Key Product Characteristics Research Articles

Effects of Conservation Tillage on Agricultural Green Total Factor Productivity in Black Soil Region: Evidence from Heilongjiang Province, China

The implementation of conservation tillage is crucial for the preservation and utilization of black soil. This study examined 297 new agricultural management entities in five pilot counties in the black soil region of northeast China. Using the SBM-Undesirable model, this study measured and evaluated the agricultural green total factor productivity (AGTFP) of these entities. We further employed the Tobit model to explore the impact of conservation tillage on the AGTFP. The findings revealed that the average AGTFP value of the sample entities was 0.4364, indicating a generally low degree of AGTFP that exhibited significant variation. Improvement in input indicators (such as machinery) and undesirable output indicators (such as net carbon emissions) was particularly needed. Additionally, conservation tillage had a significant positive impact on AGTFP, with a higher number of applied technologies correlating with increased productivity. Material subsidies for conservation tillage offered greater direct cost relief and had a stronger positive effect on AGTFP in comparison with cash subsidies. Furthermore, apart from policy factors, key production and operation characteristics—such as access to agricultural materials—also significantly influenced AGTFP. The results of this study offer a valuable decision-making framework and scientific reference for countries in black soil regions worldwide, enabling them to enhance the conservation and sustainable utilization of this vital resource.

Technological variations, microbial diversity and quality characteristics of maize ogi used for akpan production in Benin

Fermented maize starch, called ogi in Benin, is used for preparing akpan, a traditional yoghurt-like food that contributes to the food and nutrition security of its consumers. Current ogi processing technologies used by two socio-cultural groups of Benin, namely the Fon and the Goun, and aspects of the quality of the fermented starches were studied to assess the current state-of-the-art, explore changes in key product characteristics over time and identify priorities for follow-up research to increase product quality and shelf life. A survey on processing technologies was conducted in five municipalities in south Benin and samples of maize starch were collected, which were analysed after the fermentation required to obtain ogi. Four processing technologies were identified, two from the Goun (G1, G2) and two from the Fon (F1, F2). The main difference between the four processing technologies was the steeping procedure used for the maize grains. The pH of the ogi samples ranged between 3.1 and 4.2, with the highest values for G1 samples, which also contained relatively higher concentrations of sucrose (0.05–0.3 g/L) than F1 samples (0.02–0.08 g/L), and lower citrate and lactate concentrations (0.2–0.3 and 5.6–16.9 g/L, respectively) than F2 samples (0.4–0.5 and 14–27.7 g/L, respectively). Fon samples collected in Abomey were particularly rich in volatile organic compounds and free essential amino acids. Members of the genera Lactobacillus (8.6–69.3%), Limosilactobacillus (5.4–79.1%), Streptococcus (0.6–59.3%) and Weissella (2.6–51.2%) dominated the bacterial microbiota of ogi with a significant abundance of Lactobacillus spp. in Goun samples. Sordariomycetes (10.6–81.9%) and Saccharomycetes (6.2–81.4%) dominated the fungal microbiota. The yeast community of ogi samples mainly consisted of the genera Diutina, Pichia, Kluyveromyces, Lachancea and unclassified members of the Dipodascaceae family. Hierarchical clustering of metabolic data showed similarities between samples from different technologies at a default threshold of 0.05.No obvious trend in the composition of the samples’ microbial communities reflected the clusters observed for the metabolic characteristics. The results indicate that beyond the general impact of the use of Fon or Goun technologies on fermented maize starch, the individual contribution of processing practices warrants study, under controlled conditions, to determine the drivers of difference or similarity between maize ogi samples to further contribute to improving product quality and shelf life.

Ensuring sustainability in sensory sciences

Ensuring sustainability in sensory sciences

Managerial ability and trade credit

AbstractWe examine whether and how managerial ability affects trade credit (accounts payable, in particular). The empirical analyses show that firms with higher managerial ability are associated with an increase in trade credit received. We also find that the impact of managerial ability on accounts payable is stronger for firms having poorer credit quality or more binding financial constraints. Our findings are robust to alternative measures of key variables, financial crises, product characteristics, and endogeneity concerns.

Simulation Model of an Oil Refinery with a Complex Scheme of Equipment Interaction

The article presents the developed simulation model of an oil refinery. The feature of model is the use of agent-based and discrete-event modelling methods. Another feature is applying heuristic optimization technique to find the best values of key production and resource characteristics of oil refinery equipment interacting within the framework of a complex production scheme to build a refinery plant that provides a deep refining level. At the same time, the objective function of the system is the profit of refinery plant, which takes into account both the sale revenue of petroleum products and operating expenditure. The proposed model is implemented in the AnyLogic system and allows, in particular, maximising the profit of refinery plant at various restrictions, including the volumes of petroleum products supplies required to perform contractual obligations at specified times.

