The Impact of Demand and Supplier on Wine's Supply Chain Performance

García-Alcaráz, Jorge Luis; Alvarado‐Iniesta, Alejandro; Blanco‐Fernández, Julio; Maldonado-Macías, Aidé Aracely; Jiménez‐Macías, Emilio; Muro, Juan Carlos Sáenz-Díez

doi:10.1111/jfpe.12257

Cited by 5 publications

(4 citation statements)

References 40 publications

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“…Therefore, to maintain their competitive performance, organizations need to have the ability and the flexibility to forecast and measure supply chain performance (Yu et al , 2019; Hill et al , 2018). Companies should consider the fact that they need to use different scales to measure and forecast supply chain performance, depending on the environment in which they work (García-Alcaraz et al , 2016).…”

Section: Introductionmentioning

confidence: 99%

Exploring the consequences of judgmental and quantitative forecasting on firms' competitive performance in supply chains

Salehzadeh¹,

Tabaeeian

Esteki³

2020

BIJ

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PurposeThe purpose of this study is to examine the impacts of different forecasting methods (judgmental, quantitative and mixed forecasting) on firms' supply chains and competitive performance.Design/methodology/approachWorking with three groups of manufacturing companies, we explore the consequences of judgmental, quantitative and mixed forecasting methods on firms' competitive performance in supply chains. The validity of constructs and path relationships was examined using structural equation modeling (SEM).FindingsOur findings indicate that supply chain efficiency influences both cost reduction and customer satisfaction. In addition, the three dimensions of supply chain performance are shown to be direct antecedents of competitive performance. Our empirical results reveal that although all studied forecasting methods meaningfully influence supply chain performance, the mixed method, compared to the other two methods, has greater capabilities to enhance supply chain performance.Originality/valueThis research provides originality and insight into supply chain practices through forecasting methods to improve competitive performance.

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Section: Introductionmentioning

confidence: 99%

Exploring the consequences of judgmental and quantitative forecasting on firms' competitive performance in supply chains

Salehzadeh¹,

Tabaeeian

Esteki³

2020

BIJ

View full text Add to dashboard Cite

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“…The study of this interface is sometimes referred to as the supplier selection problem (SSP). This subfield of supply chain management (SCM) has been tackled largely in the dedicated literature, where it is mostly described by taking into consideration a set of criteria, traditionally based on cost, delivery time and quality among many other components [5], to then classify and rank suppliers [6,7]. This paper looks at the SSP of an orange juice producing company that uses suppliers in a collaborative scheme, that additionally includes in their supplier roster, small and medium farmers, under a contract farming model.…”

Section: Introductionmentioning

confidence: 99%

Green Supplier Selection in the Agro-Food Industry with Contract Farming: A Multi-Objective Optimization Approach

et al. 2019

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An important contribution to the environmental impact of agro-food supply chains is related to the agricultural technology and practices used in the fields during raw material production. This problem can be framed from the point of view of the Focal Company (FC) as a raw material Green Supplier Selection Problem (GSSP). This paper describes an extension of the GSSP methodology that integrates life cycle assessment, environmental collaborations, and contract farming in order to gain social and environmental benefits. In this approach, risk and gains are shared by both parties, as well as information related to agricultural practices through which the FC can optimize global performance by deciding which suppliers to contract, capacity and which practices to use at each supplying field in order to optimize economic performance and environmental impact. The FC provides the knowledge and technology needed by the supplier to reach these objectives via a contract farming scheme. A case study is developed in order to illustrate and a step-by-step methodology is described. A multi-objective optimization strategy based on Genetic Algorithms linked to a MCDM approach to the solution selection step is proposed. Scenarios of optimization of the selection process are studied to demonstrate the potential improvement gains in performance.

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“…These include studies on the impact of human resources on the agility, flexibility, and performance of the Rioja wine SC [16], the impact of human resources skills on the quality obtained from production processes, and the associated economic performance [17], analysis of demand, suppliers, quality, and delivery times with regard to financial benefits [18], and the development of human resources within the sector [19].…”

Section: Critical Success Factors In the Rioja Wine Scmentioning

confidence: 99%

“…In order to gather information from the companies, the questionnaire proposed by García-Alcaraz et al [18] was used, which consists of 64 items or questions, divided into 15 categories that are called latent variables. For this research, only 12 items and four latent variables were used: Production Processes (two items), Infrastructure (five items), Economic Benefits (two items), and Transport Benefits (three items).…”

Section: Stage 1: Survey Designmentioning

confidence: 99%

Impact of Infrastructure and Production Processes on Rioja Wine Supply Chain Performance

et al. 2018

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This paper presents a structural equation model for analyzing the relationship between four latent variables: infrastructure, production processes, transport benefits, and economic benefits within the supply chain for wine from La Rioja, Spain, by incorporating 12 observed variables. The model proposes six hypothesis that were tested using information gathered from 64 surveys completed by managers of several wineries in the region. The WarpPLS v.5 ® software (Version 5.0, Script Warp Systems, Laredo, TX, USA) was used to execute the model and analyze the direct, indirect, and total effects among latent variables. The results show that the control of production processes is a direct source of economic and transport benefits because of its higher explanatory power of those variables. Similarly, infrastructure is a direct source of transport and production benefits, and some of them are given indirectly. In addition, infrastructure does not have a direct effect on economic benefits; however, there were indirect effects given through production process and transport benefits. Infrastructure is a very important variable because of its influence in the final performance, but also because of its high environmental impact. Finally, economic benefits were explained in 43.8%, 19.1% belonging to production process, 21.1% coming from transport benefits, and 3.7% from infrastructure.

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The Impact of Demand and Supplier on Wine's Supply Chain Performance

Cited by 5 publications

References 40 publications

Exploring the consequences of judgmental and quantitative forecasting on firms' competitive performance in supply chains

Exploring the consequences of judgmental and quantitative forecasting on firms' competitive performance in supply chains

Green Supplier Selection in the Agro-Food Industry with Contract Farming: A Multi-Objective Optimization Approach

Impact of Infrastructure and Production Processes on Rioja Wine Supply Chain Performance

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