Modeling customer satisfaction with new product design using a flexible fuzzy regression-data envelopment analysis algorithm

The success of new products depends greatly on customer satisfaction and meeting the customer needs is vital for new product development. By incorporating customer needs in the design and development process, organizations can improve productivity for their new products and reduce the risks associated with new product markets. Hence, design teams require methods to model customer satisfaction when setting the associated product design attributes. Thus, different approaches have been developed for modeling the relationship between customer satisfaction and product design parameters. In this study, 16 well-known fuzzy regression (FR) models are considered to understand the relationship between customer satisfaction and new product design. The design of FR models is based on the 4Ps marketing mix (product, price, place, and promotion) concept in fuzzy environments. A flexible algorithm is then presented based on the index of confidence, error measures, and data envelopment analysis for selecting the best FR model. The applicability and usefulness of the proposed algorithm is demonstrated experimentally based on an actual case study, where the flexible algorithm is employed to predict customer satisfaction with a new product design in the freezer/refrigerator industry.     

Quantile regression metamodeling: Toward improved responsiveness in the high-tech electronics manufacturing industry

Both technology and market demands within the high-tech electronics manufacturing industry change rapidly. Accurate and efficient estimation of cycle-time (CT) distribution remains a critical driver of on-time delivery and associated customer satisfaction metrics in these complex manufacturing systems. Simulation models are often used to emulate these systems in order to estimate parameters of the CT distribution. However, execution time of such simulation models can be excessively long limiting the number of simulation runs that can be executed for quantifying the impact of potential future operational changes. One solution is the use of simulation metamodeling which is to build a closed-form mathematical expression to approximate the input–output relationship implied by the simulation model based on simulation experiments run at selected design points in advance. Metamodels can be easily evaluated in a spreadsheet environment “on demand” to answer what-if questions without needing to run lengthy simulations. The majority of previous simulation metamodeling approaches have focused on estimating mean CT as a function of a single input variable (i.e., throughput). In this paper, we demonstrate the feasibility of a quantile regression based metamodeling approach. This method allows estimation of CT quantiles as a function of multiple input variables (e.g., throughput, product mix, and various distributional parameters of time-between-failures, repair time, setup time, loading and unloading times). Empirical results are provided to demonstrate the efficacy of the approach in a realistic simulation model representative of a semiconductor manufacturing system.     

Role of design-philosophies in interfacing manufacturing with marketing

Product design involves the mapping of a product's marketing attributes to its engineering and manufacturing features. In practice, it is a non-trivial task for design engineers to determine an appropriate mapping between a product's marketing and manufacturing attributes so as to generate a product design that satisfies customer-needs while being feasible to produce within the technical and financial constraints of the firm's manufacturing domain. In this paper, we hypothesize that in a mature industry, the expertise that guides designers in this mapping process exists in the form of an engineering design-philosophy that governs the design of products in that industry. Clearly, if we are able to discern the design-philosophies that exist in an industry, they can then be used to channrl designers' creativity to be congruent with the commercial objectives of firms in that industry. Using real-world data from the automobile industry, we show the existence of design-philosophies and illustrate methodologies for identifying and interpreting them. We also describe predictive models that allow managers to exploit the knowledge available in these design-philosophies and accurately specify a new product's manufacturing attribute values, given market input regarding the product's desired features. This total approach should save firms valuable product designing time and enable them to generate products that can be successfully produced and sold.     

Fuzzy linear programming models for NPD using a four-phase QFD activity process based on the means-end chain concept

Quality function deployment (QFD) is a customer-driven approach in processing new product development (NPD) to maximize customer satisfaction. Determining the fulfillment levels of the “hows”, including design requirements (DRs), part characteristics (PCs), process parameters (PPs) and production requirements (PRs), is an important decision problem during the four-phase QFD activity process for new product development. Unlike previous studies, which have only focused on determining DRs, this paper considers the close link between the four phases using the means-end chain (MEC) concept to build up a set of fuzzy linear programming models to determine the contribution levels of each “how” for customer satisfaction. In addition, to tackle the risk problem in NPD processes, this paper incorporates risk analysis, which is treated as the constraint in the models, into the QFD process. To deal with the vague nature of product development processes, fuzzy approaches are used for both QFD and risk analysis. A numerical example is used to demonstrate the applicability of the proposed model.     