Optimal Subsidy Schemes and Budget Allocations for Government‐Subsidized Trade‐in Programs

Applications of government subsidies to speed up consumer trade‐ins of used products can be commonly observed in practice. This study investigates the design of such trade‐in subsidy programs and aims to provide implementable insights for practice. In particular, we focus on two open problems in the literature, (i) how to optimally allocate the subsidy budget among the multiple products covered by the trade‐in program, and (ii) how to most effectively utilize the assigned budget to incentivize consumer trade‐ins for each product. We develop a three‐stage Stackelberg game model that captures the essence of the interaction between the government's subsidy decision, the manufacturer's trade‐in rebate decision, and the consumer's product replacement decision. We show that a sharing subsidy scheme under which the government subsidy is proportional to the manufacturer's rebate is more effective in encouraging consumer trade‐ins than fixed‐amount subsidies. Moreover, a product with a higher environmental impact, a larger market size, a longer lifespan, or a lower value to consumers typically demands a larger subsidy budget allocation. We further use our results to derive a simple proportional budget allocation rule that can provide robust and near‐optimal performance. We illustrate our results by a case study based on the “old‐for‐new" program in China that subsidizes home appliance trade‐ins. Our results indicate that policy makers should pay attention to the correlation between government subsidies and manufacturer's rebate as well as key product and market characteristics when designing a subsidy scheme for trade‐in programs.

Variation management of key control characteristics in multistage machining processes considering quality-cost equilibrium

In multistage machining processes (MMPs), variations from key control characteristics (KCCs) continue to propagate and eventually accumulate to deviations in key product characteristics (KPCs). Therefore, the variation control of KCCs is significant to ensure the final product quality. In this paper, a variation management framework for KCCs in MMPs is established to address this issue. The new concept of variation management consists of process-oriented tolerancing and maintenance planning, and the optimal variation management strategy for each KCC is assigned based on its impact to the manufacturing system. The proposed framework deduces corresponding KCC variation distributions for previously unresearched locating schemes, thereby expanding the application scenarios of this method. For the quality specification constraints, geometric tolerances are integrated for the first time beside traditional dimensional tolerances, which expand the error scale of quality control. The modified Chebyshev goal programming (MCGP) approach is adopted to find the equilibrium point between quality and cost effectively. The superiority of the proposed method is verified by a case study of an automotive engine cylinder block MMP. The results show a remarkable improvement on the manufacturing system performance in terms of quality and cost.

Fault diagnosis for underdetermined multistage assembly processes via an enhanced Bayesian hierarchical model

Previous works have shown that only relying on measurement data is generally insufficient to identify root causes of the dimensional variation for the complex manufacturing system. It is also well known that for underdetermined multistage assembly processes (MAPs), the number of measurements is less than that of process errors so that the traditional methods are not available for the variation source identification. Therefore, there exists a substantial challenge for fault diagnosis of underdetermined MAPs, especially for the case with multiple fault patterns. To tackle this problem, a novel approach that integrates statistical analysis with domain knowledge is proposed in this paper. First, the variation propagation model is employed to reveal the inherent relationship between key control characteristics and key product characteristics, and then the corresponding variance model is constructed to interpret process faults by means of the abnormal variance of process errors. Considering that the probability of less process faults is far higher than that of more process faults in MAPs, the problem of fault diagnosis is further transformed into searching the sparse solution of abnormal variance changes for process faults. Afterwards, based on the non-negative property of the covariance matrix, an enhanced Bayesian hierarchical model is developed to favor sparse estimation of the variance for the underdetermined system in MAPs. Finally, experimental results of both the numeric simulation and practical case study demonstrate the proposed diagnosis methodology can effectively identify the process faults for different patterns with system noise.

Epidemiological Impact of SARS-CoV-2 Vaccination: Mathematical Modeling Analyses.

