Customer Need Research Articles

Use big data to leverage customer need diversity for radical innovation

ABSTRACT Despite the growing diversity of customer needs, little empirical evidence would guide managers to leverage customer need diversity (CND) for radical innovation. Building on the knowledge-based view and dynamic capabilities, the authors explore relationships among CND, strategic flexibility, big data analytics capabilities, and radical innovation performance. With 308 survey responses from innovation managers, the authors find that CND impacts radical innovation performance via (1) mediation through strategic flexibility and (2) moderation by volume, variety, and velocity capabilities associated with big data analytics. The moderation effects of volume and velocity capabilities are positive and negative simultaneously, depending on the purpose for which CND is utilised. Therefore, to leverage customer need diversity, firms need to build strategic flexibility and exercise caution when utilising big data analytics capabilities.

A branch-and-price-and-cut algorithm for time-dependent pollution routing problem

The time-dependent pollution routing problem (TDPRP) extends the pollution routing problem (PRP) cause it captures traffic congestion at peak periods in urban transportation. It concerns planning a fleet of homogeneous vehicles to serve all customers, jointly deciding their speed on every arc and the departure time from each node such that the total cost of routes reaches a minimum. To our knowledge, all solution methods for the TDPRP are founded on (meta-)heuristics. In this study, we propose an exact branch-and-price-and-cut (BPC) algorithm for the TDPRP. The master problem is a route selection problem formulated as a set-partitioning model, and the pricing problem is a time-dependent elementary shortest path problem with resource constraints (TDESPPRC), in which the speed and start time at each customer need to be decided. We devise a tailored label-setting algorithm to handle the pricing problem, and the master problem is solved by column generation. Then, two valid inequalities are employed to tighten the lower bound, and several accelerating techniques are used to speed up the label-setting algorithm. Meanwhile, we present an analytical characterization applied to different scenarios. Finally, extensive computational experiments show our algorithm outperform a commercial MIP solver, and the optimal solutions are found for more benchmark instances using less time.

Impact of Reverse Innovation on Global Competitiveness of MNCs: The Moderating Role of Understanding Latent Customer Needs in Developed Markets

Reverse innovation (RI) is the adoption of an invention in an impoverished economy proceeding to wealthier countries. Reverse innovation has also become a brand-new growth strategy for Multinational Corporations (MNCs) to drive innovation in developing markets and further utilize the potential profit of certain developments by consequently presenting innovation not only in other market segments, but also in developed economies, providing MNCs with global sustained development. Reverse innovation studies may be conducted by extending previous ideas on innovation, internationalization, and MNC management. Thus, this study analyzes the impact of Reverse Innovation on MNCs’ global competitiveness. This study is unique because it attempts to study the moderating role of understanding latent customer needs in developed markets. Smart PLS was utilized to analyze the impact of Reverse Innovation. The database utilized in this analysis is primarily focused on the Siemens market with interviews and information provided by business customers because of their demonstrated success as well as high level of adoption in emerging economies. The PLS-SEM technique was chosen as the primary analysis tool because it is a variance-based technique. Their analysis found that performance in emerging markets requires end-to-end localization, core value discovery, and a balanced portfolio combination of both types of innovation.

Time Window Oriented IoT Vehicle Pathway Study for the Dynamically Changing Needs of E-Commerce Customers

The main dynamic truck routing problem also presents a significant difficulty in the logistics sector, which is an unavoidable development trend of the contemporary technological changing society. A dynamic vehicle routing problem with time window model is suggested by the study in order to establish an effective and low-energy dynamic response method. The fundamental concept is to disrupt the conventional strategy of static dynamic consumers responding in time slots by dividing the dynamic time window into a static time window with several time slice intervals. The study makes use of cutting-edge ideas including dynamic attitude, before-and-after time slicing, and continuous optimisation while proposing a new method for model solution to optimise dynamic vehicle route issues effectively and affordably. The study employs the Solomon optimisation dataset and runs simulation studies on the Java platform to confirm its efficacy. The experimental findings demonstrated that the optimisation technique employed in the study reduced the cost of travelling by 83.8 miles while also considerably increasing the average vehicle utilisation by 3.6%. Because driving distance cost and vehicle number cost are typically positively connected with dynamic attitude, the study employs solutions that can increase dynamic response efficiency and save money. As a result, their robustness is higher.

