DE202023106385U1 - A system for designing the structural transformation of a fuzzy analysis hierarchy-based supply chain - Google Patents

Abstract

A system (100) for designing a structural transformation of a fuzzy analysis hierarchy based supply chain, the system (100) comprising:an input unit (102) for collecting responses from multiple raters as input to perform a pairwise comparison to perform rankings based on several factors and stored in a database (102a), the answers being generated by a user device (102b); a fuzzy logic-based processing unit (104) connected to the input unit (102) in order to perform the to process collected input responses to calculate fuzzy weights based on a kappa value, the fuzzy logic based processing unit comprising:a comparator (106) for receiving a plurality of fuzzy logic criteria for comparison based on the responses, wherein the answers are compared based on a fuzzy triangle scale when sorting in either ascending or descending order; anda computing unit (108) connected to the comparator (106) for calculating a plurality of values such as the kappa value, a geometric mean and a fuzzy weight; Anda defuzzification-based processing unit (110) associated with the fuzzy logic-based processing unit (104) for defuzzification of the calculated weights.

Description

FIELD OF THE INVENTION

The present invention relates to the field of fuzzy logic based supply chain systems. In particular, the present subject matter relates to a system for designing a structural transformation of a fuzzy analysis and hierarchy-based supply chain to address the challenges faced by Covid-19.

BACKGROUND OF THE INVENTION

The Covid-19 disruption led to a change in markets, interaction and transactions between business-to-business (B2B) buyers and sellers. In recent years, Covid-19 has disrupted supply chains and caused huge losses to the economy and the lives of citizens. On the supply chain side, vendors play a crucial role in moving the supply chain before it reaches the customer. Purchasing is a core function of an integrated supply chain and ensures a reliable supply of materials. The efficiency of supply is directly proportional to the capabilities of the supplier, from the information technologies used to the operational capabilities. Therefore, the selection and management of suppliers becomes crucial to a company's purchasing and sourcing strategy. Suppliers are the first and crucial layer that helps shape supply chain strategies as they directly support purchasing managers in cost savings and quality. The function of purchasing is to identify, evaluate, select and keep track of them for their well-being and to develop them into diverse skills. The purchasing function is responsible for striking a balance between supply risk and its impact on an organization's financial performance. For routine and regular products, purchasing managers decide on systematic contract processing due to different products and highly complex logistics activities.

Deliveries must ensure a secure supply chain and easily assessable alternatives. Procurement professionals must be careful when reviewing supplier proposals to ensure minimal supply chain risk while ensuring they have a high impact on financial performance during times of uncertainty such as Covid-19. A performance-oriented partnership with suppliers is recommended for strategic products. In addition to the typical selection criteria for suppliers, other parameters are also crucial for the purchasing function. These parameters include previous contact, provision of other raw materials, evaluation based on a price offer and the financial position of the supplier. In addition, reference checks, visits to suppliers, audits and testing of their processes are carried out to complete the supplier selection process by the purchasing function.

Traditionally, purchasing uses numerous supplier selection methods, ranging from cost ratio to linear average. Compared to traditional settings, sustainability, transparency and reliability are more important in Covid-19-like situations. In the past, companies also used knowledge-based sourcing to select and source from suppliers.

In addition to raw materials, there are other suppliers that support a company's production, such as insurance providers for critical equipment in the production facility. Due to the fact that sixty to seventy percent of the product costs incurred are raw materials, purchasing is of strategic importance. Furthermore, the purchasing function's strategic decisions impact a company's goals and even become critical when operating under uncertainty. Purchasing managers need close coordination with other departments to successfully integrate stakeholders, including suppliers, into the company's vision.

In the past, researchers have reviewed and analyzed provider selection criteria and methods. In recent decades, technological performance has also developed into a decisive criterion for supplier selection.

For example, the hybrid AHP, a hierarchical fuzzy technique for the ordering of preferences by similarity to the ideal solution (TOPSIS), the Taguchi loss function, an interval-valued pairwise comparison in FAHP, linguistic fuzzy expression, weighted max-min fuzzy -decision model, and the integration of multiple techniques. Furthermore, AHP allows for inconsistencies in pairwise assessment.

