{r}}_{\mathrm{ij}}\right]}_{\mathrm{m}\times \mathrm{n}}=\frac{{\mathrm{h}}_{\mathrm{ij}}}{{{\displaystyle \sum}}_{\mathrm{i}=1}^{\mathrm{m}}{\mathrm{h}}_{\mathrm{ij}}}$$**Step 19:**Produce the weighted decision matrix ($wz$) as in Equation (15). Sum of weighted decision matrix calculated according to Equations (22) and (23).$${\mathrm{S}}_{+\mathrm{i}}={\displaystyle \sum}_{\mathrm{j}=1}^{\mathrm{n}}w{z}_{+\mathrm{ij}},\mathrm{for}\mathrm{beneficial}\mathrm{criteria}$$$${\mathrm{S}}_{-\mathrm{i}}={\displaystyle \sum}_{\mathrm{j}=1}^{\mathrm{n}}w{z}_{-\mathrm{ij}},\mathrm{for}\mathrm{nonbeneficial}\mathrm{criteria}$$**Step 20:**Utilize Equation (24) to determine the relative importance of alternatives. Calculate quantity utility ${\mathrm{U}}_{\mathrm{i}}$ for each alternative based on Equation (25) to rank the alternatives.$${\mathrm{Q}}_{\mathrm{i}}={\mathrm{s}}_{+\mathrm{i}}+\frac{{\mathrm{s}}_{-\mathrm{min}}{{\displaystyle \sum}}_{\mathrm{i}=1}^{\mathrm{m}}{\mathrm{s}}_{-\mathrm{i}}}{{\mathrm{s}}_{-\mathrm{i}}{{\displaystyle \sum}}_{\mathrm{i}=1}^{\mathrm{m}}({\mathrm{s}}_{-\mathrm{m}/}{\mathrm{s}}_{-\mathrm{i}})}$$$${\mathrm{U}}_{\mathrm{i}}=\left[\frac{{\mathrm{Q}}_{\mathrm{i}}}{{\mathrm{Q}}_{\mathrm{max}}}\right]\times 100\%,$$

## 4. Validation of Hybrid Mathematical Approach: An Empirical Case Study

_{1}) is a home appliances manufacturer (i.e., electrical equipment). Manufacturer two (A

_{2}) is a ceramic manufacturer operating for more than 20 years. Manufacturer three (A

_{3}) and manufacturer four (A

_{4}) are textile manufacturers who have a strong long-term commitment to enhancing management techniques for eco-friendly manufacturing, in addition to optimizing the operations through saving usage resources, energy, and finances to support manufacturers to be more competitive in the marketplace.

**First, deciding and evaluating the most effective criteria**

**Step 1:**Determine the most effective criteria related to the proposed framework.**Step 2:**The panel of experts expresses their opinions and preferences for the decided criteria. Their expressions are performed based on NTS, as shown in [40].

**Second, utilizing the subjective method of MCDM for valuing criteria’s weights**

**Step 3:**Apply the N-DEMATEL to compute the relation between criteria as:**3.1**- Build the comparison matrix
**3.2**- Deneutrosophicate a pairwise comparison matrix for criteria using Equation (5).
**3.3**- Aggregate the pairwise comparison into one matrix to obtain the direct relation matrix by Equations (6) and (7) as in Table 4.
**3.4**- Obtain the sum of each row, and the maximum value for the sum of each row is 33.3. Next, utilize Equations (8) and (9) to normalize the direct relation.
**3.5**- Produce the total relation matrix according to Equation (11).
**3.6**- C
_{6}is considered the most effective or most important criterion, and C_{1}is the least important or least effective for BWM.Criteria C _{1}C _{2}C _{3}C _{4}C _{5}C _{6}C _{7}C _{8}C _{1}0.50 4.82 5.56 5.18 4.30 5.98 2.88 4.08 C _{2}0.22 0.50 5.77 5.56 5.84 6.30 2.00 3.30 C _{3}0.19 0.18 0.50 4.97 5.56 5.03 5.98 5.56 C _{4}0.20 0.19 0.21 0.50 5.03 5.98 4.16 4.16 C _{5}0.23 0.18 0.19 0.21 0.50 6.51 6.93 6.93 C _{6}0.18 0.16 0.21 0.18 0.15 0.50 0.85 0.85 C _{7}0.83 0.86 0.18 0.24 0.14 1.18 0.50 4.27 C _{8}0.24 0.42 0.19 0.24 0.14 1.18 0.31 0.50

**Step 4**: Determine the judgments and preferences of the expert panel using the scale in [41] to evaluate C_{6}over other criteria.**Step 5**: Determine the judgments and preferences of the expert panel using the scale in [41] to evaluate criteria over C_{1}.**Step 6**: Convert the evaluations and judgments in steps 4 and 5 for the expert panel from NTS to crisp values as in procedures 3.2 and 3.3 in the previous step.**Step 7**: Find the optimal weights for determined criteria-based Equation (12) on Equation (13). Figure 6 show that C_{6}has the largest weight ${w}_{1}=0.103274226,{w}_{2}=0.076431404,{w}_{3}=0.0558155,{w}_{4}=0.0964963,{w}_{5}=0.049269274,{w}_{6}=0.310396423,{w}_{7}=0.153813887,{w}_{8}=0.154502948$.

