Enterprise resource digital allocation management optimization method, device and readable storage medium

Abstract

The present invention proposes a method, device and readable storage medium for optimizing the digital allocation management of enterprise resources. By constructing a multi-dimensional and multi-layer service management model, the customer's perception and expectation of service quality is quantified, and the comprehensive utility function method is used to compare asymmetric triangular fuzzy numbers to screen out high-importance and low-perception service requirements to be improved, and their weights are calculated based on P-I analysis. Then, a linear programming model for enterprise resource optimization is constructed, the fuzzy evaluation results are integrated, the constraints are simplified, the accurate allocation of enterprise resources is achieved, and customer service satisfaction is maximized. The present invention effectively solves the shortcomings of traditional service quality management methods in accurately predicting customers' future service perceptions, identifying key service factors and optimizing resource allocation, and has important practical application value.

Description

Technical Field

[0001] The present invention relates to the intersection of enterprise resource management and service quality management, and in particular to a method and device for optimizing digital allocation management of enterprise resources and a readable storage medium thereof. Background Art

[0002] With the booming development of the digital economy, all industries are accelerating their digital transformation, and the service economy has ushered in unprecedented changes and opportunities. In this context, service quality management has become a key factor for enterprises to improve user experience and satisfaction and enhance market competitiveness. Traditional service quality evaluation and improvement methods have many limitations and cannot meet the needs of modern enterprises for accurate and efficient service management.

[0003] First, traditional service quality evaluation methods often focus on the evaluation of the company's existing service quality, and lack accurate predictions of customers' future service perception expectations. This makes it difficult for companies to fully consider customers' potential needs and expectations when formulating service improvement strategies, thus affecting the effectiveness of service improvement.

[0004] Secondly, when identifying service quality management factors (SMFs), traditional methods often fail to effectively distinguish service factors that are highly demanded by customers but poorly rated. These factors are usually the key points for companies to improve service quality, but due to the limitations of traditional methods, companies may not be able to accurately identify and prioritize these factors for improvement, thus missing out on opportunities to improve customer satisfaction.

[0005] In addition, traditional service quality management methods are also insufficient in terms of resource allocation. Enterprise resources are limited, and how to reasonably allocate resources to various service links to maximize customer satisfaction is an important challenge facing enterprises. Traditional methods lack an effective resource allocation optimization model, making it difficult to achieve accurate allocation and efficient use of resources.

[0006] In order to solve the above problems, the present invention proposes an enterprise resource digital allocation management optimization method for improving customer service perception. This method constructs an advanced fuzzy linear programming model, combines a multi-dimensional and multi-layer service management model and an improved PI analysis algorithm, accurately identifies and quantifies customer perception and expectations of service quality, optimizes enterprise resource allocation, and thus maximizes customer service satisfaction. Summary of the invention

[0007] The embodiments of the present invention provide a method, device and readable storage medium for optimizing the digital allocation management of enterprise resources. The present invention aims to solve the problems that the service quality evaluation and improvement are separated in the past service management research in the current technology, the traditional quality function deployment method cannot accurately evaluate customer expectations, focus on key factors and optimize resource allocation, and it is difficult to deal with the ambiguity of services.

[0008] The core technology of the present invention is mainly to realize the digital allocation and management of enterprise resources to enhance customer service perception by integrating digital technology, adopting multi-dimensional and multi-layer models, fuzzy evaluation quantification, comprehensive utility function method, improved PI analysis and fuzzy linear programming model.

[0009] In a first aspect, the present invention provides a method for optimizing digital deployment management of enterprise resources, the method comprising the following steps:

[0010] S1. Setting of evaluation dimensions for digital service management:

[0011] Adopt a multi-dimensional and multi-layered service management model that includes three main dimensions: service environment, service process, and service value, and subdivide it into multiple sub-dimensions to construct an evaluation system;

[0012] S2. Quantification of service quality perception and importance fuzzy evaluation:

[0013] Design a service quality questionnaire, use seven-level language rating and triangular fuzzy numbers to quantify customers' perceived evaluation and expected importance of service quality, and solve the fuzzy perception and fuzzy expectations of multiple customers' service quality evaluation factors through the average operation rule of triangular fuzzy numbers;

[0014] S3. Calculation and comparison of comprehensive utility value of variables:

[0015] The comprehensive utility function method is introduced to deal with the comparison problem of asymmetric triangular fuzzy numbers, and the comprehensive utility value of asymmetric triangular fuzzy numbers is calculated to achieve the comparison of the size of asymmetric triangular fuzzy numbers.

[0016] S4. Screening and weight calculation of demand for improvement based on PI analysis:

[0017] According to the requirements for improving customer satisfaction, select the customer service needs to be improved, which must meet the two conditions of importance greater than the perceived evaluation and greater than the average value of the overall comprehensive utility;

[0018] Using the improved PI analysis graphic judgment method, the service quality management factors are divided into multiple location areas, and the factors to be improved with high importance and low perception are screened out, and their weights are calculated;

[0019] S5. Construction and solution of enterprise resource optimization model:

[0020] Define the enterprise resource optimization problem of maximizing customer service improvement and construct a linear programming model;

[0021] Through the fuzzy linear programming model, the fuzzy evaluation results are integrated to simplify the constraint operation;

[0022] The comprehensive utility value of customer service quality perception and importance fuzzy evaluation is used to determine the boundary constraints of the objective function.

[0023] The fuzzy linear programming model is transformed into a deterministic linear programming model with a given threshold to solve the optimal allocation plan of enterprise resources.

[0024] Furthermore, in step S1, the sub-dimensions of the service environment include dynamics, complexity, and threat; the sub-dimensions of the service process adopt the five sub-dimensions of the SERVQUAL scale, namely, tangibles, reliability, responsiveness, assurance, and empathy; and the sub-dimensions of service value are divided into quality, effect, and end state based on the “path-end theory”.