However, most studies are limited to examining supplier identification and evaluation in the traditional environment, leading to a gap in developing and specifying an approach suitable for supplier selection under uncertainty situations such as Covid-19. Furthermore, there is a lack of literature on managerial decision making, such as: B. supplier selection, which affects an uncertain group of people and situations.

Therefore, in order to overcome the above limitations, there is a need to develop a system for designing the structural transformation of a supply chain based on fuzzy analysis and hierarchy.

The technical advances disclosed by the present invention overcome the limitations and disadvantages of existing and conventional systems and methods.

SUMMARY OF THE INVENTION

The present invention relates generally to a system for developing a structural transformation of a fuzzy analysis hierarchy.

An object of the present invention is to provide a system for developing a structural transformation of the fuzzy analytical hierarchy process.

Another objective of the present invention is to ensure reliable responses from respondents by using pairwise comparisons and weighting.

Another object of the present invention is to facilitate information management, decision making development, or machine-driven decisions under uncertain conditions.

Another object of the present invention is to provide a reliable process by offering purchasing managers management implications to consider multiple factors and responses regarding the vendor's performance for their evaluation and similar situations where more than one evaluator must be considered and to combine.

A system for designing a structural transformation of a fuzzy analysis hierarchy based supply chain, the system comprising: an input unit for collecting responses from multiple evaluators as input to perform a pairwise comparison for performing rankings based on multiple factors, and in to store a database; a fuzzy logic-based processing unit connected to the input unit for processing the collected input responses to calculate fuzzy weights based on a kappa value, the fuzzy logic-based processing unit comprising: a comparator for receiving a plurality of fuzzy logic criteria for comparison based on the answers, wherein the answers are compared based on a fuzzy triangle scale after sorting in either ascending or descending order; a computing unit connected to the comparator for calculating a variety of values such as the kappa value, a geometric mean and a fuzzy weight, where if the kappa value (k) < 0.21, then a fuzzy analytic hierarchy analysis for assigning fuzzy weightings are carried out, otherwise an agreement is made to compare the fuzzy logic criteria, whereby the fuzzy weights are calculated based on a Saaty scale and the weights are recorded; and a defuzzification-based processing unit connected to the fuzzy logic-based processing unit for defuzzifying the calculated weights by an area center technique, wherein the defuzzified weights are normalized for each fuzzy logic criterion.

In order to further illustrate the advantages and features of the present invention, a more detailed description of the invention will be made with reference to specific embodiments thereof shown in the accompanying drawings. It is understood that this drawing represents only typical embodiments of the invention and is therefore not to be viewed as limiting its scope. The invention is described and explained in more detail and in more detail with reference to the accompanying drawing.

BRIEF DESCRIPTION OF THE FIGURE

• 1 zeigt ein Blockdiagramm eines Systems zur Entwicklung der Strukturtransformation des Fuzzy Analytical Hierarchy Process (FAHP).

These and other features, aspects and advantages of the present invention will be better understood when the following detailed description is read with reference to the accompanying drawings, in which like reference numerals represent like parts, in which:

• 1 shows a block diagram of a system for developing the structural transformation of the Fuzzy Analytical Hierarchy Process (FAHP).

Additionally, experienced craftsmen will recognize that elements in the drawing are shown for convenience and may not necessarily have been drawn to scale. For example, the flowcharts illustrate the method through key steps that help improve understanding of aspects of the present disclosure. Additionally, in view of the construction of the device, one or more components of the device may have been represented in the drawing by conventional symbols, and the drawing may show only the specific details relevant to understanding the embodiments of the present disclosure around the drawing not to be obscured by details that would be readily apparent to one of ordinary skill in the art who would benefit from the description herein.

DETAILED DESCRIPTION:

In order to promote understanding of the principles of the invention, reference will now be made to the embodiment shown in the drawings and specific language will be used to describe the same. It is to be understood, however, that this is not intended to limit the scope of the invention, since changes and further modifications to the system illustrated and further applications of the principles of the invention set forth therein are contemplated as would normally occur to one skilled in the art Technology to which the invention relates.

