ANALYSING THE IMPACT OF THE PRODUCT LIFE CYCLE ON THE IMPORTANCE OF OUTSOURCING DECISION-MAKING CRITERIA: A MANUFACTURING CASE STUDY

<p>ENGLISH ABSTRACT: This study has been carried out to discover the impact of the product life cycle (PLC) on the importance of outsourcing decision-making criteria in a manufacturing firm. The four dimensions of outsourcing decision-making were identified, based on a literature review and on the conditions of the firm: (1) core competencies, (2) information leakage risk, (3) technological capability, and (4) cost. A case study and survey research were used, along with two non-parametric tests (Friedman’s test and Kendall’s W). The results show in particular that the importance of ‘technological capability’ and ‘strategic information leakage risk’ does not differ across the various PLC stages. The importance of ‘leakage risk of product volume information’ and ‘total cost’ change over different stages of the product life cycle are also addressed. For the latter two criteria, the probabilities of each rating related to each criterion importance have been estimated by ordinal logistic regression, and the weights of these criteria have been calculated at each stage of the product life cycle.</p><p>AFRIKAANSE OPSOMMING: Die navorsing ondersoek die invloed van produklewensiklus op die gebruik van buite leweransiers deur ’n vervaardigingsonderneming. ’n Besluitvormingsmetode word ontwerp en getoets deur skepping van ’n boeket van wiskundige gereedskap soos, onder andere, ordinale logistiese regressie en die logistieke funksie. Die metode word ten slotte afgerond met ’n keurige gebruik van die Wasigheidsleer.</p&gt

Evaluation of mathematical models for predicting particle size distribution using digital soil mapping in semiarid agricultural lands

Soil Particle Size Distribution (PSD) is a fundamental physical property that can affect soil hydraulic properties, soil structure characterization, and available water. Many models have been applied to define the PSD curve, but predicting the spatial distribution information of PSD has been rarely investigated. Therefore, the main objective of the current study was to predict soil texture fractions using the most accurate PSD models. First, the performance of 16 mathematical PSD models was evaluated. Then, Random Forest (RF) was used to determine the relationship between covariates (i.e., remote sensing and the digital elevation model) and georeferenced measurements of the best PSD models’ parameters. Results indicated that a PSD model may be acceptable for some particle diameters or even whole particles, but not necessarily be suitable for other particles. For example, in the estimation of sand content, the best model was Simple Lognormal, while the Fred-4p was the best model in the estimation of the clay fraction. Importantly, the Jaky model with only one parameter of P did a great job in predicting soil particle fractions. Further, the spatial distribution of clay, silt, and sand contents was accurately derived from the predicted map of P (R2 for Sand = 0.86). Consequently, the current research indicated that the combination of PSD models and digital soil mapping techniques can be used to quantify the spatial distribution of the PSD curve in other similar agroclimatological regions

Evaluation of mathematical models for predicting particle size distribution using digital soil mapping in semiarid agricultural lands

Soil Particle Size Distribution (PSD) is a fundamental physical property that can affect soil hydraulic properties, soil structure characterization, and available water. Many models have been applied to define the PSD curve, but predicting the spatial distribution information of PSD has been rarely investigated. Therefore, the main objective of the current study was to predict soil texture fractions using the most accurate PSD models. First, the performance of 16 mathematical PSD models was evaluated. Then, Random Forest (RF) was used to determine the relationship between covariates (i.e., remote sensing and the digital elevation model) and georeferenced measurements of the best PSD models’ parameters. Results indicated that a PSD model may be acceptable for some particle diameters or even whole particles, but not necessarily be suitable for other particles. For example, in the estimation of sand content, the best model was Simple Lognormal, while the Fred-4p was the best model in the estimation of the clay fraction. Importantly, the Jaky model with only one parameter of P did a great job in predicting soil particle fractions. Further, the spatial distribution of clay, silt, and sand contents was accurately derived from the predicted map of P (R2 for Sand = 0.86). Consequently, the current research indicated that the combination of PSD models and digital soil mapping techniques can be used to quantify the spatial distribution of the PSD curve in other similar agroclimatological regions.</p

Make-or-Buy Decision Using Interval-Valued Intuitionistic Fuzzy COPRAS Method

International Conference on Intelligent and Fuzzy Systems, INFUS 2019 -- 23 July 2019 through 25 July 2019 -- -- 228529One of the central issues in strategic management is the determination of a firm’s vertical boundaries. Since almost all the publications in the literature are based on crisp measurements and evaluations for make-or-buy decisions, we proposed a multi attribute decision making method using interval-valued intuitionistic fuzzy COPRAS for make-or-buy decision assessment. An illustrative example is presented for a Turkish toy company. © 2020, Springer Nature Switzerland AG