Channel Estimation of MC-CDMA aided with Evolutionary Algorithms

Currently wireless communication has gained great demand. In order to satisfy current need of customer’s, new technologies are required. Multi-Carrier Code Division Multiple Access (MC-CDMA) is one of the promising technologies for mobile radio communications. In this article MC-CDMA performance is evaluated by estimating the channel with the help of evolutionary algorithms. Block type pilot arrangement is used for estimating the channel with least square (LS) estimation algorithm. Matlab used for simulation

Monitoring health related quality of life in survivorship care of young adult survivors of childhood cancer using web-based patient-reported outcome measures: survivors’ and health care practitioners’ perspectives on the KLIK method

PurposeThe KLIK method is a tool to systematically monitor and discuss Health Related Quality of Life (HRQOL) in clinical practice. It has been successfully used in clinical practice in The Netherlands, and has recently been implemented in survivorship care for young adult childhood cancer survivors (CCSs). This study evaluates implementation fidelity and satisfaction of CCSs and healthcare practitioners (HCPs) with the KLIK method in survivorship care.MethodsCCSs’ HRQOL was monitored using the KLIK questionnaire (PedsQL generic 18–30 years). In a mixed-methods design, implementation fidelity was based on registrations, and user satisfaction was assessed with evaluation surveys (CCSs) and semi-structured interviews (CCSs, HCPs). Descriptive statistics and qualitative analysis methods were used.ResultsA total of 245 CCSs were eligible for the study. Fidelity was 79.2% (194/245) for registration in the KLIK PROM portal, 89.7% (174/194) for completed KLIK questionnaires, 74.7% (130/174) for its discussion during consultation. Of the eligible CCSs, 17.6% (43/245) completed the study evaluation survey. Five CCSs and HCPs were invited for an interview and participated. CCSs (7.7/10) and HCPs (7.5/10) were satisfied with the KLIK method. Reported facilitators included increased insight into CCSs’ functioning, improved preparation before, and communication during consultation, without lengthening consultation duration. Barriers included CCSs not always completing KLIK questionnaires, incomplete content of the KLIK questionnaire, and the need for customization for CCSs with cognitive disabilities.ConclusionThe KLIK method is a feasible and valuable tool to systematically monitor and discuss HRQOL in survivorship care. Integration of the KLIK method within the organization is essential, with structural support in reminding CCSs to complete questionnaires.

Effects of process parameters and scan strategy on the microstructure and density of stainless steel 316 L produced via laser powder bed fusion

The ability to produce reliable and reproducible components from 316 L using additive manufacturing is crucial to serve the need for customization, on-demand manufacturing and reduced lead times for various industries. Laser powder bed fusion is becoming widely accepted as a versatile additive manufacturing technique capable of producing near defect-free components with tailored microstructures and mechanical properties. However, despite the recent progress in process parameter selection and optimization, even a small change in any of the laser parameters, equipment and feedstock powder characteristics can influence the microstructure and subsequently the mechanical properties of the fabricated parts. The purpose of this work is to tackle the process optimization challenge through, a full factorial design of experiments approach, to systematically assess the widely adopted energy density factor to evaluate the density of the final component. A statistical approach was also followed to evaluate potential plastic anisotropy in different samples produced with various energy densities and scan strategies. Density measurements indicated that beyond laser power and scan speed, the interaction effects of the aforementioned parameters with the layer thickness and the powder size distribution have a significant effect on the sample. Microstructural features such as melt pools, grains and crystallographic texture were characterized against a range of volumetric energy densities and scan strategies represented by different angles of rotation between successive layers. Smaller angles of rotation per layer were found to decrease texture anisotropy and suppress the formation of keyhole porosity in finer and more homogenous microstructures. The assessment of plastic anisotropy in the produced samples was evaluated using microhardness measurements on all three orthogonal planes of the samples. The hierarchical microstructure of laser powder bed fusion materials induces several strengthening mechanisms, that can simultaneously be activated depending on the loading scenario, location and plane.