This study aims to inform SARS-CoV-2 vaccine development/licensure/decision-making/implementation, using mathematical modeling, by determining key preferred vaccine product characteristics and associated population-level impacts of a vaccine eliciting long-term protection. A prophylactic vaccine with efficacy against acquisition (VES) ≥70% can eliminate the infection. A vaccine with VES <70% may still control the infection if it reduces infectiousness or infection duration among those vaccinated who acquire the infection, if it is supplemented with <20% reduction in contact rate, or if it is complemented with herd-immunity. At VES of 50%, the number of vaccinated persons needed to avert one infection is 2.4, and the number is 25.5 to avert one severe disease case, 33.2 to avert one critical disease case, and 65.1 to avert one death. The probability of a major outbreak is zero at VES ≥70% regardless of the number of virus introductions. However, an increase in social contact rate among those vaccinated (behavior compensation) can undermine vaccine impact. In addition to the reduction in infection acquisition, developers should assess the natural history and disease progression outcomes when evaluating vaccine impact.

The influence of extrinsic product attributes on consumers’ food decisions: review and network analysis of the marketing literature

ABSTRACT The paper synthesises the existing research on the influence of key product characteristics on the consumers’ food decision-making. By applying network analysis on a sample of 233 empirical studies from the last three decades, the paper demonstrates how marketing-specific variables are embedded in a network of other predictors. The paper also analyses network structure and density using well established measures. The results show that there is still a lack of research concerning the interplay between marketing-relevant extrinsic product attributes (e.g. price, brand, labelling, country of origin) and intrinsic food attributes, policy-related factors, as well as aspects of the proximal and distal environment. The paper identifies gaps in the marketing literature and derives research propositions. Additionally, implications for marketing practice are developed.

How product characteristics can guide measures for resource efficiency — A synthesis of assessment studies

A circular economy aims at decoupling value creation from resource throughput. For circular economy to contribute to environmental and resource improvements, there is need for critical assessments regarding in what general situations, beyond individual cases, solutions may lead to improvements. On the product-level, there is need for synthesized knowledge accounting for a wide range of contexts and environmental impacts. We investigate what resource efficiency (RE) measures result in reduced physical flows and environmental impacts, depending on the characteristics of products and their life cycles. The study is limited to physical measures on a product system level, irrespective of manner of implementation. A library of comparative assessments (primarily life cycle assessments and material flow analyses) was built, covering a wide range of products and RE measures. A framework was formulated for analysing for which product characteristics a measure tends to improve RE, and under which contexts there are trade-offs to take into account. For example, sharing of products is best suited for durable and infrequently used products that tend not to reach their full technical lifetime. A trade-off is that sharing can increase transportation for accessing shared stock. The identified key product characteristics were: whether products are consumable or durable, active or passive, typically used for their full technical lifetimes or discarded before being worn out, the product’s frequency of use and whether function remains at a product’s end of use. Pace of development matters for suitability of measures for active, durable products, while complexity is relevant for restorative measures and recycling.

Potential of circular economy implementation in the mechatronics industry: An exploratory research

Abstract This paper investigates the current state of design in the mechatronic industry. First, based on a literature overview, key circular product characteristics are identified, namely modularity and robustness. Then, a survey has been distributed to practicing mechatronic engineers. Based on the survey, it is found that mechatronic systems are currently designed for long-term circular strategies, while the end-of-life is rarely considered. Furthermore, it is found that one issue in designing mechatronic devices is that they are prone to electronics obsolescence, and that safety regulations may prevent to design for circularity. Moreover, it is reported that customers do not currently require circular aspects in the mechatronic products and thus there is no incentive to designs according to circular principles. Finally, the paper discusses on new research ways of improving the circularity aspect of mechatronic devices, such as by facilitating remanufacturing or by using distributed system.

Variation Simulation and Diagnosis Model of Compliant Block Assembly Considering Welding Deformation

This article proposes a variation simulation and diagnosis model for ship block assembly processes considering the effects of welding distortion. The welding process and the deformation pattern affecting the final shape of a block assembly are diagnosed. Prior studies on welding distortion mainly focused on mitigation methodologies. In this research, welding distortion is regarded as the main cause of geometric variation in parts. In addition, how geometric variations are accumulated throughout multiple assembly processes is mathematically modeled. The variation simulation model is based on a state space equation, where variations of previous stages are propagated to the current stage. The diagnosis model predicts the quantitative effect of each variation source on the final assembly's geometrical variation, based on a normal equation and designated component analysis. The proposed model is simulated with FEM (Dassault Systèmes Americas Corp., Waltham, MA) and MATLAB (Mathworks (https://www.mathworks.com/), Massachusetts, United States) replicating a realistic block assembly process for validation. The model can effectively simulate the propagation of welding distortion and quantitatively diagnose variation patterns and welding processes. 1. Introduction Analysis, management, and variation diagnostics are some of the important aspects of the production process. These have been mainly studied in mass production processes such as in the automobile industry. Mantripragada and Whitney (1999) and Whitney (2004) proposed a tolerance analysis method for the multistage rigid body assembly using a state space equation. Huang et al. (2006a, 2007) proposed a ship block tolerance model for the single and multiple stage variation propagation of a rigid body model. Liu and Hu (1995,1997) proposed a compliant assembly model using FEM, called the method of influence coefficients (MIC). Govik et al. (2012) proved MIC using an FEM simulation. Variation propagation in a multiple stage process while considering a compliant assembly has been proposed by Camelio et al. (2003, 2002a). Variation propagation models considering the location of data such as in key control characteristics, key product characteristics, and a local coordinate system of parts were proposed by Qu et al. (2016).