**Third, the ranking of stages for sustainable manufacturers based on N-TOPSIS**

**Step 8**: Repeat step 2 for evaluating four alternatives of manufacturers in this case study.**Step 9**: Build the decision matrices by the opinions of experts. Convert decision matrices constructed in the previous step for alternatives into crisp values through the deneutrosophic process by employing Equation (5), as represented in**Step 10**. Aggregate the matrix deconstruct according to Equations (6) and (7), as shown in Table 5.**Step 11**: Utilize Equation (14) to normalize aggregated matrix.**Step 12**: Produce the weighted decision matrix, by employing the weights obtained from N-BWM to obtain ${w}_{i}$ in Equation (15) with the normalized matrix.**Step 13**: Compute positive ideal solution and negative ideal solution as in Table 6.**Step 14**: Compute the distance between the positive ideal solution and negative ideal solution for each alternative via Equations (18) and (19). This step helps obtain the value of (${\mathrm{CC}}_{\mathrm{i}}$) based on Equation (20), whereas ${\mathrm{CC}}_{\mathrm{i}}$ values determine and rank alternatives from the most appropriate (A_{2}) to the least appropriate (A_{4}) as Table 6.

**Fourth, the ranking method stage-based on N-COPRAS.**

**Step 15–16**: Start with the aggregated decision matrix in Table 5.**Step 17**: Normalized an aggregated matrix based on Equation (21)**Step 18**: Follow Step 13 in Stage three to derive the weights based on N-BWM. These weights contribute to producing the weighted decision matrix through Equation (15).**Step 19**: Determine the sum of the weighted normalized decision matrix. In this paper, eight identified criteria are considered beneficial. Thus, Equation (22) is applied to find the values of ${\mathrm{S}}_{+\mathrm{i}}$. The value of ${\mathrm{S}}_{-\mathrm{i}}$ is zero. Subsequently, the value of S-min/S-i is zero, where S-min is zero. So, the relative importance of alternatives $\left({\mathrm{Q}}_{\mathrm{i}}\right)$ based on Equation (24), ${\mathrm{Q}}_{1}$ = 0.282470383$.{\mathrm{Q}}_{2}$ = 0.284099748$.{\mathrm{Q}}_{3}$ = 0.235788938$,{\mathrm{Q}}_{4}$ = 0.197640932.

**Step 20**: Calculating quantitative utility $({\mathrm{U}}_{\mathrm{i}})$ based on Equation (25). The values of ${\mathrm{U}}_{\mathrm{i}}$ clarify that A_{2}is the most appropriate manufacturer, and A_{4}has the least rank.

## 5. Implications of Results

_{2}> A

_{1}> A

_{3}> A

_{4}. Moreover, Figure 7 shows manufacturers’ ${\mathrm{CC}}_{\mathrm{i}}$ values, which present that A

_{2}is the most appropriate, sustainable manufacturer, and A

_{4}is the least sustainable one.

_{2}is the most appropriate and sustainable manufacturer and A

_{4}is the least appropriate and sustainable manufacturer.

## 6. Discussion

#### 6.1. Sensitivity Analysis

_{1}is the best alternative and A

_{4}is the worst alternative. But in case 5 the best alternative is A

_{2}and the worst alternatives is A

_{4}. Figure 9 shows the rank of N_TOPSIS under five cases in changes of weights.

#### 6.2. Comparative Analysis

_{2}is the best alternative. But in the worst alternative, we find two proposed methods and Bipolar TOPSIS accepted. A

_{4}is the worst alternative. Bipolar VIKOR has A

_{1}as the worst alternative. Bipolar EDAS has A

_{3}as the worst alternative. Table 9 shows the Spearman’s rank correlation between methods. From Table 8, we find Proposed TOPSIS and COPRA as highly correlated. The correlation between proposed TOPSIS, Bipolar TOPSIS, and Bipolar EDAS is high but the Bipolar VIKOR is the smallest.