Identification Customer Needs of Shuttle Travel by Using Servqual and Kano Methods

Bhinneka Shuttle is a companies which is engaged in customer delivery transportation services and was founded in 1972. Throughout 2022, Bhinneka Shuttle experienced an unstable amount of revenue and did not achieve the target number of passengers. Based on the results of a comparison of the four branches of Bhinneka Shuttle outlets in Bandung City, it is known that Bhinneka Shuttle Mekar Wangi needs to get improvement priority. This is supported by the low Google review rating and many complaints about the performance of the services provided. Customer dissatisfaction and high competition in the transportation business require Bhinneka Shuttle Mekar Wangi to improve and increase the quality of its services. This study aims to identify True Customer Needs by using the integration of the Servqual method and the Kano Model so that priority attributes are obtained for improvement and become basic recommendations for designing company service quality improvements. The attribute needs were obtained by conducting a study of the literature and Voice of Customer which were classified into five Servqual dimensions, that is Reliability, Comfort, Extent of Service, Safety, and Affordability (RESCA). According to research result, 11 attributes True Customer Needs that needed to be improved which states that the services provided by Bhinneka Shuttle still does not provide satisfaction to consumers. Bhinneka Shuttle are suggested to pay more attention to weak attributes that are included in the must be category because they are basic need in company’s services.

피벗을 통한 창업기업의 Death Valley 극복 사례 분석

Most successful pivot cases in the domestic market are either promotional materials for well-known companies or cases of stable companies that can afford to fail once or twice. As a result, there is a lack of pivot research that early-stage startups can refer to in order to overcome the Death Valley. Therefore, in order for early-stage startups to overcome the Death Valley through pivoting, it is necessary to reduce the probability of pivot failure. To achieve this, research on pivot cases of early-stage startups in similar environments is needed to gain a precise understanding of pivoting and its key factors. In this study, we conducted a case analysis of five early-stage startups that are currently actively conducting their business to identify the types of pivot in domestic startups and summarize the success factors. The research results revealed that domestic startups are relatively engaged in Customer Need Pivot and Zoom-out Pivot. The prevalence of Customer Need Pivot indicates that pivoting, which reflects customers' needs, is also being applied in reality as part of the Lean Startup methodology. Moreover, it was found that startups often undergo Zoom-out Pivots, where they leverage existing resources and capabilities to add new business models in order to expand their business. The research results indicate that in crisis situations, rapid decision-making and execution, problem-solving starting from understanding customers' needs, securing resources and capabilities necessary for pivoting, modifying the direction of the business through pivoting, and increasing the chances of success are crucial. Additionally, maintaining an open attitude towards various possibilities, making correct choices, and focusing on core competencies were identified as important factors for successful pivoting.

Mixing Importance Performance, Swot and Balanced Scorecard Analysis to Develop Strategic Plan at Astrini Hospital Wonogiri

Introduction: Know what customer need in healthcare service is one of important thing for hospital to win competition in competitive era. Importance performance analysis is a method which is show us about two-dimension, importance and performance. IPA tool, it helps hospital leaders and managers to see about their hospital situation and it helps hospital management to analyze about strength and weakness point their hospital and make balance scorecard to achieve hospital strategic plan appropriately. Purpose: To see about using IPA tool and mix it with SWOT and balanced scorecard in develop strategic plan policy at Astrini Hospital Wonogiri Method: we measuring IPA with servperf model five-dimension, tangible, responsiveness, reliability, empathy and safety. Sample of measurement are inpatient patient at Astrini Hospital (25 – 55 years old), and total respondent are fifteen people (n=15), SWOT and Balanced scorecard analysis use in focused group discussion from hospital management based on IPA measurement. Result and discussion: we get four of five dimensions are good and patient is satisfied with hospital performance overall. Tangible dimension is only one with low point (-0,2) and it mean our patient not satisfied with hospital performance. Each dimension, it has some question and it can be hospital leader and manager consideration during analyze SWOT (strength, weakness, opportunity, and threat) & BSC (balanced score card) to develop a hospital strategic plan. Conclusion: Hospital management is must to know what their patient need and want when they come to hospital to seek medical service. Using IPA, and then mix its with SWOT and balanced scorecard can be consideration for hospital management to analyze existing condition and then analyze hospital strategic plan further. Directly scoring from hospital patient, it can be an indicator is the hospital already give them appropriate medical service what they need and expect from hospital.