[0025] Furthermore, in step S2, the seven-level language rating includes worst, poor, bad, average, good, very good and best, and the corresponding triangular fuzzy numbers are (0, 0, 0.2), (0, 0.2, 0.4), (0.2, 0.35, 0.5), (0.3, 0.5, 0.7), (0.5, 0.65, 0.8), (0.6, 0.8, 1) and (0.8, 1, 1).

[0026] Furthermore, in step S2, the triangular fuzzy number method is used to quantify the language rating as follows:

[0027] TFNs=(a,b,c)

[0028] Among them, b is the most likely value judged by the customer, and a and c are the two upper and lower boundaries of b, which can be understood as the most pessimistic value and the most optimistic value;

[0029] use Indicates the customer Service quality factors for enterprises Importance expectation evaluation; adopt It represents the evaluation value of customer perceived service quality;

[0030] The average value operation rule of triangular fuzzy numbers is used to solve the fuzzy perception and fuzzy expectation of multiple customers' service quality evaluation factors, and the first The expected importance of service quality evaluation factors and the mean perceived quality .

[0031] Furthermore, in step S3, after introducing the comprehensive utility function method, The comprehensive utility value is , the formula is:

[0032]

[0033] The comprehensive utility value is , the formula is:

[0034]

[0035] Among them, when and When the elements in are in the interval , express Based on the right side maximize the quality set The utility value of express Based on the left-hand minimized mass set The utility value of express Based on the right side maximize the quality set The utility value of express Based on the left-hand minimized mass set utility value.

[0036] Furthermore, in step S4, the improved PI analysis graphic judgment method divides the service quality management factors into four location areas:

[0037] Location area 1 is of high importance and low perception, and is the key element to be improved in service management;

[0038] Location area 2 is high importance and high perception, and the existing situation needs to be maintained;

[0039] Location area 3 is of low importance and high perception, and its service resources can be appropriately reduced to improve the elements in location area 1;

[0040] Location area 4 is of low importance and low perception and is an element that does not require attention.

[0041] Furthermore, in step S5, the objective function of the fuzzy linear programming model is:

[0042]

[0043] The constraints are:

[0044]

[0045] in, To improve satisfaction with service quality, Provide enterprise resource element configuration plan, Equal to the customer's expectation of the importance of service, Equal to the customer service perception judgment value;

[0046] It indicates the actual management effect after the application of enterprise capabilities;

[0047] Indicates the effect of customer expectation management under ideal conditions;

[0048] It is the comprehensive ratio of the maximum service management innovation effect to the expected management effect after the implementation of enterprise resource optimization allocation, reflecting the comprehensive level of improvement in customer perceived demand satisfaction;

[0049] Indicates The first enterprise resource element and the A functional relationship between the customer service needs to be improved;

[0050] Represents enterprise capabilities correlation with other enterprise capabilities;

[0051] To improve service quality The weight of .

[0052] In a second aspect, the present invention provides an enterprise resource digital deployment management optimization device, comprising:

[0053] The digital service management evaluation dimension setting module adopts a multi-dimensional and multi-layer service management model including three main dimensions: service environment, service process and service value, and subdivides multiple sub-dimensions to construct an evaluation system;

[0054] The module of quantification of service quality perception and importance fuzzy evaluation is designed to design a service quality questionnaire, and use seven-level language rating and triangular fuzzy to quantify customers' perception and expected importance of service quality. The fuzzy perception and fuzzy expectation of multiple customers' service quality evaluation factors are solved by the average value operation rule of triangular fuzzy numbers.

[0055] The variable comprehensive utility value calculation and comparison module introduces the comprehensive utility function method to deal with the comparison problem of asymmetric triangular fuzzy numbers, calculates the comprehensive utility value of asymmetric triangular fuzzy numbers, and realizes the comparison of the size of asymmetric triangular fuzzy numbers;

[0056] The module for screening and weight calculation of needs to be improved screens customer service needs to be improved according to the requirements for improving customer satisfaction. The needs must meet two conditions: the importance is greater than the perceived evaluation and greater than the average value of the overall comprehensive utility. The improved PI analysis graphic judgment method is used to divide the service quality management factors into multiple position areas, screen out the factors to be improved with high importance and low perception, and calculate their weights.

[0057] The enterprise resource optimization model construction and solution module defines the enterprise resource optimization problem of maximizing customer service improvement and constructs a linear programming model; through the fuzzy linear programming model, the fuzzy evaluation results are integrated to simplify the constraint operation; the comprehensive utility value of customer service quality perception and importance fuzzy evaluation is used to determine the boundary constraints of the objective function; the fuzzy linear programming model is transformed into a deterministic linear programming model with a given threshold to solve the enterprise resource optimization configuration plan.

[0058] In a third aspect, the present invention provides an electronic device including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the above-mentioned enterprise resource digital allocation management optimization method.

[0059] In a fourth aspect, the present invention provides a readable storage medium, in which a computer program is stored. The computer program includes a program code for controlling a process to execute a process. The process includes the above-mentioned enterprise resource digital allocation management optimization method.

[0060] The main contributions and innovations of the present invention are as follows:

[0061] 1. Accurately identify key service factors: Through the improved PI analysis algorithm, the present invention can accurately identify high-importance, low-perception service quality management factors to be improved, solving the problem that traditional methods are difficult to effectively distinguish key service factors, enabling enterprises to make more targeted service improvements.

[0062] 2. Optimize resource allocation: The present invention constructs a linear programming model for enterprise resource optimization, efficiently integrates fuzzy evaluation results through a fuzzy linear programming model, simplifies constraint calculations, and achieves accurate allocation of enterprise resources, thus solving the shortcomings of traditional methods in resource allocation and improving resource utilization efficiency.

[0063] 3. Improve customer satisfaction: This invention quantifies customers' perception and expectations of service quality, uses the comprehensive utility function method to compare asymmetric triangular fuzzy numbers, screens out service needs to be improved, and calculates their weights, so that enterprises can better meet customer needs and improve customer satisfaction.