Product line selection and pricing under a share-of-surplus choice model

Product line selection and pricing decisions are critical to the profitability of many firms, particularly in today’s competitive business environment in which providers of goods and services are offering a broad array of products to satisfy customer needs.We address the problem of selecting a set of products to offer and their prices when customers select among the offered products according to a share-of-surplus choice model. A customer’s surplus is defined as the difference between his utility (willingness to pay) and the price of the product. Under the share-of-surplus model, the fraction of a customer segment that selects a product is defined as the ratio of the segment’s surplus from this particular product to the segment’s total surplus across all offered products with positive surplus for that segment.We develop a heuristic procedure for this non-concave, mixed-integer optimization problem. The procedure utilizes simulated annealing to handle the binary product selection variables, and a steepest-ascent-style procedure that relies on certain structural properties of the objective function to handle the non-concave, continuous portion of the problem involving the prices. We also develop a variant of our procedure to handle uncertainty in customer utilities. In computational studies, our basic procedures perform extremely well, producing solutions whose objective values are within about 5% of those obtained via enumerative methods. Our procedure to handle uncertain utilities also performs well, producing solutions with expected profit values that are roughly 10% higher than the corresponding expected profits from solutions obtained under the assumption of deterministic utilities.     

A new model and solution method for product line design with pricing

Existing customer preference based product design models do not consider product prices and consumer budgets. These models assume that a purchase is based only on the satisfaction obtained from the product, irrespective of the product price and customer budget. However, when products are expensive relative to buyers' budgets, the effect of prices and budgets must be considered in addition to customer satisfaction. Most current models, moreover, assume that a low preference for one product characteristic is compensated by high preference for another, which may not hold for unacceptable levels of characteristics. For such products, we incorporate prices, budget constraints, and minimum acceptable thresholds in our model. To solve the model we develop a highly accurate, robust and efficient Beam Search (BS) based heuristic that identifies optimal or near optimal product lines. The heuristic is tested on 300 simulated problems and an application. It is also compared to a Genetic Algorithms (GA) based heuristic. We found that our heuristic worked better than the GA heuristic in identifying optimal and near optimal solutions quickly. We also give detailed examples that illustrate the heuristic and demonstrate a pricing analysis application of the model.     

Evaluating eco-efficiency with data envelopment analysis: an analytical reexamination

This paper reexamines the unintended consequences of the two widely cited models for measuring environmental efficiency—the hyperbolic efficiency model (HEM) and directional distance function (DDF). I prove the existence of three main problems: (1) these two models are not monotonic in undesirable outputs (i.e., a firm’s efficiency may increase when polluting more, and vice versa), (2) strongly dominated firms may appear efficient, and (3) some firms’ environmental efficiency scores may be computed against strongly dominated points. Using the supply-chain carbon emissions data from the 50 major U.S. manufacturing companies, I empirically compare these two models with a weighted additive DEA model. The empirical results corroborate the analytical findings that the DDF and HEM models can generate spurious efficiency estimates and must be used with extreme caution.     

An evaluation approach to engineering design in QFD processes using fuzzy goal programming models

Quality function deployment (QFD) is a product development process used to achieve higher customer satisfaction: the engineering characteristics affecting the product performance are designed to match the customer requirements. From the viewpoint of QFDs designers, product design processes are performed in uncertain environments, and usually more than one goal must be taken into account. Therefore, when dealing with the fuzzy nature in QFD processes, fuzzy approaches are applied to formulate the relationships between customer requirements (CRs) and engineering design requirements (DRs), and among DRs. In addition to customer satisfaction, the cost and technical difficulty of DRs are also considered as the other two goals, and are evaluated in linguistic terms. Fuzzy goal programming models are proposed to determine the fulfillment levels of the DRs. Differing from existing fuzzy goal programming models, the coefficients in the proposed model are also fuzzy in order to expose the fuzziness of the linguistic information. Our model also considers business competition by specifying the minimum fulfillment levels of DRs and the preemptive priorities between goals. The proposed approach can attain the maximal sum of satisfaction degrees of all goals under each confidence degree. A numerical example is used to illustrate the applicability of the approach.     