#### 6.3. Limitations

## 7. Managerial Implications

- The study highlights the possible results of sustainability-focused I4.0 projects which can persuade managers and decision-makers to consider implementing sustainable I4.0 programs due to their economic and socio-environmental benefits. While the potential impacts of certain I4.0 technologies (such as IoT, automation, Cloud computing, BC, and BDA) on green practices and manufacturing are well-known in [41]. The integrative framework proposed by the authors enables managers to calibrate their involvement and assess their willingness to engage in terms of sustainability and the potential effects of such initiatives. The study assists firms in determining their readiness to incorporate sustainability into their I4.0 deployment.
- The findings emphasize the critical significance of management support and dedication in adopting I4.0 in a sustainable manner which is another managerial contribution of this work. Management should be prepared to build an environment conducive to incorporating sustainable practice within I4.0 implementation (including proper investments, staff involvement, resource deployment, and governance). Thus, firms may be able to expand the breadth and impact of externalities generated by sustainability-focused I4.0 efforts.
- Numerous experts have stated that employees are frequently hesitant to participate in I4.0 activities and perceive smart technologies as a threat. The findings of this study emphasize the critical role of management in resolving employee problems. Indeed, managers’ active participation in educating, informing, and elevating employees’ understanding of I4.0’s positive social externalities might assuage numerous of their reservations about such efforts. Managers’ support for such measures would increase staff engagement and success with a sustainability-focused I4.0 implementation.

## 8. Conclusions

#### 8.1. Theoretical Contributions

- C
_{6}is the most beneficial criterion and C_{1}is the least benefit-based N-DEMATEL to use these criteria in N-BWM to obtain the final weights for eight determined criteria, as shown in Figure 6. - The sustainability performances of four alternatives are evaluated and ranked in this case study through DMs.

#### 8.2. Future Direction

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

CE | Circular Economy |

I4.0 | Industry 4.0 |

CP | Cleaner Production |

BCT | Blockchain Technology |

BDA | Big Data Analytical |

MCDM | Multiple-criteria decision-making |

BWM | best worst method |

DEMATEL | Decision Making trial and evaluation laboratory |

TOPSIS | Technique for order of preference by similarity to ideal solution |

COPRAS | Complex PRoportional ASsessment |

TBL | Triple Bottom Line |

N-DEMATEL | Neutrosophic-Decision Making trial and evaluation laboratory |

N-BWM | Neutrosophic-best worst method |

N-TOPSIS | Neutrosophic-Technique for order of preference by similarity to ideal solution |

N-COPRAS | Neutrosophic-Complex PRoportional ASsessment |

DMs | Decision Makers |

NTS | Neutrosohic Triangular Scale |

SSCM | sustainable supply chain management |

NOC-TOPSIS | Non-orthogonal coordinates based TOPSIS |

MEREC | MEthod based on the Removal Effects of Criteria |

VIKOR | VIekriterijumsko KOmpromisno Rangiranje |

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**Figure 3.**The architecture of the manufacturing lifecycle adopted from [28].

Ref# | Methodology | Handling Uncertainty |
---|---|---|

Jamwal et al. [14] | The authors performed literature about applying various MCDM in sustainable supply chain management (SSCM) for analysis of barriers, challenges, drivers, enablers, criteria, outcomes, and practices of SSCM. | The literature observed that there are missing and limited opportunities in integrating optimization and simulation techniques with MCDM to handle imprecise. |

Lin et al. [15] | TOPSIS was refined, and the so-called Non-orthogonal coordinates based TOPSIS (NOC-TOPSIS) was created for prioritizing industrial systems after a life cycle sustainability assessment. | Uncertainty problems could not be discussed in assessing sustainability for industrial systems |

Keshavarz-Ghorabaee et al. [16] | Applied MEthod based on the Removal Effects of Criteria (MEREC) is a method of MCDM to determine the weights of criteria. | The scholars focus on a method of calculating weights of criteria, not covering the problem of ambiguity. |

Büyüközkan et al. [18] | proposed a decision-making method for evaluating BC as an enterprise platform solution for businesses. The evaluation process performed based on VIKOR | any imprecise or vagueness information leads to incorrect judgments. |

Criteria | I4.0 Technologies in This Study | Pillars of Sustainability Achieved Based 6R and I4.0 Technologies | ||||
---|---|---|---|---|---|---|

IIoT | BCT | BDA | CE | CP | Social | |

C_{1} | √ | √ | √ | √ | ||

C_{2} | √ | √ | √ | √ | ||

C_{3} | √ | √ | √ | √ | √ | |

C_{4} | √ | √ | √ | √ | ||

C_{5} | √ | √ | √ | √ | ||

C_{6} | √ | √ | √ | √ | ||

C_{7} | √ | √ | √ | √ | √ | |

C_{8} | √ | √ | √ | √ | √ |

DMs | Department | Years of Experience | Qualification | Specialization |
---|---|---|---|---|