Collaborative Planning in Non-Hierarchical Networks—An Intelligent Negotiation-Based Framework

In today’s competing business market, companies are constantly challenged to dynamically adapt to customer expectations by diminishing the time response that goes from the beginning of the business opportunity to the satisfaction of the customer need. Simultaneously, there is increased recognition of the advantages that companies obtain in focusing on their core business and seeking other competencies through partnerships with other partners by forming collaborative networks. These new collaborative organizational structures require a new set of methods and tools to support the management of manufacturing processes across the entire supply chain. The present paper addresses the collaborative production planning problem in networks of non-hierarchical, decentralized, and independent companies. By proposing a collaborative planning intelligent framework composed of a web-based set of methods, tools, and technologies, the present study intends to provide network stakeholders with the necessary means to responsively and efficiently address each one of the market business opportunities. Through this new holistic framework, the managers of the networked companies can address the challenges posed during collaborative network formation and supply chain production planning.

Modeling System Configurations Over Time

AbstractOne of the most troublesome and challenging parts of systems engineering is that systems keep changing. Some changes are planned as part of a phased system rollout. Others can occur because of changes in the environment, technology, customer need, unexpected emergent properties, competition, discovered system vulnerabilities, etc. Regardless of why, the systems engineer needs to document these configurations as part of a digital thread leading to physical systems that will be manufactured and installed. When using SysML as part of MBSE, there are advantages and disadvantages regarding the modeling variability using inheritance and other structural modeling techniques. Elements in a so‐called superclass will be inherited by all members. This is advantageous for additive systems but can cause issues when configurations require removal of systems. This paper will examine system configuration over time showing how to model configurations and changes. It will also look at other techniques such as Product Line Engineering (PLE) with OVM and Feature Based Modeling.

A Systems Engineering Approach to Reducing Mis‐pulls and Misplaced Trailers for Trucking Fleets

AbstractHeavy vehicle fleets can have more than three times the number of trailers than tractors, but tractors host most of the vehicle network and tracking systems. Tractor location is readily available through telematics systems, yet trucking operations may pull the wrong trailer during operations because of the lack of trailer location information. The lack of automated trailer location data can lead to lost or mis‐placed trailers, which compromises the service of the product being shipped. Adding dedicated telematics systems to individual trailers is cost prohibitive. Therefore, we propose leveraging the tractor's existing location data with unique identifiers from the trailer to geo‐reference trailers. The goal of the proposed system is to use existing truck and trailer hardware (i.e. the braking system controllers) to identify the trailer serial numbers to the truck‐tractor, which creates geo‐referenced tractor‐trailer combinations. This work follows a system engineering approach for a trailer tracking system design beginning with a thorough review of the problem and customer need, generating concise requirements, conducting a tradeoff study of potential solutions, and demonstrating the proposed solution on a test bench. A systems engineering approach following the International Organization for Standardization (ISO) 15288 and International Council on Systems Engineering (INCOSE) technical processes is used for this design and development effort. This work demonstrates a novel approach, delivering a trailer tracking capability fielded through minimal changes to existing hardware and software. This automated trailer location system would reduce mis‐pulled and temporarily misplaced trailers, thus saving time and money for fleet operations worldwide.

Computational Mechanics of Form-Fitting 3D-Printed Lattice-Based Wrist-Hand Orthosis for Motor Neuron Disease.