[0064] 4. Enhance enterprise competitiveness: The method of the present invention can help enterprises manage service quality more effectively, improve customer satisfaction, thereby enhancing the competitiveness of enterprises in the market and bringing better economic and social benefits to enterprises.

[0065] 5. Strong adaptability: The method of the present invention is applicable to various service-oriented enterprises, has strong adaptability and operability, and can help enterprises better cope with market changes and diversified customer needs.

[0066] The details of one or more embodiments of the invention are set forth in the following drawings and description so that other features, objects, and advantages of the invention are more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0068] Figure 1 It is a process of the enterprise resource digital deployment management optimization method according to an embodiment of the present invention;

[0069] Figure 2 is a schematic diagram of an improved PI analysis graphical judgment method according to an embodiment of the present invention;

[0070] Figure 3 is an application diagram of an improved PI analysis graphical judgment method according to an embodiment of the present invention;

[0071] Figure 4 is a schematic diagram of blur level removal according to an embodiment of the present invention;

[0072] Figure 5 4 is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0073] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with one or more embodiments of this specification. Instead, they are merely examples of devices and methods consistent with some aspects of one or more embodiments of this specification as detailed in the appended claims.

[0074] It should be noted that: in other embodiments, the steps of the corresponding method are not necessarily performed in the order shown and described in this specification. In some other embodiments, the steps included in the method may be more or less than those described in this specification. In addition, a single step described in this specification may be decomposed into multiple steps for description in other embodiments; and multiple steps described in this specification may be combined into a single step for description in other embodiments.

[0075] Traditional service quality evaluation and improvement methods have limitations. They cannot accurately evaluate the importance of customer service perceptions and expectations for the future, nor do they fully focus on service quality management factors (SMFs) that are highly demanded by customers but poorly evaluated. They also do not deeply explore how to improve customer demand satisfaction by optimizing enterprise resource allocation.

[0076] Based on this, the present invention solves the problems existing in the prior art by constructing an advanced fuzzy linear programming model.

[0077] Embodiment 1

[0078] The present invention aims to propose an optimization method for the digital allocation management of enterprise resources. By constructing an advanced fuzzy linear programming model, it solves the problem of optimizing the configuration of enterprise capabilities to maximize the comprehensive satisfaction of customer needs. Based on the dimensional hierarchical management method of digital service quality management, a digital service quality questionnaire is carefully designed, and triangular fuzzy numbers (TFNs) are used to accurately quantify the subjective ratings of users on service quality and experts on the correlation between quality and enterprise capabilities. At the same time, the fuzzy P-I (Perception-Importance) analysis algorithm is innovatively improved to accurately identify the high-importance and low-perception service quality management factors to be improved and their weights. Subsequently, experts are organized to use digital tools to evaluate the correlation between customers' demand for service quality improvement and enterprise resource allocation capabilities, as well as the autocorrelation of enterprise resource allocation, and these fuzzy evaluation results are efficiently integrated to simplify constraint operation. Finally, with the help of the advanced idea of ​​fuzzy-QFD target decision-making, the fuzzy expectation operator is used to obtain the expected value of enterprise capabilities, thereby realizing the innovation of digital service management based on the optimal configuration of enterprise resources.

[0079] Specifically, the embodiment of the present invention provides an enterprise resource digital allocation management optimization method, specifically, referring to Figure 1 , the method comprising:

[0080] S1. Setting of evaluation dimensions for digital service management:

[0081] Adopt a multi-dimensional and multi-layered service management model that includes three main dimensions: service environment, service process, and service value, and subdivide it into multiple sub-dimensions to construct an evaluation system;

[0082] In this embodiment, step S1 specifically includes: adopting a multi-dimensional and multi-layer service management model, starting from the three main dimensions of service environment, service process, and service value, based on service characteristics and related theories, setting dynamics (dynamic changes in market competition, technology, and service innovation, reflecting industry vitality), complexity (referring to the ability of service enterprises to maintain stable service quality and sustainable upgrades in a complex, changeable and fully competitive market environment) and threats (relying on the support of environmental resources to cope with competition) as service environment sub-dimensions; using the five sub-dimensions of the SERVQUAL scale as service process sub-dimensions, and tangibles (service facilities, equipment, and appearance of service personnel), reliability (the ability to reliably and accurately fulfill service commitments), responsiveness (service enterprises help customers improve service expectations), assurance (customers' trust in service personnel and enterprises) and empathy (the service process is full of human touch) as interaction quality sub-dimensions; according to the "path-end theory", quality, effect, and end state are used as service value sub-dimensions, quality refers to the characteristic attributes inherent in the service itself, effect refers to what the product can bring to consumers, and end state refers to the core values, intentions, and goals deep in the hearts of consumers, so as to comprehensively and scientifically construct an evaluation system.

[0083] S2. Quantification of service quality perception and importance fuzzy evaluation:

[0084] Design a service quality questionnaire, use seven-level language rating and triangular fuzzy numbers to quantify customers' perceived evaluation and expected importance of service quality, and solve the fuzzy perception and fuzzy expectations of multiple customers' service quality evaluation factors through the average operation rule of triangular fuzzy numbers;

[0085] In this embodiment, step S2 specifically includes: first, using a service quality questionnaire to investigate customers' perception and importance of service quality. Then, using a seven-level language rating (Table 1), the perceived quality evaluation and expected importance evaluation of customers completing the service quality survey are quantified into seven-level triangular fuzzy numbers. For each service quality management factor, the importance and perception evaluation of the language rating are obtained. There are seven levels (as shown in Table 1), namely: worst, poor, bad, average, good, very good, and best. The language rating is quantified using the triangular fuzzy number (TFNs) method. ,in is the most likely value judged by the customer, and Then The two upper and lower boundaries can be understood as the most pessimistic value and the most optimistic value. Indicates the customer Service quality factors for enterprises Importance of expected evaluation, The evaluation value of customer perceived service quality.

[0086] The average value operation rule of triangular fuzzy numbers is used to solve the fuzzy perception and fuzzy expectation of multiple customers' service quality evaluation factors, and the first The expected importance of service quality evaluation factors and the mean perceived quality The formula for calculating the mean of each item is as follows:

[0087] , (1)

[0088] Similarly, The number of customers who participated in the evaluation.