Application of fuzzy compromise solution method for fit concept selection

The modern manufacturing environment is highly turbulent so as to satisfy the dynamic needs of customers’. To enable the achievement of competitiveness in this complex business environment, newer manufacturing strategies have been introduced for enabling waste elimination and enhancing flexibility and responsiveness of systems. Fit manufacturing is a competitive manufacturing paradigm which includes lean and agile systems coupled with sustainable benefits. This article presents a study in which the concept selection in fit environment was formulated as Multi Criteria Decision Making (MCDM) problem and solved using fuzzy based compromise solution method, Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR). The selected concept design of automotive component was subjected to implementation in the case organisation.     

Fuzzy linear programming models for new product design using QFD with FMEA

Quality function deployment (QFD) is a customer-driven approach in processing new product developments in order to maximize customer satisfaction. Determining the fulfillment levels of design requirements (DRs) and parts characteristics (PCs) is an important decision problem during QFD activity processes for new product development. Unlike the existing literature, which mainly focuses on the determination of DRs, this paper proposes fuzzy linear programming models to determine the fulfillment levels of PCs under the requirement to achieve the determined contribution levels of DRs for customer satisfaction. In addition, considering the design risk, this paper incorporates failure modes and effect analysis (FMEA) into QFD processes, which is treated as the constraint in the models. To cope with the vague nature of product development processes, fuzzy approaches are used for both FMEA and QFD. The illustration of the proposed models is performed with a numerical example to demonstrate the applicability in practice.     

面向可选服务的产品服务系统配置研究

可选服务是制造企业继必选服务后获取差异化服务竞争优势的有效手段,类似于必选服务,可选服务模块的设计与选择对产品功效和企业服务成本均有影响。本文运用模糊测度研究产品模块与可选服务模块之间的相关关系,建立客户满意度最大和企业成本最小的多目标优化模型,求解不同相关关系下面向可选服务的产品服务系统配置方案;以某制造企业矿渣立磨机产品模块与可选服务模块配置为例,验证了方法的有效性。研究结果表明：产品模块与可选服务模块的相关关系会影响产品服务系统配置方案,相关关系越强,客户越在意重视度较高模块的质量和功能,市场成本报价越高,可选服务在方案配置过程中越重要。本文为企业提供面向可选服务的产品服务系统配置方案提供借鉴。     

Productivity and customer satisfaction in Swedish pharmacies: A DEA network model

This paper presents a Data Envelopment Analysis (DEA) network model that allows inclusion of customer satisfaction in efficiency and productivity measures. The network consists of a production node and a consumption node and offers flexibility in modelling the production and consumption process where a firm-specific allocation of input resources to production and customer oriented activities is allowed. The proposed model is applied on a sample of Swedish pharmacies with organizational objectives that necessitates a monitoring of efficiency and productivity as well as customer satisfaction. Estimation results from the network model and a direct productivity model (without customer satisfaction) are compared and indicate that the technical efficiency is lower under the network model. The productivity results indicate productivity progress under both models, albeit with a slower rate of change under the network model.     

Modern analysis of customer satisfaction surveys: comparison of models and integrated analysis

Customer satisfaction is a key dimension driving business outcomes and performance of processes in service and product organizations. Measuring customer satisfaction is typically based on self‐declared or interview‐based questionnaires where users or consumers are asked to express opinions on statements, or satisfaction scales, mapping out various interactions with the service provider or product supplier. The topic has gained importance in recent years with researchers proposing new models and methods for designing, implementing, and analyzing customer satisfaction surveys. This paper builds on material presented in a recent edited book entitled Modern Analysis of Customer Satisfaction Surveys (Kenett and Salini, 2011). The book provides a comprehensive exposition of a variety of models that have all been applied to the same data set by leading experts. These models generate a variety of management insights. Combining models opens up opportunities for further research and applications. Specifically, we suggest that an integrated analysis, aggregating several approaches to survey data analysis, may prove effective in increasing the information quality derived from of a customer satisfaction survey. Copyright © 2011 John Wiley & Sons, Ltd.     