It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and illustrative of the invention and are not intended to limit the same.

References in this specification to “an aspect,” “another aspect,” or similar language mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention is included. Therefore, the phrases “in one embodiment,” “in another embodiment,” and similar phrases in this specification may, but not necessarily, refer to the same embodiment.

The terms "includes", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, such that a process or method that includes a list of steps not only includes those steps, but may include other steps not expressly listed or are inherent in that process or method. Likewise, one or more devices or subsystems or elements or structures or components prefixed with "comprises...a" do not exclude, without further limitation, the existence of other devices or other subsystems or other elements or other structures from other components or additional devices or additional subsystems or additional elements or additional structures or additional components.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as generally understood by one of ordinary skill in the art to which this invention pertains. The system, methods and examples provided herein are for illustrative purposes only and are not intended to be limiting.

Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

1 shows a block diagram of a system (100) for designing a structural transformation of a fuzzy analysis hierarchy-based supply chain, the system (100) comprising: an input device (102), a database (102a), and a user device (102b), one on Fuzzy logic-based processing unit (104), a comparator (106), a computing unit (108), a defuzzification-based processing unit (110) and a normalization unit (112).

The input unit (102) collects responses from multiple raters as input to perform a pairwise comparison to perform rankings based on multiple factors and stores them in a database (102a), the responses being generated by a user device (102b). The fuzzy logic based processing unit (104) is connected to the input unit (102) for processing the collected input responses to calculate fuzzy weights based on a kappa value, the fuzzy logic based processing unit comprising: a comparator (106). ) for receiving a plurality of fuzzy logic criteria for comparison based on the answers, comparing the answers based on a fuzzy triangle scale after sorting in either ascending or descending order; and a computing unit (108) is connected to the comparator (106) to calculate a plurality of values such as the kappa value, a geometric mean and a fuzzy weight, where if the kappa value (k) < 0.21, a Fuzzy value is present For the assignment of fuzzy weights, an analytical hierarchy analysis is carried out, otherwise an agreement is made to compare the fuzzy logic criteria, where the fuzzy weights are calculated based on a Saaty scale and the weights are recorded. The fuzzy logic-based processing unit (110) is connected to the fuzzy logic-based processing unit (104) for defuzzifying the calculated weights by a center-of-area technique, the defuzzified weights being defuzzified by a normalization unit (112). every fuzzy logic criterion can be normalized.

In one embodiment, the computing unit (108) uses a Fleiss Kappa technique to determine the type of agreement of the respondents to the comparison of the pairs, which indicates the value of K 0.21 ≥.

In one embodiment, the variety of factors used to perform rankings includes: quality of delivery based on rejection rate, supplier location, unit price offered, the delivery time from the time the order is placed to the time it finally arrives, and thereafter - Sales services such as collection and replacement of the rejected materials.

In one embodiment, the comparison unit (106) compares the linguistic variable-based criteria using a triangular membership function, wherein when a respondent assigns a fuzzy triangular scale for comparison, a contribution matrix is formed by forming the reciprocal of the triangular scale in reverse order as corresponding fuzzy value.

In one embodiment, if K < 0.21, a reassignment of weights is performed by the respondents.

In one embodiment, a normalization unit (112) is connected to the defuzzification-based processing unit (110) to perform normalization of the defuzzification weights for each of the plurality of factors.

In one embodiment, the computing unit (108) calculates the vector sum and the inverse power of each summation vector to replace the fuzzy triangular number by arranging it in increasing order.

In one embodiment, the user device (102b) is either a smartphone or a tablet or a computer or a laptop with a keyboard and a display.

In one embodiment, the criteria or alternatives are compared based on linguistic variables. If the respondent assigns a fuzzy triangle scale (4, 5, 6) to the given comparison, the contribution matrix of this pair takes (1/6, 1/5, 1/4) as the fuzzy value. If the number of respondents is more than one, the average of the weights assigned by the multiple respondents is calculated. The geometric mean of the fuzzy comparison values of each criterion is calculated as shown in the following equation (ibid.): $${\tilde{r}}_i={\left({\displaystyle \prod _{j=1}^n{\tilde{d}}_{ij}}\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$n$}\right.}$$

For i=1, 2... n.