[0089] Table 1 Seven-level fuzzy language rating table

[0090]

[0091] S3. Calculation and comparison of comprehensive utility value of variables:

[0092] The comprehensive utility function method is introduced to deal with the comparison problem of asymmetric triangular fuzzy numbers, and the comprehensive utility value of asymmetric triangular fuzzy numbers is calculated to achieve the comparison of the size of asymmetric triangular fuzzy numbers.

[0093] In this embodiment, step S3 specifically includes: and As an asymmetric triangular fuzzy number, it is difficult to directly compare the size. Therefore, the comprehensive utility function method (Cochran & Chen, 2005) was introduced, such as for The comprehensive utility value is based on the maximized utility value on the right side. and based on the left-hand side minimizing the utility value The comprehensive expression of is:

[0094] (2a)

[0095] Among them, when and When the elements in are in the interval , express Based on the right side maximize the quality set utility value. ,express Based on the left-hand minimized mass set utility value.

[0096] for The comprehensive utility value of Similar to the following:

[0097] (2b)

[0098] in, and .

[0099] S4. Screening and weight calculation of demand for improvement based on PI analysis:

[0100] According to the requirements for improving customer satisfaction, select the customer service needs to be improved, which must meet the two conditions of importance greater than the perceived evaluation and greater than the average value of the overall comprehensive utility;

[0101] Using the improved PI analysis graphic judgment method, the service quality management factors are divided into multiple location areas, and the factors to be improved with high importance and low perception are screened out, and their weights are calculated;

[0102] In this embodiment, step S4 specifically includes: based on the fact that customer satisfaction after implementing service management innovation is greater than the original service quality perception and necessarily exceeds the customer's expectations of the importance of service quality (due to limited resources and the need to concentrate efforts on breakthrough innovation), screening of customer service needs to be improved must meet two judgment conditions: first, customers believe that the importance of service quality management factors is greater than the existing perceived evaluation of the factors, indicating that there is a need to improve the factors; second, customers' evaluation of the importance of a certain service quality management factor is greater than the average value of the overall comprehensive utility value of the importance evaluation of all service quality management factors.

[0103] The improved graphic judgment method (Lin, 2010) can more intuitively screen customer needs (such as Figure 2 ), Figure 2 Divided into four positions by a 45-degree line and a vertical line, is the comprehensive utility value of service quality importance evaluation The average value is calculated as:

[0104] , (3)

[0105] The improved PI analysis graphical judgment method has four position areas. Their representative meanings are as follows:

[0106] Position zone 1 represents high importance and low perception, which is the key element to be improved in service management;

[0107] Location zone 2 represents high importance and high perception, and the existing situation needs to be maintained;

[0108] Location area 3 represents low importance and high perception, and its service resources can be appropriately reduced to improve the elements in location area 1;

[0109] Position zone 4 represents low importance and low perception, and is an element that does not require attention.

[0110] Since the selected customer needs may be arranged discontinuously, for the convenience of the following expression, Figure 2 Location area 1 The service quality management factors (SMFs) are numbered consecutively and redefined as customer needs to be improved. , .

[0111] Customer demand to be improved It is a service quality evaluation factor with high importance and low perception, so its weight coefficient is The customer service perception-expectation importance gap that needs to be combined with SMFs Comparison of customer expectations on the importance of factors to improve service management , and after data standardization, meet , the formula is:

[0112] , (4a)

[0113] Among them, the comprehensive utility function method is suitable for the comparison of asymmetric triangular fuzzy numbers, but not for the calculation of customer service perception-expectation importance gap. As the Customer needs to be improved The corresponding The expected importance of service quality management factors (SMFs) and the mean perceived quality The vertex method (Chen, 2000) is used to accurately and intuitively calculate the distance between the two asymmetric triangular fuzzy numbers of customer service perception-expectation importance gap (P-I). The calculation formula is:

[0114] , (4b)

[0115] As customer demand to be improved The expected importance comparison value reflects the customer's expectation of improving service quality, which can be expressed by the expected comprehensive utility value. and the highest comprehensive utility value The ratio method is used for quantification, and the calculation formula is as follows:

[0116] , (4c)

[0117] and Using the calculation formulas in (2a) and (2b) above, such as:

[0118]

[0119] in, . The value calculated for the highest rated triangular fuzzy number.

[0120] S5. Construction and solution of enterprise resource optimization model:

[0121] Define the enterprise resource optimization problem of maximizing customer service improvement and construct a linear programming model;

[0122] Through the fuzzy linear programming model, the fuzzy evaluation results are integrated to simplify the constraint operation;

[0123] The comprehensive utility value of customer service quality perception and importance fuzzy evaluation is used to determine the boundary constraints of the objective function.

[0124] The fuzzy linear programming model is transformed into a deterministic linear programming model with a given threshold to solve the optimal allocation plan of enterprise resources.

[0125] In this embodiment, step S5 specifically includes: maximizing the satisfaction of customer service demand improvement to determine the target level of allocating enterprise capabilities, which can be regarded as a target optimization problem. As follows:

[0126] (5a)

[0127] Constraints:

[0128] , (5b)

[0129] , (5c)

[0130] , (5d)

[0131] in, To improve satisfaction with service quality, It is a solution for the allocation of enterprise resource elements. In the objective function, It indicates the actual management effect after the application of enterprise capabilities;

[0132] Represents the effect of customer expectation management under ideal conditions. It is the comprehensive ratio of the maximum service management innovation effect to the expected management effect after the implementation of enterprise resource optimization allocation, reflecting the comprehensive level of improvement in customer perceived demand satisfaction. Indicates The first enterprise resource element and the Functional relationship of customer service needs to be improved, Represents enterprise capabilities and correlations with other enterprise capabilities. To improve service quality The weight of Customer service demands, demand weights, and boundary constraints all originate from customers’ fuzzy evaluations, which are in line with the market-oriented and customer-based approach.