Strategic capacity planning for light emitting diode (LED) supply chains across Taiwan and China

This paper describes a mixed-integer programming (MIP) model formulated for strategic capacity planning for light emitting diode (LED) makers of Taiwan, major companies in the global LED market. These firms have complex supply chains across Taiwan and China, and the region’s unique political and economic environment has created not only competitive advantages but also challenges in supply chain management: government regulations require that customer orders be accepted from Taiwan or China according to customer attributes; when conducting manufacturing, Taiwanese firms may need to transfer orders across national borders for reasons such as manufacturing technology (the required technology is available only at certain manufacturing facilities) or more efficient capacity utilization; and there are operations to be performed with specific processing requirements to follow, posing substantial challenges for planners. Motivated by the significance of these firms in the global market, we develop a MIP model with novel features to support their strategic capacity planning, covering demand and manufacturing-related decisions, including order acceptance and transfer, manufacturing starts, capacity expansion, and logistics. We illustrate the model’s performance using modified industry data in a numerical example; we also describe the potential impacts the model may create in industry applications.     

Product design and selection using fuzzy QFD and fuzzy MCDM approaches

Quality function deployment (QFD) is a useful analyzing tool in product design and development. To solve the uncertainty or imprecision in QFD, numerous researchers have applied the fuzzy set theory to QFD and developed various fuzzy QFD models. Three issues are investigated by examining their models. First, the extant studies focused on identifying important engineering characteristics and seldom explored the subsequent prototype product selection issue. Secondly, the previous studies usually use fuzzy number algebraic operations to calculate the fuzzy sets in QFD. This approach may cause a great deviation in the result from the correct value. Thirdly, few studies have paid attention to the competitive analysis in QFD. However, it can provide product developers with a large amount of valuable information. Aimed at these three issues, this study integrates fuzzy QFD and the prototype product selection model to develop a product design and selection (PDS) approach. In fuzzy QFD, the α-cut operation is adopted to calculate the fuzzy set of each component. Competitive analysis and the correlations among engineering characteristics are also considered. In prototype product selection, engineering characteristics and the factors involved in product development are considered. A fuzzy multi-criteria decision making (MCDM) approach is proposed to select the best prototype product. A case study is given to illustrate the research steps for the proposed PDS method. The proposed method provides product developers with more useful information and precise analysis results. Thus, the PDS method can serve as a helpful decision-aid tool in product design.     

PROMETHEE based sustainable concept selection

Manufacturing organisations started recognising the importance of sustainability concepts: This is due to increasing government regulations and the consciousness of the sustainable products among the customers. In general, the sustainability concepts are classified into economic, environment and social perspectives. The classification of sustainable concept concerning to manufacturing firms can be material oriented, product design oriented and manufacturing process oriented. Sustainable concept selection is a vital problem for the contemporary manufacturing organisations. The selection process includes multiple criteria, therefore a Multi-Criteria Decision Making (MCDM) method namely, PROMETHEE (Preference Ranking Organisational Method for Enrichment Evaluation) is used in the study to select the best sustainable concept considering the criteria from social, economical and natural perspectives. The study revealed that the change of material as the best orientation and it implies that the material should be done at the first stage to achieve sustainability in the case organisation.     

