New Product Development Process (NPD) is a key aspect of launching new and innovative products in the market. Many products fail in the market because of technical risks, financial risks and product development time risks. It is very important to understand the overall risk factors associated with different stages of product development so that risks can be mitigated effectively. This paper presents a methodology to understand the risk associated with the initial stages of NPD. Design flexibility is higher in initial design stages requiring minimum redesign efforts and costs. It is a great opportunity to deal with risk factors and uncertainties in initial design stages than the later design stages. Product development costs in initial stages are around 5 to 10 percent but impact is 70 to 80 percent so exploration assessment in initial stages of NPD can be hugely beneficial. Stage-wise risk assessment will also provide the details of risk associated with each stage, which will be helpful in implementing appropriate mitigation strategies. Since transition from one stage to another stage of NPD is independent of the previous stage, different NPD stages can be easily expressed by the transition state of the Markov process. In this paper, the Markov process has been used for the risk assessment of initial stages of NPD, keeping mitigation strategies in mind. The three initial stages of NPD considered in this study include the concept design, detailed design and integration & testing stages. This paper also explores a method by integration of quality function deployment (QFD) and Markov process, to understand risk patterns associated with several complete design solutions (CFDs). By using QFD, the mapping between customer requirements can be reflected into risk assessment of complete design solutions (CFDs). This methodology has been demonstrated by a case study on Coffee Maker.

A hybrid batch fabrication plan involving an outsourcing option is often established to deal with the in-house capacity constraint and/or meet timely demand with a reduced cycle time. Besides, the occurrences of unpredictable equipment malfunction and imperfect product quality should also be effectively managed during in-house fabrication to meet the production schedule and the required quality level. To address these concerns, we examine a hybrid economic production quantity (EPQ) problem with an unreliable machine and quality reassurance; wherein, an outside provider helps supply a portion of the batch at a requested timing and desirable quality, but at the price of a higher than in-house unit cost. Corrective action is performed immediately when a Poisson-distributed breakdown occurs. Once the equipment repairing task completes, the interrupted lot’s fabrication resumes. Random nonconforming products are identified, and the re-workable items among them are separated from the scraps. A rework task follows the regular process in each cycle at an extra cost. A portion of reworked items fails and are scrapped. A model portraying the problem’s characteristics is built, and an optimization methodology is utilized to find the optimal runtime solution to the problem. A numerical example reveals our result’s applicability, and specific managerial implications are suggested.

This article originates from an experimental program and nonlinear finite element analysis aimed at examining how pozzolanic concrete influences the behavior of RC shear walls with openings. To achieve this, stress-strain diagrams, and the elastic modulus of 33 cylindrical concrete specimens, each containing varying percentages of silica fume and zeolite (ranging from 0% to 25%), were evaluated. The impact of silica fume and zeolite pozzolans on the ductility and load-bearing capacity of RC shear walls with openings was explored. This was done by analyzing the mechanical properties of the specimens and integrating them into the analytical model. Subsequently, a shear wall with conventional concrete was simulated using the finite element method (FEM). The study delved into the effects of substituting the initial concrete with pozzolanic concrete within the shear wall. Additionally, it investigated the simultaneous reduction in the diameter of the reinforcing bars employed, all in the pursuit of attaining the optimal design for these walls. The findings demonstrated that employing pozzolanic concrete in shear walls, coupled with a balanced configuration of rebar, led to heightened ductility, improved energy absorption, and an enhanced load-bearing capacity for the walls.

Production planners today must simultaneously face with the time and quality demands of various goods externally and meet limited capacity internally. This study presents a two-stage delayed- differentiation multiproduct model that considers the outsourcing options for common parts, overtime strategy for end products, and quality reassurance to assist in making fabrication runtime decisions that are cost-effective. Stage one produces all necessary common intermediate components for end products. To reduce stage one’s utilization/uptime, this study adopts a partial outsourcing option. Stage two uses an overtime strategy to fabricate end products that further shorten the uptime. The production processes in both phases are assumed to be imperfect. This study employs the reworking/scrapping of random faulty items to reassure product quality. The researchers build a model to depict the proposed problem’s characteristics and used the mathematical modeling, analysis, and optimization approach to determine the best rotation cycle length that minimizes the system’s expenses. Further, in this study, the researchers provide sensitivity analyses and a numerical illustration, which validate the result’s applicability and exhibit its capability. This result contributes to practical multiproduct-fabrication by (1) deriving the optimal manufacturing policy for a delayed-differentiation multiproduct system with dual uptime reduction policies and quality reassurance; and (2) offering a decisional model that allows production planners to explore the collective/separate effect of a quality-ensured and dual uptime reduction strategy on a problem’s operating policy and crucial system performance indicators, which assists in cost-effective decision-making.

Bin packing problem (BPP) is a combinatorial optimization problem with a wide range of applications in fields such as financial budgeting, load balancing, project management, supply chain management. Harmony search algorithm (HSA) is widely used for various real-world and engineering problems due to its simplicity and efficient problem solving capability. Literature shows that basic HSA needs improvement in search capability as the performance of the algorithm degrades with increase in the problem complexity. This paper presents HSA with improved exploration and exploitation capability coupled with local iterative search based on random swap operator for solving BPP. The study uses the despotism based approach presented by Yadav et al. (2012) [Yadav P., Kumar R., Panda S.K., Chang, C. S. (2012). An intelligent tuned harmony search algorithm for optimisation. Information Sciences, 196, 47-72.] to divide Harmony memory (HM) into two categories which helps to maintain balance between exploration and exploitation. Secondly, local iterative search explores multiple neighborhoods by exponentially swapping components of solution vectors. A problem specific HM representation, HM re-initialization strategy and two adaptive PAR strategies are tested. The performance of proposed HSA is evaluated on 180 benchmark instances which consists of 100, 200 and 500 objects. Evaluation metrics such as best, mean, success rate, acceleration rate and improvement measures are used to compare HSA variations. The performance of the HSA with iterative local search outperforms other two variations of HSA.