[0133] and And the correlation values ​​between different x are obtained through expert evaluation. Expert members Ability to allocate enterprise resource elements and customer service needs to be improved The seven-level fuzzy evaluation of the correlation is recorded as , combined with expert authority have to , the calculation formula is as follows:

[0134] (5e)

[0135] Autocorrelation of enterprise resource factor allocation capability The correlations are divided into 1-3-9-18, indicating weak, medium, strong and very strong correlations, respectively. Give its correlation matrix , combined with expert authority The correlation matrix , the calculation formula is as follows:

[0136] (5f)

[0137] The fuzzy linear model has two constraints (5b) and (5c), which will bring great difficulties to the model operation. In order to simplify the model operation, the constraint merging method is adopted to combine the enterprise resource element configuration capability The autocorrelation parameter Fuzzy correlation evaluation parameters are incorporated into the customer service needs to be improved and the enterprise resource element allocation capabilities To obtain comprehensive evaluation parameters Matrix. The formula is as follows:

[0138] , , (5g)

[0139] Therefore, the constraint can be redefined as yes and The comprehensive embodiment of The correlation equation of . The simplified formula of the fuzzy linear programming equation constraint is as follows.

[0140] (5h)

[0141] Since asymmetric triangular blur is difficult to compare, defuzzification is helpful to obtain Maximize the effect of service management innovation. Based on the decomposition theorem and performance theorem of fuzzy set theory, and drawing on the function defuzzification method proposed by Chen and Chen (2006), It is expressed as the linear function relationship between the decision variables and the objective function, expressed by the fuzzy linear regression equation. Maximization, therefore only involves the enterprise's resource element allocation capabilities and customer service needs to be improved Correlation, using the matrix The objective function The expansion formula is as follows:

[0142] , (6a)

[0143] By eliminating The ambiguity can be realized Defuzzification. It is the enterprise's resource element allocation capability and customer service needs to be improved The triangular fuzzy number for correlation evaluation is .

[0144] in, . express The peak value, and Represent the upper and lower limits respectively. Substitute into the formula , Can be transformed into an asymmetric triangular fuzzy number:

[0145] (6b)

[0146] According to the extension principle of fuzzy mathematics and the cut-set, decomposition and representation theorem, conduct Cut , The lower and upper boundaries are:

[0147] (6c)

[0148] (6d)

[0149] when , Domain The set of all fuzzy sets above, and , then it is called for The λ cut set of can be expressed as an interval:

[0150]

[0151]

[0152] (6e)

[0153] is the number of fuzzy sets, and the upper and lower limits of asymmetric triangular fuzzy numbers are used as interval boundaries. The definite integral method is used to calculate all the numbers of elements in the λ cut-off interval. The average value of , and obtain the defuzzified real number at different λ cutoff levels. The formula is as follows:

[0154]

[0155] (6f)

[0156] When λ takes different values hour, There are different forms of expression.

[0157] (1) When the cut set λ=0, Equivalent to fuzzy sets The support set of all interval numbers R (R represents a real number) is converted into a continuous interval number for averaging operation according to the method of average level cut set defuzzification in a continuous interval:

[0158]

[0159] (6g)

[0160] (2) Cutting Since the existing literature does not provide The horizontal cut-set defuzzification method of the connected interval under the condition is extended on the basis of the original formula. hour, Fuzzy set Membership The continuous interval of . And according to the mapping relationship, such as Figure 4 As shown, The boundary is , The number of cardinal numbers in the interval range is Therefore, the mean formula can be expressed as:

[0161]

[0162]

[0163]

[0164] (6h)

[0165] When the cut set λ=1, the asymmetric triangular fuzzy number is a real number :

[0166] (6i)

[0167] (4) and Constraints combined , the formula can be expressed as:

[0168] (6j)

[0169] It is the enterprise's resource element allocation capability. is a triangular fuzzy number expressed as ,in, . express The peak value, and Represent the upper and lower limits respectively.

[0170] Similarly, (6b) and (6e), according to the fuzzy cut set, decomposition and representation theorem, when When the asymmetric triangular fuzzy number Perform λ-cut to minimize output ambiguity, ,in,

[0171]

[0172]

[0173]

[0174] It can be represented by the following interval numbers:

[0175]

[0176] (6k)

[0177] because , when λ=h, we get the new constraint:

[0178]

[0179] (6l)

[0180] Boundary constraints on the objective function and is the minimum and maximum value of the satisfaction degree of service quality improvement, which is determined by the comprehensive utility value of customer service quality perception and importance fuzzy evaluation:

[0181] and It represents the customer's perceived judgment value of service quality management factors and their expected importance value. The minimum and maximum values ​​of Therefore, the minimum value is equal to the customer service perception judgment value , the maximum value is equal to the customer's expected value of service importance After implementing service management innovation, customer satisfaction with service needs is greater than the original customer service perception. Under the action of the model objective function, Will get as close as possible .

[0182] When λ takes different values ​​h Substituting into the objective function and combining it with the inequality method after defuzzification of the constraints, the fuzzy linear programming model is transformed into a deterministic linear programming model with a given h threshold:

[0183]

[0184] ,

[0185] (7)

[0186] Embodiment 2

[0187] Based on the same concept, this embodiment uses a questionnaire survey to determine the customer's perception of the service quality of the mobile communication operator and the expected importance to evaluate it. The expected importance questionnaire is a text adjustment based on the perception questionnaire, reflecting the customer's inner belief that the service management factor should meet the quality requirements. Here, the eleven sub-dimensions of the service management evaluation model are regarded as 11 service management evaluation factors. .in For customers, considering that the service quality survey and analysis based on fuzzy logic does not require large sample operations, the service quality survey subjects are determined to be 44 people .