Data-driven modeling and simulation of complex multistation manufacturing process for dimensional variation analysis

Dimensional variation analysis in multistation manufacturing processes (MMPs) is a challenging research topic with great practical significance. Researchers have been focused on constructing various mathematical models to identify the correlations among the huge amounts of collected production data. However, current models have achieved insufficient insights into the variation correlation laws due to the complexity of the data’s mutual relations. In this study, a data-driven modeling method is developed for deep data-mining and dimensional variation analysis. The proposed initial mathematical expression originates from practical engineering knowledge. Through a mathematical treatment, the mathematical expression is transformed into a first-order AR(1) model format, which contains multiple dimensional variations’ interstation and temporal correlating information. To obtain this information, the estimation of the proposed model is discussed in detail. A simulation case involving two key product characteristics of a grinding process is used to demonstrate the effectiveness and accuracy of the proposed method for dimensional variation analysis in MMPs.

Variation propagation modeling and analysis of automotive body outer cover panels assembly systems

PurposeThis study aims to present a model and analysis of automotive body outer cover panels (OCPs) assembly systems to predict assembly variation. In the automotive industry, the OCPs assembly process directly influences the quality of the automobile body appearance. However, suitable models to describe variation propagation of OCPs assembly systems remain unknown.Design/methodology/approachAn adaptive state space model for OCPs assembly systems is introduced to accurately express variation propagation, including variation accumulation and transition, where two compliant deviations make impacts on key product characteristics (KPCs) of OCP, and the impacts are accumulated from welding process to threaded connection process. Another new source of variation from threaded connection is included in this model. To quantify the influence of variation from threaded connection on variation propagation, the threaded connection sensitivity matrix is introduced to build up a linear relationship between deviation from threaded connection and output deviation in KPCs. This matrix is solved by homogeneous coordinate transformation. The final deviation of KPCs will be transferred to ensure gaps and flushes between two OCPs, and the transition matrix is considered as a unit matrix to build up the transition relationship between different states.FindingsA practical case on the left side body structure is described, where simulation result of variation propagation reveals the basic rule of variation propagation and the significant effect of variation from threaded connection on variation propagation of OCPs assembly system.Originality/valueThe model can be used to predict assembly variation or potential dimension problems at a preliminary assembly phase. The calculated results of assembly variation guide designers or technicians on tolerance allocation, fixture layout design and process planning.

Cross-Domain Tolerance Analysis for Directional Control Valves Based on Imperfect Information

The task of tolerance analysis usually addresses the question of the mechanical mountability of an assembly. We extend this viewpoint when talking about directional control valves in a crossdomain tolerance analysis; an analysis whose task is to determine the possible variation in the key product characteristics induced by a specific tolerance concept. As the available information about the noise factors to be toleranced is almost always imperfect generalised methods for their representation and the propagation of their impact on the key product characteristics are required. In this study the capabilities and potentials of belief and plausibility measures as well as fuzzy random variables are compared to traditional worst-case and statistical tolerance analysis.

Production characteristics of intensive whiteleg shrimp (Litopenaeus vannamei) farming in four Vietnam Provinces

Whiteleg (Litopenaeus vannamei) and black tiger (Penaeus monodon) shrimp are the most common farm-based shrimp species cultured in Vietnam, but there have been few studies of intensive whiteleg shrimp production. The objectives of this study were to describe production characteristics and to identify factors associated with yield in intensive shrimp production systems in Vietnam. This study included data from shrimp farms in four Vietnam provinces, collected from mid-2014 to early 2016. The data included key production characteristics, such as length of production cycle, number and quality of postlarvae (PL) stocked, feed intake, production yield (kg/m2), harvest size (shrimp/kg), average weight and water quality measurements for some of the crops. We report descriptive statistics for the production parameters and findings from two linear mixed models describing the associations of these parameters with two outcomes: production yield and average weight of shrimp. The average stocking density for the 846 crops included in the dataset was 109 PL/m2, average length of production cycle was 92 days, and average production yield was 1.15 kg/m2. The shrimp gained 0.2 g per day, on average, and consumed an average of 0.17 kg of feed, daily/1,000 PL stocked. The linear mixed models indicated that stocking density, stocking month and region were the main predictors for production yield, while water temperature 4 weeks prior and PL quality were the main predictors for the average weight of shrimp, after accounting for the length of production cycle. This study describes baseline production parameters for whiteleg shrimp production in Vietnam.

