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Value chain analysis and managing supply chain costs

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Abstract

The study aimed to reveal the effect of value chain analysis (VCA) of internal and external activities to manage and reduce product costs along the supply chain (SC) in industrial facilities in the Red Sea State in Sudan from the point of view of managers, financial managers and accountants there, from the basic dimensions of value chain analysis represented by VCA of suppliers' activities, VCA for product design activities, VCA for production activities, and VCA for customer activities. The supply chain costs are defined in just-in-time (JIT), target costing (TC), and activity-based costing (ABC). The study revealed that analyzing VCA contributes to managing and reducing product costs along the supply chain by exploiting the interrelationships and overlap between the value chain analysis and the supply chain for each supplier's production and customer activities.

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Extant studies of open book accounting focus primarily on data disclosure in long-term, committed purchasing arrangements. We extend research beyond that context by exploring the association between open book practices (in terms of nature and uses of disclosed data as well as conditions of data disclosure) and two different purchasing strategies. Three case studies are performed. Results indicate that within market procurement characterized by a transactional purchasing strategy, cost data primarily serve to reduce purchase price. Therefore, data disclosure is limited in scope and scale, occurs primarily during supplier evaluation and selection, and is characterized by an adversarial atmosphere. Incentives for suppliers to open their books focus on short-term tangible gains. Within a hybrid exchange arrangement characterized by a relational purchasing strategy, data disclosure supports cost reduction, e.g., through joint product development, and is more comprehensive. The atmosphere is less adversarial and suppliers reap long-term benefits.
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A general formula (α) of which a special case is the Kuder-Richardson coefficient of equivalence is shown to be the mean of all split-half coefficients resulting from different splittings of a test. α is therefore an estimate of the correlation between two random samples of items from a universe of items like those in the test. α is found to be an appropriate index of equivalence and, except for very short tests, of the first-factor concentration in the test. Tests divisible into distinct subtests should be so divided before using the formula. The index [`(r)]ij\bar r_{ij} , derived from α, is shown to be an index of inter-item homogeneity. Comparison is made to the Guttman and Loevinger approaches. Parallel split coefficients are shown to be unnecessary for tests of common types. In designing tests, maximum interpretability of scores is obtained by increasing the first-factor concentration in any separately-scored subtest and avoiding substantial group-factor clusters within a subtest. Scalability is not a requisite.
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