Motor neuron disease (MND) patients often experience hand-wrist muscle atrophy resulting in severe social consequences and hampering their daily activities. Although hand-wrist orthosis is commonly used to assist weakened muscles, its effectiveness is limited due to the rapid progression of the disease and the need for customization to suit individual patient requirements. To address these challenges, this study investigates the application of three-dimensional (3D) printing technology to design and fabricate two lattice structures inspired by silkworm cocoons, using poly-ε-caprolactone as feedstock material. Finite element method (FEM) analysis is employed to study the mechanical behavior, enabling control over the geometric configuration incorporated into the hand-wrist orthosis. Through tensile displacement and three-point bending simulations, the stress distribution is examined for both lattice geometries. Geometry-1 demonstrates anisotropic behavior, while geometry-2 exhibits no strict directional dependence due to its symmetry and uniform node positioning. Moreover, the biocompatibility of lattices with human skin fibroblasts is investigated, confirming excellent biocompatibility. Lastly, the study involves semi-structured interviews with MND patients to gather feedback and develop prototypes of form-fitting 3D-printed lattice-based hand-wrist orthosis. By utilizing 3D printing technology, this study aims to provide customized orthosis that can effectively support weakened muscles and reposition the hand for individuals with MND.

Systematic knowledge-based product redesign: An empirical study of solar power system for unmanned transport ship

Product redesign is widely required in the process of product innovation, which greatly depends on a series of innovative theories of product. Traditional product redesign can identify functional requirements from customer needs by functional analysis and utilize inventive principles of TRIZ to address the problems. However, the relationships between the customer needs (CNs), engineering parameters (EPs), and inventive principles (IPs) has not been explored, which may limit and hinder the opportunity for product innovation. To improve the design efficiency, Random Forest (RF) is introduced in this method to mine the relationship between EPs and IPs by researching many cases for design knowledge. A systematic redesign process is proposed to apply the design knowledge. Firstly, functional analysis is used to decompose customer needs (CNs) into product functions. And then, the effect-based approach which is the application of scientific laws is used to transform functions into EPs. Secondly, IPs are recommended based on EPs by a trained RF model. Finally, the specific solution is generated from the problem domain using the analogy design based on the Su-Field model. The effectiveness of the method is verified in the redesign of solar power systems for unmanned transport ships.

GoPrintBot: An Interactive E-Commerce for Online Printing Services

GoPrintBot is an innovative endeavor focused on creating an interactive e-commerce chatbot tailored specifically for online printing services. The primary objective is to enhance the customer experience by offering a seamless and efficient ordering process, eliminating the need for customers to navigate complex websites or wait for customer service representatives. This study addresses common issues encountered in traditional e-commerce platforms, including unintuitive navigation, ineffective search and filter systems, and slow response times for customer inquiries. The proposed solution involves developing a chatbot system that will revolutionize the online printing industry, significantly improving the customer experience and operational efficiency. The methodology employed encompasses comprehensive requirements gathering, followed by the design and development of the chatbot system using established System Development Life Cycle (SDLC) methodologies. GoPrintBot serves as a prototype, showcasing the potential of chatbots to streamline the online printing process and provide personalized and efficient services. The findings of this study have significant implications for businesses that prioritize customer satisfaction and aim to optimize their online printing services.

Online Fake Review Detection Using SVM

Abstract: After the pandemic, our overall life is changing and challenging that’s why our demand is changing now, we are focused on wellness, sustainability, technology, and the gig economy because of these trends we can observe the reflection in the changing desired need and limitation of seller and customer. Many customers can post a review on any website after making a purchase. Whether it’s an online purchase or an offline retail purchase. When customers buy a product online, they check the product reviews. This is very important for today’s e-commerce product decisions. There is a financial gain associated with writing fake reviews, which is why there has been a significant increase in misleading statements about certain product reviews on websites. Misleading reviews are dangerous reviews. Positive product reviews can attract customers and increase sales. Negative product reviews can reduce demand for that product and reduce sales. These misleading reviews are dangerous to your product’s reputation. In this paper, we use machine learning algorithms which are SVM Support vector machines, which are one of the most popular supervised learning algorithms used for both classification and regression problems. However, it is mainly used for machine-learning classification problems