Fixture layout optimization in multi-station sheet metal assembly considering assembly sequence and datum scheme

One of the most influential causes of dimensional inaccuracy in automotive bodies is the inescapable fixture deviation, which is propagated in a Multi-Station Assembly (MSA) process. Therefore, a robust fixture design is necessary to increase product quality. The aim of this paper is to present a methodology for simultaneous optimization of fixture layout and assembly configuration, including assembly sequence and datum shift scheme, in MSA. To model the accumulated variation from one station to the downstream stations, a non-linear state-space representation is adopted. Feasible assembly sequences are automatically generated using a liaison graph and Breadth-First Search algorithm, and also all datum scheme options are provided based on a permutation method that is dependent on the number of parts and stations. Further, the sum of squared standard deviations for key product characteristics to be minimized is an optimization object. Part boundaries, design requirements, and assembly configuration feasibility are the constraints. This constrained problem is converted into an unconstrained one by two strategies: (i) Geometry boundaries and assembly configuration constraints are fulfilled through removing infeasible solutions from the design space and (ii) Design requirements are met by using a self-adaptive penalty function. Finally, the unconstrained problem is optimized using a genetic algorithm. The proposed methodology improved assembly process capability by 68 to 84% for an automotive body side in a case study.

The performance of global film franchises: installment effects and extension decisions

An empirical exploration of global film franchises provides insights for managers of film franchises, investors in franchisable products, and scholars interested in motion picture performance. Performance tends to deteriorate as extensions are introduced: production budgets rise, advertising expenditures remain similar, and the number of opening-weekend theaters experiences a jump with the first sequel and then remains similar in subsequent installments. However, revenue, return-on-investment (ROI) and audience and critical reviews fall, and foreign performance becomes increasingly important. Offsetting deteriorating performance, risk falls: revenue and ROI become more predictable. An early change in the lead actor causes reduced performance, but changes in key product characteristics and inputs in later installments help prolong the franchise. ROI of the current installment is the most critical financial determinant of whether a further extension will occur, but high-budget films and those with higher domestic share of revenue are also more likely to yield further extensions.

How do farm models compare when estimating greenhouse gas emissions from dairy cattle production?

The European Union Effort Sharing Regulation (ESR) will require a 30% reduction in greenhouse gas (GHG) emissions by 2030 compared with 2005 from the sectors not included in the European Emissions Trading Scheme, including agriculture. This will require the estimation of current and future emissions from agriculture, including dairy cattle production systems. Using a farm-scale model as part of a Tier 3 method for farm to national scales provides a more holistic and informative approach than IPCC (2006) Tier 2 but requires independent quality control. Comparing the results of using models to simulate a range of scenarios that explore an appropriate range of biophysical and management situations can support this process by providing a framework for placing model results in context. To assess the variation between models and the process of understanding differences, estimates of GHG emissions from four farm-scale models (DairyWise, FarmAC, HolosNor and SFARMMOD) were calculated for eight dairy farming scenarios within a factorial design consisting of two climates (cool/dry and warm/wet)×two soil types (sandy and clayey)×two feeding systems (grass only and grass/maize). The milk yield per cow, follower:cow ratio, manure management system, nitrogen (N) fertilisation and land area were standardised for all scenarios in order to associate the differences in the results with the model structure and function. Potential yield and application of available N in fertiliser and manure were specified separately for grass and maize. Significant differences between models were found in GHG emissions at the farm-scale and for most contributory sources, although there was no difference in the ranking of source magnitudes. The farm-scale GHG emissions, averaged over the four models, was 10.6 t carbon dioxide equivalents (CO2e)/ha per year, with a range of 1.9 t CO2e/ha per year. Even though key production characteristics were specified in the scenarios, there were still significant differences between models in the annual milk production per ha and the amounts of N fertiliser and concentrate feed imported. This was because the models differed in their description of biophysical responses and feedback mechanisms, and in the extent to which management functions were internalised. We conclude that comparing the results of different farm-scale models when applied to a range of scenarios would build confidence in their use in achieving ESR targets, justifying further investment in the development of a wider range of scenarios and software tools.