[0188] The questionnaire measurement items were evaluated using 7-level language variable descriptions. The service perception of the qth evaluation factor of the fth customer and service importance The specific value of is the mean of the relevant measurement item indicators of the evaluation model sub-dimension. The average value of customer group evaluation is obtained by the triangular fuzzy number mean calculation method. and , see Table 2 below:

[0189] Table 2 Fuzzy average of customer evaluation

[0190]

[0191] Then, using formulas (2a) and (2b), we can get the utility value and .pass The calculation process is described as follows:

[0192]

[0193]

[0194]

[0195] We get Table 3:

[0196] Table 3 Customer service perception and importance utility value

[0197]

[0198] Use formula (3) to calculate the expected comprehensive utility mean of service quality evaluation factors .

[0199]

[0200] According to the customer demand factor judgment conditions, customer demand can also be judged by the location area graphic method (such as Figure 3 as shown).

[0201] The available quality factors 1, 5, 6, 9, and 10 are high-importance, low-perception factors to be improved that fall into location area 1, and they are reordered. : 1 dynamic; 2 reliable; 3 responsive; 4 quality; 5 effect. It can be seen that customers hope that communications can provide more diverse services, guarantee network coverage and service quality, and that communications companies can handle problems as quickly as possible when problems occur or when there are new demands. Customers have high requirements for quality and effect, regard mobile communication services as necessities in daily life, and focus mainly on practical value.

[0202] Use formulas (4a), (4b), and (4c) to obtain the demand weight. For example:

[0203]

[0204] The weight of customer demand can be increased for

[0205]

[0206] After data standardization, :

[0207]

[0208] The calculation results of each step are summarized in Table 4 below:

[0209] Table 4 Model parameters obtained based on comparison of customer perception-expectation gap

[0210]

[0211] Digital service companies need to have seven key capabilities, such as leadership and planning, when managing. These capabilities are important factors for companies to successfully achieve digital transformation and improve operational efficiency. The obtained values ​​are calculated through expert evaluation (5f), as shown in Table 5:

[0212] Table 5 Weighted average enterprise capability self-relationship table

[0213]

[0214] Experts evaluate the relevance of the enterprise's resource allocation capabilities and the customer service needs to be improved, combined with the authority of experts According to formula (5e), ​​we get the weighted average correlation evaluation table 6, and build the fuzzy matrix based on this table. .

[0215] Table 6 Weighted average expert evaluation table

[0216]

[0217] In order to simplify the model operation, the constraint merging method is adopted to combine the enterprise resource element configuration capability The autocorrelation parameters are integrated into the fuzzy correlation evaluation parameters of customer service needs to be improved and the enterprise resource element allocation capability. To obtain comprehensive evaluation parameters Matrix. As shown in Table 7:

[0218] Table 7 Autocorrelation of integrated enterprise capabilities Expert evaluation form

[0219]

[0220] The linear programming equation is constructed based on the data in Table 4, Table 6 and Table 7 and formula (7). and It represents the customer's perceived judgment value of service quality management factors and their expected importance value. The minimum and maximum values ​​of , using the data in Table 4. It is the enterprise's resource element allocation capability and customer service needs to be improved The triangular fuzzy number of correlation evaluation, the data in Table 6 is applied to the model objective function. The constraint conditions use the merging method and the data in Table 7. The model objective function and constraint condition equations are:

[0221]

[0222] Assumptions , its linear programming is as follows:

[0223]

[0224] Constraints:

[0225]

[0226] Use Matlab to solve linear equations and obtain the given The threshold is used to obtain the calculated value of the enterprise capability under the condition of maximizing service quality improvement, as shown in Table 8 below:

[0227] Table 8 is based on the given Model solution for threshold

[0228]

[0229] According to the calculation results, it is found that planning, technical, basic and innovative capabilities are the main capabilities for improving service quality. Enterprises form enterprise capabilities through resource combination and then improve service quality. The resources they obtain are often limited. Therefore, they need to invest resources in the enterprise capabilities that are most likely to improve services. We normalize the enterprise capabilities obtained and obtain the results shown in Table 9. This reflects the ambiguity of management work. Generally, experienced managers make appropriate decisions based on the ambiguity of service content and management work. In Table 9, we find that when When the enterprise's capabilities are configured, the proportion of the varies significantly due to differences in After that, the ratio between the capabilities basically tends to be stable, indicating that the calculation results are feasible for practical operation.

[0230] Table 9

[0231]

[0232] Embodiment 3

[0233] Based on the same concept, the present invention also proposes an enterprise resource digital deployment management optimization device, comprising:

[0234] The digital service management evaluation dimension setting module adopts a multi-dimensional and multi-layer service management model including three main dimensions: service environment, service process and service value, and subdivides multiple sub-dimensions to construct an evaluation system;

[0235] The module of quantification of service quality perception and importance fuzzy evaluation is designed to design a service quality questionnaire, and use seven-level language rating and triangular fuzzy to quantify customers' perception and expected importance of service quality. The fuzzy perception and fuzzy expectation of multiple customers' service quality evaluation factors are solved by the average value operation rule of triangular fuzzy numbers.

[0236] The variable comprehensive utility value calculation and comparison module introduces the comprehensive utility function method to deal with the comparison problem of asymmetric triangular fuzzy numbers, calculates the comprehensive utility value of asymmetric triangular fuzzy numbers, and realizes the comparison of the size of asymmetric triangular fuzzy numbers;

[0237] The module for screening and weight calculation of needs to be improved screens customer service needs to be improved according to the requirements for improving customer satisfaction. The needs must meet two conditions: the importance is greater than the perceived evaluation and greater than the average value of the overall comprehensive utility. The improved PI analysis graphic judgment method is used to divide the service quality management factors into multiple position areas, screen out the factors to be improved with high importance and low perception, and calculate their weights.

[0238] The enterprise resource optimization model construction and solution module defines the enterprise resource optimization problem of maximizing customer service improvement and constructs a linear programming model; through the fuzzy linear programming model, the fuzzy evaluation results are integrated to simplify the constraint operation; the comprehensive utility value of customer service quality perception and importance fuzzy evaluation is used to determine the boundary constraints of the objective function; the fuzzy linear programming model is transformed into a deterministic linear programming model with a given threshold to solve the enterprise resource optimization configuration plan.