DM_{1} | Sales and purchasing | 15 | B. Sc | Textile Manufacturing |

DM_{2} | Planning and production | 20 | MBA | Electronic Manufacturing |

DM_{3} | Product quality control | 18 | B. Sc | Ceramic Manufacturing |

DM4 | Information Technology | 12 | Professional Diploma | Textile Manufacturing |

Criteria | C_{1} | C_{2} | C_{3} | C_{4} | C_{5} | C_{6} | C_{7} | C_{8} |
---|---|---|---|---|---|---|---|---|

A_{1} | 3.744444 | 4.344444 | 4.666667 | 3.788889 | 1.9 | 2.316667 | 6.933333 | 5.844444 |

A_{2} | 2.666667 | 4.577778 | 3.955556 | 0.822222 | 2.983333 | 6.722222 | 5.633333 | 1.9 |

A_{3} | 3.166667 | 1.872222 | 4.894444 | 4.344444 | 6.722222 | 4.027778 | 0.794444 | 3.15 |

A_{4} | 3.927778 | 5.844444 | 1.111111 | 3.194444 | 4.027778 | 1.944444 | 4.133333 | 1.8 |

Alternatives | d* | d^{−} | CC_{i} | Rank |
---|---|---|---|---|

A_{1} | 0.165864 | 0.14217 | 0.46154 | 2 |

A_{2} | 0.106345 | 0.194545 | 0.646565 | 1 |

A_{3} | 0.15421 | 0.104559 | 0.404063 | 3 |

A_{4} | 0.204862 | 0.074798 | 0.26746 | 4 |

Case 1 | Case 2 | Case 3 | Case 4 | Case 5 | |
---|---|---|---|---|---|

C_{1} | 0.125 | 0.2 | 0.1 | 0.1 | 0.1 |

C_{2} | 0.125 | 0.1 | 0.2 | 0.1 | 0.1 |

C_{3} | 0.125 | 0.1 | 0.2 | 0.2 | 0.1 |

C_{4} | 0.125 | 0.1 | 0.1 | 0.2 | 0.1 |

C_{5} | 0.125 | 0.1 | 0.1 | 0.1 | 0.1 |

C_{6} | 0.125 | 0.1 | 0.1 | 0.1 | 0.2 |

C_{7} | 0.125 | 0.1 | 0.1 | 0.1 | 0.2 |

C_{8} | 0.125 | 0.2 | 0.1 | 0.1 | 0.1 |

Proposed Ranking Methods | A_{1} | A_{2} | A_{3} | A_{4} |
---|---|---|---|---|

Proposed TOPSIS | 2 | 1 | 3 | 4 |

Proposed COPRA | 2 | 1 | 3 | 4 |

Bipolar TOPSIS | 2 | 1 | 3 | 4 |

Bipolar VIKOR | 4 | 1 | 2 | 3 |

Bipolar EDAS | 2 | 1 | 4 | 3 |

Proposed TOPSIS | Proposed COPRA | Bipolar TOPSIS | Bipolar VIKOR | Bipolar EDAS | |
---|---|---|---|---|---|

Proposed TOPSIS | - | 1 | 1 | 0.4 | 0.8 |

Proposed COPRAS | 1 | - | 1 | 0.4 | 0.8 |

Bipolar TOPSIS | - | 0.4 | 0.8 | ||

Bipolar VIKOR | - | 0.2 | |||

Bipolar EDAS | - |

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**MDPI and ACS Style**

Eldrandaly, K.A.; El Saber, N.; Mohamed, M.; Abdel-Basset, M.
Sustainable Manufacturing Evaluation Based on Enterprise Industry 4.0 Technologies. *Sustainability* **2022**, *14*, 7376.
https://doi.org/10.3390/su14127376

**AMA Style**

Eldrandaly KA, El Saber N, Mohamed M, Abdel-Basset M.
Sustainable Manufacturing Evaluation Based on Enterprise Industry 4.0 Technologies. *Sustainability*. 2022; 14(12):7376.
https://doi.org/10.3390/su14127376

**Chicago/Turabian Style**

Eldrandaly, Khalid A., Nissreen El Saber, Mona Mohamed, and Mohamed Abdel-Basset.
2022. "Sustainable Manufacturing Evaluation Based on Enterprise Industry 4.0 Technologies" *Sustainability* 14, no. 12: 7376.
https://doi.org/10.3390/su14127376