Optimization-Based Available-To-Promise with Multi-Stage Resource Availability

Increasingly, customer service, rapid response to customer requirements, and flexibility to handle uncertainties in both demand and supply are becoming strategic differentiators in the marketplace. Organizations that want to achieve these benchmarks require sophisticated approaches to conduct order promising and fulfillment, especially in today’s high-mix low-volume production environment. Motivated by these challenges, the Available-to-Promise (ATP) function has migrated from a set of availability records in a Master Production Schedule (MPS) toward an advanced real-time decision support system to enhance decision responsiveness and quality in Assembly To Order (ATO) or Configuration To Order (CTO) environments. Advanced ATP models and systems must directly link customer orders with various forms of available resources, including both material and production capacity. In this paper, we describe a set of enhancements carried out to adapt previously published mixed-integer-programming (MIP) models to the specific requirements posed by an electronic product supply chain within Toshiba Corporation. This model can provide individual order delivery quantities and due dates, together with production schedules, for a batch of customer orders that arrive within a predefined batching interval. The model considers multi-resource availability including manufacturing orders, production capability and production capacity. In addition, the model also takes into account a variety of realistic order promising issues such as order splitting, model decomposition and resource expediting and de-expediting. We conclude this paper with comparison of our model execution results vs. actual historical performance of systems currently in place.     

Class clustering destroys delay differentiation in priority queues

This paper considers a discrete-time priority queueing model with one server and two types (classes) of customers. Class-1 customers have absolute (service) priority over class-2 customers. New customer batches enter the system at the rate of one batch per slot, according to a general independent arrival process, i.e., the batch sizes (total numbers of arrivals) during consecutive time slots are i.i.d. random variables with arbitrary distribution. All customers entering the system during the same time slot (i.e., belonging to the same arrival batch) are of the same type, but customer types may change from slot to slot, i.e., from batch to batch. Specifically, the types of consecutive customer batches are correlated in a Markovian way, i.e., the probability that any batch of customers has type 1 or 2, respectively, depends on the type of the previous customer batch that has entered the system. Such an arrival model allows to vary not only the relative loads of both customer types in the arrival stream, but also the amount of correlation between the types of consecutive arrival batches. The results reveal that the amount of delay differentiation between the two customer classes that can be achieved by the priority mechanism strongly depends on the amount of such interclass correlation (or, class clustering) in the arrival stream. We believe that this phenomenon has been largely overlooked in the priority-scheduling literature.     

Coordination of a Supply Chain with One-Manufacturer and Two-Retailers Under Demand Promotion and Disruption Management Decisions

Increasingly, customer service, rapid response to customer requirements, and flexibility to handle uncertainties in both demand and supply are becoming strategic differentiators in the marketplace. Organizations that want to achieve these benchmarks require sophisticated approaches to conduct order promising and fulfillment, especially in today’s high-mix low-volume production environment. Motivated by these challenges, the Available-to-Promise (ATP) function has migrated from a set of availability records in a Master Production Schedule (MPS) toward an advanced real-time decision support system to enhance decision responsiveness and quality in Assembly To Order (ATO) or Configuration To Order (CTO) environments. Advanced ATP models and systems must directly link customer orders with various forms of available resources, including both material and production capacity. In this paper, we describe a set of enhancements carried out to adapt previously published mixed-integer-programming (MIP) models to the specific requirements posed by an electronic product supply chain within Toshiba Corporation. This model can provide individual order delivery quantities and due dates, together with production schedules, for a batch of customer orders that arrive within a predefined batching interval. The model considers multi-resource availability including manufacturing orders, production capability and production capacity. In addition, the model also takes into account a variety of realistic order promising issues such as order splitting, model decomposition and resource expediting and de-expediting. We conclude this paper with comparison of our model execution results vs. actual historical performance of systems currently in place.     

A variable neighborhood search heuristic for periodic routing problems

The aim of this paper is to propose a new heuristic for the Periodic Vehicle Routing Problem (PVRP) without time windows. The PVRP extends the classical Vehicle Routing Problem (VRP) to a planning horizon of several days. Each customer requires a certain number of visits within this time horizon while there is some flexibility on the exact days of the visits. Hence, one has to choose the visit days for each customer and to solve a VRP for each day. Our method is based on Variable Neighborhood Search (VNS). Computational results are presented, that show that our approach is competitive and even outperforms existing solution procedures proposed in the literature. Also considered is the special case of a single vehicle, i.e. the Periodic Traveling Salesman Problem (PTSP). It is shown that slight changes of the proposed VNS procedure is also competitive for the PTSP.