[0239] Embodiment 4

[0240] This embodiment also provides an electronic device, referring to Figure 5 , comprises a memory 404 and a processor 402, wherein the memory 404 stores a computer program, and the processor 402 is configured to run the computer program to execute the steps in any of the above method embodiments.

[0241] Specifically, the processor 402 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiment of the present invention.

[0242] Among them, the memory 404 may include a large capacity memory 404 for data or instructions. For example, but not limitation, the memory 404 may include a hard disk drive (HDD), a floppy disk drive, a solid state drive (SSD), a flash memory, an optical disk, a magneto-optical disk, a tape, or a universal serial bus (USB) drive, or a combination of two or more of these. In appropriate cases, the memory 404 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 404 may be inside or outside the data processing device. In a specific embodiment, the memory 404 is a non-volatile memory. In a specific embodiment, the memory 404 includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (Programmable Read-Only Memory, PROM for short), an erasable PROM (Erasable Programmable Read-Only Memory, EPROM for short), an electrically erasable PROM (Electrically Erasable Programmable Read-Only Memory, EEPROM for short), an electrically alterable ROM (Electrically Alterable Read-Only Memory, EAROM for short) or a flash memory (FLASH) or a combination of two or more of these. In appropriate circumstances, the RAM may be a static random access memory (SRAM) or a dynamic random access memory (DRAM), wherein the DRAM may be a fast page mode dynamic random access memory 404 (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0243] The memory 404 may be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor 402 .

[0244] The processor 402 reads and executes the computer program instructions stored in the memory 404 to implement any one of the enterprise resource digital allocation management optimization methods in the above embodiments.

[0245] Optionally, the electronic device may further include a transmission device 406 and an input / output device 408 , wherein the transmission device 406 is connected to the processor 402 , and the input / output device 408 is connected to the processor 402 .

[0246] The transmission device 406 can be used to receive or send data via a network. Specific examples of the above-mentioned network may include a wired or wireless network provided by a communication provider of the electronic device. In one example, the transmission device includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 406 can be a radio frequency (Radio Frequency, referred to as RF) module, which is used to communicate with the Internet wirelessly.

[0247] Input / output devices 408 are used to input or output information.

[0248] Embodiment 5

[0249] This embodiment also provides a readable storage medium, in which a computer program is stored. The computer program includes a program code for controlling a process to execute the process. The process includes the enterprise resource digital allocation management optimization method according to the first embodiment.

[0250] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0251] In general, various embodiments may be implemented in hardware or dedicated circuits, software, logic, or any combination thereof. Some aspects of the invention may be implemented in hardware, while other aspects may be implemented in firmware or software executed by a controller, microprocessor, or other computing device, but the invention is not limited thereto. Although various aspects of the invention may be shown and described as block diagrams, flow charts, or using some other graphical representation, it should be understood that, as non-limiting examples, the boxes, devices, systems, techniques, or methods described herein may be implemented in hardware, software, firmware, dedicated circuits or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0252] Embodiments of the present invention may be implemented by computer software that is executable by a data processor of a mobile device, such as in a processor entity, or by hardware, or by a combination of software and hardware. Computer software or programs (also referred to as program products) including software routines, applets and / or macros may be stored in any device-readable data storage medium, and they include program instructions for performing specific tasks. A computer program product may include one or more computer executable components configured to perform an embodiment when the program is run. One or more computer executable components may be at least one software code or a portion thereof. In addition, at this point, it should be noted that, for example, Figure 1 Any block of the logic flow in the program may represent program steps, or interconnected logic circuits, blocks and functions, or a combination of program steps and logic circuits, blocks and functions. The software may be stored on physical media such as memory chips or storage blocks implemented within the processor, magnetic media such as hard disk or floppy disks, and optical media such as, for example, DVDs and their data variants, CDs, etc. Physical media are non-transitory media.

[0253] Those skilled in the art should understand that the technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0254] The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present invention. It should be pointed out that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the attached claims.

Claims

1. A method for optimizing the digital deployment management of enterprise resources, characterized in that: The following steps are involved: S1. Setting of evaluation dimensions for digital service management: A multi-dimensional and multi-layered service management model including three main dimensions, namely service environment, service process and service value, is adopted, and multiple sub-dimensions are subdivided to construct an evaluation system; the sub-dimensions of the service environment include dynamics, complexity and threat; the sub-dimension of the service process adopts the five sub-dimensions of the SERVQUAL scale, namely tangibles, reliability, responsiveness, assurance and empathy; the sub-dimension of service value is divided into quality, effect and end state according to the "path-end theory"; S2. Quantification of service quality perception and importance fuzzy evaluation: Design a service quality questionnaire, use seven-level language rating and triangular fuzzy numbers to quantify customers' perceived evaluation and expected importance of service quality, and solve the fuzzy perception and fuzzy expectations of multiple customers' service quality evaluation factors through the average operation rule of triangular fuzzy numbers; Among them, the triangular fuzzy number method is used to quantify the language rating as follows: TFNs=(a,b,c) Among them, b is the most likely value judged by the customer, a and c are the upper and lower boundaries of b, a is the most pessimistic value, and c is the most optimistic value; use Indicates the customer Service quality factors for enterprises Importance expectation evaluation; adopt It represents the evaluation value of customer perceived service quality; The average value operation rule of triangular fuzzy numbers is used to solve the fuzzy perception and fuzzy expectation of multiple customers' service quality evaluation factors, and the first The expected importance of service quality evaluation factors and the mean perceived quality ; S3. Calculation and comparison of comprehensive utility value of variables: The comprehensive utility function method is introduced to deal with the comparison problem of asymmetric triangular fuzzy numbers, and the comprehensive utility value of asymmetric triangular fuzzy numbers is calculated to achieve the comparison of the size of asymmetric triangular fuzzy numbers. S4. Screening and weight calculation of demand for improvement based on PI analysis: According to the requirements for improving customer satisfaction, select the customer service needs to be improved, which must meet the two conditions of importance greater than the perceived evaluation and greater than the average value of the overall comprehensive utility; Using the improved PI analysis graphic judgment method, the service quality management factors are divided into multiple location areas, and the factors to be improved with high importance and low perception are screened out, and their weights are calculated; S5. Construction and solution of enterprise resource optimization model: Define the enterprise resource optimization problem of maximizing customer service improvement and construct a linear programming model; Through the fuzzy linear programming model, the fuzzy evaluation results are integrated to simplify the constraint operation; The comprehensive utility value of customer service quality perception and importance fuzzy evaluation is used to determine the boundary constraints of the objective function. The fuzzy linear programming model is transformed into a deterministic linear programming model with a given threshold to solve the optimal allocation plan of enterprise resources.