Enhancing sustainable supply chain performance by adopting sustainable lean six sigma-Industry 4.0 practices

PurposeThe Sustainable Lean Six Sigma (SLSS) adoption approach, advancements in Internet technologies and the use of Industry4.0 technologies has resulted in faster customer need fulfilment. The Industry4.0 technologies have resulted in a new paradigm where strategic and operational decisions are in favour of profitability and long-term viability. The purpose of this study is to identify Industry4.0-SLSS practices and sustainable supply chain performance metrics, as well as to develop a framework for decision-makers and managers to make supply chains more sustainable.Design/methodology/approachThe 33 Industry4.0-SLSS practices and 24 performance metrics associated with the sustainable supply chain are shortlisted based on extensive literature review and expert opinion. The Pythagorean Fuzzy Analytical Hierarchy Process (PF-AHP) approach is used to evaluate the weights of Industry4.0-SLSS practices after collecting expert panel opinions. The Weighted Aggregated Sum Product Assessment (WASPAS) methodology used these weights to rank performance metrics.FindingsAccording to the results of PF-AHP, “Product development competencies (PDC)” are first in the class of major criteria, followed by “Advanced technological competencies (ATC)” second, “Organisational management competencies (OMC)” third, “Personnel and sustainable competencies (PSC)” fourth and “Soft Computing competencies (SCC)” fifth. The performance metric “Frequency of NPD” was ranked first by the WASPAS method.Research limitations/implicationsThe proposed paradigm helps practitioners to comprehend Industry4.0 technology and SLSS practices well. The identified practices have the potential to boost the sustainability and supply chain's performance. Organizational effectiveness will benefit from practices that promote a sustainable supply chain and the use of developing technology. Managers can evaluate performance using performance metrics that have been prioritized.Originality/valueThe present study is one of the unique attempts to establish a framework for enhancing the performance of the sustainable supply chain. The idea of establishing Industry4.0-SLSS practices and performance measures is the authors' original contribution.

A Case Study On Customer Satisfaction Towards Online Banking In Bangladesh

Today's technological era has made all daily tasks much easier than they were in the past. Everything has been digitized thanks to technology. A further one among them is banking. Technology has resulted in many improvements in the banking industry. Every task in the banking sector is digitized, from account opening to money transfers through online banking, eliminating the need for customers to visit banks and wait in long queues. This makes work for both customers and banking institutions easy, efficient, and quick. Online banking also encourages competition among banks to offer their customers better services and draw in a growing number of customers. to check the level of customer satisfaction. 500 randomly chosen and conveniently placed individuals are surveyed to achieve this. The result of the research shows that Privacy & Security, Online Bill Pay, Fund Transfer, any time anywhere use, Update information, delivers services and save time and energy are directly related to Customer Satisfaction towards Online Banking in Bangladesh.

Effects of millennials willingness to pay on buying behaviour at ethical and socially responsible restaurants: Serial mediation analysis

This study empirically examines millennials' buying behaviour at restaurants undertaking Corporate Social Responsibility activities by testing the effects of willingness to pay on buying behaviour. Using Hayes’ serial mediation PROCESS model, the study analyses the direct and indirect effects of millennials’ willingness to pay on their buying behaviour at ethical and socially responsible restaurants using data from 212 millennials in North-East Scotland. Results showed that willingness to pay has significant direct and indirect effects on buying behaviour. The mediation effect of environmental concern was not supported. The serial mediation analysis showed that environmental concern, social influence, and personal norms jointly mediated the effects of willingness to pay on buying behaviour. The proposed serial model suggests that only direct measure of willingness to pay on buying behaviour is insufficient for restaurants to respond to millennials’ expectations, providing empirical evidence on the need for customer's engagement as businesses emerge from covid-19.