Vector summation is performed and the inverse power of each summation vector is calculated and the fuzzy triangular number is replaced by arranging them in increasing order. The fuzzy weights are calculated using the following equation: $${\tilde{w}}_i={\tilde{r}}_i\otimes {\left({\tilde{r}}_1\oplus {\tilde{r}}_2\oplus \dots \oplus {\tilde{r}}_n\right)}^{-1}$$

The fuzzy weights are defuzzified using the center-of-area method, followed by normalization of the values.

• Schritt 1: Ersetzen Sie die Waage durch die Saaty- Waage und notieren Sie die von den mehreren Befragten zugewiesenen Gewichte.
• Schritt 2: Wenden Sie die Kappa-Technik von Fleiss an, um die Art der Zustimmung der Befragten zum Vergleich der Paare (Items) zu ermitteln. Diese besagt, dass die Zustimmung der Bewerter mindestens auf dem angemessenen Niveau von K = 0.21 und höher liegen sollte. Für K < 0.21 wird eine Neuzuweisung der Gewichte durch die Befragten erwartet. Dieser Schritt wird unter Berücksichtigung der Tatsache vorgeschlagen, dass der Durchschnittswert der Antworten mehrerer Befragten die Reaktionen möglicherweise nicht angemessen widerspiegelt.
• Schritt 3: Betrachten Sie die n Antworten als Fuzzy-Gewichte. Wiederholen Sie Schritt 3, um zu den normalisierten Bewertungen zu gelangen.

In one embodiment, the suggestive structure transformation in FAHP is performed by the fuzzy logic-based processing unit in multiple steps, including:

• Step 1: Replace the scale with the Saaty scale and record the weights assigned by the multiple respondents.
• Step 2: Apply Fleiss's Kappa technique to determine the type of agreement respondents have to compare the pairs (items). This states that the agreement of the raters should be at least at the appropriate level of K = 0.21 and higher. For K < 0.21, the respondents are expected to reassign the weights. This step is proposed taking into account the fact that the average of responses from multiple respondents may not adequately reflect responses.
• Step 3: Consider the n responses as fuzzy weights. Repeat step 3 to get to the normalized scores.

In an exemplary embodiment, the organization has considered three suppliers for evaluation because they are the predominant suppliers of a particular material category. The procurement department of the organization under consideration consisted of several appraisers. Table 1 was interpreted so that for the item pair numbered one, two respondents rated the pair's importance as one and six respondents rated the pair as 3. A similar interpretation is expected for the remaining element pairs. Items/Pairs Categories 1 2 3 4 5 6 7 8th 9 Quality location 2 0 6 0 0 0 0 0 0 Quality costs 0 0 0 0 0 0 4 0 4 Quality delivery 0 0 0 0 0 1 4 3 0 Quality after-sales service 0 0 0 0 4 2 1 0 0 Location costs 0 0 0 0 4 3 1 0 0 Location delivery 0 0 0 0 0 0 1 3 4 Location after-sales service 0 0 0 0 0 2 4 2 0 Shipping 1 2 5 0 0 0 0 0 0 Cost-effective after-sales service 0 0 4 2 2 0 0 0 0 Delivery-After-Sales Service1 0 0 0 0 0 1 6 0 1 Table 2: represents GM of fuzzy comparison values 1 2 3 4 5 6 7 8th Quality 2.93 3.5 3.99 3.94 3.09 3.74 3.52 3.88 Location 2.17 2.41 3.29 3.38 3.12 3.38 2.81 2.99 Cost 0.43 0.51 0.46 0.51 0.55 0.45 0.58 0.53 Delivery 0.37 0.41 0.39 0.37 0.39 0.4 0.41 0.49 Customer service 0.52 0.53 0.56 0.46 0.52 0.52 0.56 0.64 Σ 6.42 7.36 8.69 8.65 7.67 8.49 7.89 8.53 Σ ^-1 0.16 0.14 0.12 0.12 0.13 0.12 0.13 0.12 Σ ^-1 < 0.12 0.12 0.12 0.12 0.13 0.13 0.14 0.16 Table 3 Calculate the fuzzy weights of each criterion, its average and the normalized weights Weights Average N* Quality 0.3 4 0.4 0.4 7 0.4 6 0.3 9 0.4 9 0.4 8 0.6 1 0.45 0.4 5 Location 0.2 5 0.2 8 0.3 9 0.4 0.4 4 0.3 8 0.4 7 0.4 7 0.37 0.3 7 Cost 0.0 5 0.0 6 0.0 5 0.0 6 0.0 7 0.0 6 0.0 8 0.0 8 0.06 0.0 6 Delivery 0.0 4 0.0 5 0.0 5 0.0 4 0.0 5 0.0 5 0.0 6 0.0 8 0.05 0.0 5 Customer service 0.0 6 0.0 6 0.0 7 0.0 5 0.0 7 0.0 7 0.0 8 0.1 0.07 0.0 7