2. The enterprise resource digital deployment management optimization method according to claim 1, characterized in that: In step S2, the seven-level language rating includes worst, poor, bad, average, good, very good and best, and the corresponding triangular fuzzy numbers are (0, 0, 0.2), (0, 0.2, 0.4), (0.2, 0.35, 0.5), (0.3, 0.5, 0.7), (0.5, 0.65, 0.8), (0.6, 0.8, 1) and (0.8, 1, 1).

3. The enterprise resource digital deployment management optimization method according to claim 1, characterized in that: In step S3, after the comprehensive utility function method is introduced, The comprehensive utility value is , the formula is: ； The comprehensive utility value is , the formula is: ； Among them, when and When the elements in are in the interval , express Based on the right side maximize the quality set The utility value of express Based on the left-hand minimized mass set The utility value of express Based on the right side maximize the quality set The utility value of express Based on the left-hand minimized mass set utility value.

4. The enterprise resource digital deployment management optimization method according to claim 1, characterized in that: In step S4, the improved PI analysis graphic judgment method divides the service quality management factors into four location areas: Location area 1 is of high importance and low perception, and is the key element to be improved in service management; Location area 2 is of high importance and high perception, and the existing situation needs to be maintained; Location area 3 is of low importance and high perception, and its service resources can be appropriately reduced to improve the elements in location area 1; Location area 4 is of low importance and low perception and is an element that does not require attention.

5. The enterprise resource digital deployment management optimization method according to any one of claims 1 to 4, characterized in that: In step S5, the objective function of the fuzzy linear programming model is: ； The constraints are: ； in, To improve satisfaction with service quality, Provide enterprise resource element configuration plan, Equal to the customer's expectation of the importance of service, Equal to the customer service perception judgment value; It indicates the actual management effect after the application of enterprise capabilities; Indicates the effect of customer expectation management under ideal conditions; It is the comprehensive ratio of the maximum service management innovation effect to the expected management effect after the implementation of enterprise resource optimization allocation, reflecting the comprehensive level of improvement in customer perceived demand satisfaction; Indicates The first enterprise resource element and the A functional relationship between the customer service needs to be improved; Indicates enterprise capabilities correlation with other enterprise capabilities; To improve service quality The weight of .

6. An enterprise resource digital deployment management optimization device, characterized in that: include: The digital service management evaluation dimension setting module adopts a multi-dimensional and multi-layer service management model including three main dimensions: service environment, service process and service value, and subdivides multiple sub-dimensions to construct an evaluation system; the sub-dimensions of the service environment include dynamic, complex and threatening; the sub-dimensions of the service process adopt the five sub-dimensions of the SERVQUAL scale, namely tangible, reliable, responsive, assured and empathetic; the sub-dimensions of the service value are divided into quality, effect and end state according to the "path-end theory"; The module of quantification of service quality perception and importance fuzzy evaluation is designed to design a service quality questionnaire, and use seven-level language rating and triangular fuzzy to quantify customers' perception and expected importance of service quality. The fuzzy perception and fuzzy expectation of multiple customers' service quality evaluation factors are solved by the average value operation rule of triangular fuzzy numbers. The variable comprehensive utility value calculation and comparison module introduces the comprehensive utility function method to deal with the comparison problem of asymmetric triangular fuzzy numbers, calculates the comprehensive utility value of asymmetric triangular fuzzy numbers, and realizes the comparison of the size of asymmetric triangular fuzzy numbers; The module for screening and weight calculation of needs to be improved screens customer service needs to be improved according to the requirements for improving customer satisfaction. The needs to meet the two conditions of importance being greater than the perceived evaluation and greater than the average value of the overall comprehensive utility. Using the improved PI analysis graphic judgment method, the service quality management factors are divided into multiple location areas, and the factors to be improved with high importance and low perception are screened out, and their weights are calculated; The enterprise resource optimization model construction and solution module defines the enterprise resource optimization problem of maximizing customer service improvement and constructs a linear programming model; Through the fuzzy linear programming model, the fuzzy evaluation results are integrated to simplify the constraint operation; the comprehensive utility value of customer service quality perception and importance fuzzy evaluation is used to determine the boundary constraints of the objective function; Transform the fuzzy linear programming model into a deterministic linear programming model with a given threshold to solve the optimal allocation plan of enterprise resources; Among them, the triangular fuzzy number method is used to quantify the language rating as follows: TFNs=(a,b,c) Among them, b is the most likely value judged by the customer, a and c are the upper and lower boundaries of b, a is the most pessimistic value, and c is the most optimistic value; use Indicates the customer Service quality factors for enterprises Importance expectation evaluation; adopt It represents the evaluation value of customer perceived service quality; The average value operation rule of triangular fuzzy numbers is used to solve the fuzzy perception and fuzzy expectation of multiple customers' service quality evaluation factors, and the first The expected importance of service quality evaluation factors and the mean perceived quality .

7. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to execute the enterprise resource digital deployment management optimization method according to any one of claims 1 to 5.

8. A readable storage medium, characterized in that: A computer program is stored in the readable storage medium, and the computer program includes a program code for controlling a process to execute a process, and the process includes the enterprise resource digital allocation management optimization method according to any one of claims 1 to 5.

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