E-commerce is one of the industries most affected by big data, from collection to analytics in the highly competitive market. Previous research on big data analytics in E-commerce focused only on particular applications, and there is still a gap in presenting a framework to evaluate big data applications from a challenges-values point of view. This study employs a three-phase methodology to evaluate big data applications in E-commerce with respect to big data challenges and values using a hybrid multi-criteria decision-making technique that combines BWM and fuzzy TOPSIS. The results showed process challenge and the strategic value obtained the highest weight for challenges and values criteria. Financial fraud detection is relatively the most challenging, and online review analytics is the most valuable application of big data in E-commerce among identified applications. Evaluating big data applications based on cost and benefit criteria is practical for E-commerce managers and experts to make decisions on implementation priorities to overcome the challenges and make the most of values. Moreover, the proposed approach is not only limited to big data analytics in E-commerce and can also be applied in other industries to evaluate emerging technology applications.

Despite the numerous advantages that different mobile payments can provide, their acceptance, and adoption rates are still relatively low. This study aims at investigating mobile payments and demonstrates how drivers and barriers that influence behavioral intentions to use mobile payments interact and support one another by combining the theory of planned behavior (TPB) and the innovation resistance theory (IRT). A self-administered online survey was employed to gather data from 341 users of mobile payments in the State of Kuwait. To test the proposed model and its hypotheses, responses were analyzed using a partial least square structural equation modeling approach (PLS-SEM). The results show that usage, value, risk, and tradition resistance-related factors are significant barriers towards behavioral intentions to use mobile payments, while the image barrier is insignificant. The findings also affirmed that perceived behavioral control and attitudes motivate and influence consumers’ behavioral intentions; however, the subjective norm was non-significant. The study’s findings have significant implications for scholars, mobile payments’ service providers, marketers, policymakers, and banks.

Variety, quality, and rapid response are becoming a trend in customer requirements in the contemporary competitive markets. Thus, an increasing number of manufacturers are frequently seeking alternatives such as redesigning their fabrication scheme and outsourcing strategy to meet the client’s expectations effectively with minimum operating costs and limited in-house capacity. Inspired by the potential benefits of delay differentiation, outsourcing, and quality assurance policies in the multi-item production planning, this study explores a single-machine two-stage multi-item batch fabrication problem considering the abovementioned features. Stage one is the fabrication of all the required common parts, and stage two is manufacturing the end products. A predetermined portion of common parts is supplied by an external contractor to reduce the uptime of stage one. Both stages have imperfect in-house production processes. The defective items produced are identified, and they are either reworked or removed to ensure the quality of the finished batch. We develop a model to depict the problem explicitly. Modeling, formulation, derivation, and optimization methods assist us in deriving a cost-minimized cycle time solution. Moreover, the proposed model can analyze and expose the diverse features of the problem to help managerial decision-making. An example of this is the individual/ collective influence of postponement, outsourcing, and quality reassurance policies on the optimal cycle time solution, utilization, uptime of each stage, total system cost, and individual cost contributors.

Investment in non-fungible tokens (NFTs) has decreased dramatically over the past two years, despite the financial value and potential importance of NFTs for the future of the economy and the current decentralized marketplaces. This study investigated the barriers influencing customers' resistance to investing in NFTs using the innovation resistance theory (IRT) components such as usage barriers, value barriers, risk barriers, tradition barriers, and image barriers. The data was gathered from 375 investors via an online questionnaire. To assess and evaluate the suggested model and its hypotheses, responses were investigated using a partial least square structural equation modeling approach (PLS-SEM). The findings indicate that the five resistance-related barriers are all substantial deterrents to investing in NFTs. The usage barrier was the most significant barrier, whereas the value barrier was the least significant. The study's findings have far-reaching implications for academics, NFTs’ marketplaces, policymakers, and investors.

Artificial Intelligence (AI) is a motivation for full usage of autonomous driving. Many have predicted that autonomous technology would significantly disrupt the transportation industry. This research examines how autonomous driving might impact and disrupt the ridesharing industry and their drivers. The hypothesis is that autonomous vehicles (AV) will negatively impact the ridesharing industry. To examine the full effects of this disruption, we researched current literature on driverless technology cars and the ridesharing industry. Factors examined include: current economics of drivers and vehicles, public perception and acceptance, technological readiness, collaborations, regulations, and liability. Key findings from a host of resources were tabulated to build a case for the proposed hypothesis. The results provide a more comprehensive timeline estimate, predicted $0.75 cost estimate per mile by 2040, and documented the collaboration figure among the players that shows the significant investments across different industries. This research shows that the ridesharing industry’s current business model is due for a significant disruption by autonomous driving capabilities. Drivers in the ridesharing industry might likely suffer the most, however not for at least another decade or so. There are many independent factors, which must be further scrutinized to develop a more comprehensive understanding as to the velocity of this disruption. Findings from this study would be applicable while evaluating the future of autonomous vehicles.