Implementing a FAHP-based supplier evaluation methodology leads to interesting implications and comments. Its strength lies in the aggregation of information through hierarchy and decomposition and thus fulfills criterion 1. The proposed FAHP-based model uses data efficiently (criterion 2) and enables a qualitative assessment of parameters (criterion 3). The structural transformation model attempts to achieve fair participation among members of a group by ensuring interaction between researcher and respondent. All judgments will depend on the views of multiple defendants and this is subject to agreement between them. In addition, by considering responses as fuzzy, using the aggregation method can significantly control the variance of the weights assigned to each criteria. It can also facilitate machine-driven decision making.

The flexibility of the model set out in criterion 5 is reinforced by the fact that the supplier selection can be adapted to the requirements of a specific criterion. Regarding the 6th and 7th criteria of usefulness and acceptability, it is noted that the final evaluation may not be so useful and companies may refuse to use such tools because these tools may not meet their needs.

Organizations that need to implement supplier ranking and selection problems can opt for qualitative methods based on expert opinion.

The proposed system is useful and acceptable due to its flexibility in adopting criteria. Using a qualitative approach to supplier selection may be advantageous in terms of costs, as provided for in criterion 8. The proposed method requires minimal time spent interviewing respondents and collecting weights and thus follows Criterion 9. The clear (Criterion 10) structure of the FAHP helps in improving the knowledge of buyers and suppliers. Implementing this approach can provide new insights to make supplier-focused decisions more accurately (criterion 11). By involving multiple respondents, a supplier evaluation problem becomes a strategic supplier selection tool. It thus contributes to the communication and justification of purchasing decisions (criterion 12) and increases the requirements for decision-making competence (criterion 13). It can motivate both suppliers and buyers to improve their respective performance. In summary, the study's contribution lies in developing a structural transformation of the FAHP that best addresses the uncertainty and disruption caused by Covid-19.

The proposed FAHP structural change could result in buyer-supplier relationships going beyond the mere buyer-supplier association. In addition, there is the possibility ability to continuously realign supplier goals with those of buyers and vice versa when applying suggestive structural transformation. The orientation of the guidelines towards appraisers and suppliers can be reflected by the methodology specified in this invention. Supply chain performance can be improved indirectly by applying well-defined criteria and an evaluation process as recommended in this study. FAHP's proposed structural transformation approach can help purchasing and procurement managers identify, evaluate and select potential suppliers and eliminate the weaker ones. The proposed model also supports supplier involvement immediately before design is ready. The accuracy of a supplier's analysis and ranking can be ensured through a structural transformation approach. Additionally, this model presents a challenge for suppliers to update their systems to meet buyer expectations. Better optimization of alternatives can be achieved by evaluating suppliers under uncertainty. In addition, the structurally changed FAHP offers an easy-to-understand and transparent approach for sourcing professionals.

