Adopting the Supply Chain Operations Reference (SCOR) model, this study aims at investigating the impact of big data (volume, velocity, variety, veracity, and value) on supply chain performance through the mediating role of supply chain management (plan, source, make, deliver, and return) assuming four hypotheses. Data were collected using a questionnaire from managers of food processing companies. The results showed that big data affected supply chain management significantly and positively, which in turn affected supply chain performance significantly and positively. In addition, big data exerted a significant and positive impact on supply chain performance. Based on these links, it was found that supply chain management mediated significantly the effect of big data on supply chain performance. The study contributes to the literature showing that big data plays a pivotal role in improving supply chain performance and supply chain performance from the SCOR model perspective is critical for the relationship between these two constructs.

This study aims to evaluate supply chain management on fuel oil to optimize improvement strategies that can be applied to ConocoPhillips companies in Indonesia. Effective and efficient supply chain management is one of the goals to achieve the company's business stability in the fuel oil supply chain. Fuel oil is a very complex basic need for companies in carrying out industrial and transportation activities. The research method is measuring and evaluating company performance through a combination of the Supply Chain Operation Reference (SCOR) model and the Analytic Hierarchy Process. Research respondents through interviews with four informants from the company ConocoPhilips. Based on the SCOR Version 11.0 model, in this study the SCOR measurement is divided into four perspectives, namely Plan, Source, Deliver and Return. Furthermore, through the measurement of Key Performance Indicators, it is classified using five supply chain dimensions, namely reliability, responsiveness, agility, costs, and assets. The research resulted in the final value of supply chain performance of 74,992 which can be categorized as a moderate or intermediate level, this implies that the existence of an assessment system or measurement of supply chain performance on an ongoing basis can be used as a consideration in determining the optimal strategy. Research findings, improvements, and strategies are needed, especially in the perspective of delivering which has the lowest score.

The lack of a proper communication link between supply chain operations and financial performance seems to be due to the difficulty of using the operational metrics of supply chain measurement to reach the financial goals. This study is an attempt to find the effect of supply chain management (SCM) implementation on the financial success of the firms. The study considers the effects of Revenue, Prime Cost, Cash-to-cash cycle period and Return on working capital on the financial performance of the selected firms listed in Istanbul stock exchange from 2012 to 2017. The study divides the sample size based on their financial growth into two groups and using some statistical test measures the difference between two groups, statistically. The results show that there was a significant difference between the mean of high-growth and low-growth companies in terms of SCM implementation. However, the effects of cost, cash cycle and working capital on financial performance were not confirmed.

Fashion and Apparel Supply Chains work in a very fast-changing environment and always demand better quality, higher availability of products, broader assortments and shorter delivery times. An efficient Supply Chain Management can make a difference between success and failure in the market. In this context, the main purposes of the presented work are: (i) to define the physical and informative flows, together with connected cost and revenue items, which characterize a Fashion Supply Chain working with a wide network of direct-operated or franchising mono-brand stores and (ii) to optimize Supply Chain performances through a responsive approach which, during the sales season, analyses actual market demand and adjusts operations plans accordingly. The framework aims at becoming a decision support system for the optimization of the performances of a process that starts from the development of the collection by the Styling Office and ends with the withdrawal of unsold items from the stores. In order to analyze the performances under different scenarios, a set of Key Performance indicators, partially selected from the SCOR Model, is defined.