The application of the proposed methodology focuses on the unit of analysis consisting of suppliers within an Indian automobile company. In the present case, the quality of a supplier is given the highest weight in view of a demanding performance situation and strong competition among automobile manufacturers. It helps the original equipment manufacturer (OEM) meet consumer needs and advance supply chain strategies. Therefore, the role of a supplier is crucial from the design of the product or service to the delivery of raw materials. The quality of suppliers depends on the resources, the infrastructure, the qualification level of the employees, the supplies and the raw materials. In addition, supplier quality is also influenced by communication and problem-solving ability as they have the potential to influence the performance of the entire supply chain. The supplier's location has a direct impact on logistics costs and delivery time.

In the automotive sector selected for this study, it can be observed that some OEMs consider the location of their suppliers close to their facilities as one of the key selection criteria. It is an ideal situation for companies to improve their performance at the lowest cost. The cost of deliveries and the delivery time are the two other essential criteria when evaluating the supplier. Delivery costs also include a supplier's ability to negotiate with its suppliers (supplier's supplier) and manage the company's operations according to lean principles. The delivery task focuses on the supplier's ability to manage routes, transportation and timeliness. The customer service of OEM suppliers is also considered crucial. It helps to assess the quantity and quality of shipments as well as their acceptance, whether these batches are partially, fully accepted or completely rejected.

In the present invention, all these aspects have been considered while proposing the structural transformation in the FAHP approach. The approach also suggests accommodating multiple respondents in different locations. This study considers the case of an automobile company. The case study reinforced the formalization of the management information model through a technological information base. It can help purchasing and procurement managers consider and combine multiple supplier performance responses for different evaluation factors. In conclusion, the study offers interesting and meaningful implications for purchasing and procurement researchers and practitioners

Evaluate suppliers holistically.

The drawing and the description above provide examples of embodiments. Those skilled in the art will recognize that one or more of the elements described can certainly be combined into a single functional element. Alternatively, certain elements can be divided into several functional elements. Elements of one embodiment may be added to another embodiment. For example, the orders of the processes described herein may be changed and are not limited to the manner described herein. Additionally, the actions of a flowchart do not have to be implemented in the order shown; Not all actions necessarily have to be carried out. Even those actions that are not dependent on other actions can be carried out in parallel with the other actions. The scope of the embodiments is in no way limited by these specific examples. Numerous variations, whether explicitly stated in the specification or not, such as: B. Differences in structure, dimensions and material use, are possible. The scope of the embodiments is at least as broad as indicated by the following claims.

Advantages, other benefits, and solutions to problems have been described above with respect to specific embodiments. However, the advantages, benefits, solutions to problems and any components that may cause a benefit, advantage or solution to occur or become more pronounced should not be construed as a critical, necessary or essential function or component of any or all of the claims.

CREDENTIALS

100

A system for designing the structural transformation of a supply chain based on a fuzzy analytical hierarchy.

102

Input unit

102a

Database

102b

User device

104

Fuzzy logic based processing unit

106

comparator

108

Computing unit

110

Processing unit based on fuzzy logic

112

Normalization unit

Claims (3)

A system (100) for designing a structural transformation of a fuzzy analysis hierarchy-based supply chain, the system (100) comprising: an input unit (102) for collecting responses from multiple raters as input to perform a pairwise comparison for performing rankings based on multiple factors and storing them in a database (102a), the responses being generated by a user device (102b); a fuzzy logic based processing unit (104) connected to the input unit (102) for processing the collected input responses to calculate fuzzy weights based on a kappa value, the fuzzy logic based processing unit as follows includes: a comparator (106) for receiving a plurality of fuzzy logic criteria for comparison based on the answers, the answers being compared based on a fuzzy triangular scale when sorting in either ascending or descending order; and a computing unit (108) connected to the comparator (106) for calculating a plurality of values such as the kappa value, a geometric mean and a fuzzy weight; And a defuzzification-based processing unit (110) associated with the fuzzy logic-based processing unit (104) for defuzzification of the calculated weights. System after Claim 1 , wherein the computing unit (108) is configured to determine the type of agreement of the respondents to the comparison of the pairs, which indicates the value of K ≥ 0.21. System after Claim 1 , wherein a normalization unit (112) is connected to the defuzzification-based processing unit (110) to perform normalization of the defuzzification weights for each of the plurality of factors.

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