دانلود رایگان مقاله انگلیسی تحلیل کلان داده ها و پیش بینی درخواست در زنجیره های تامین - امرالد 2018

Abstract

Purpose – Demand forecasting is a challenging task that could benefit from additional relevant data and processes. This paper examines how big data analytics enhances forecasts’ accuracy. Design/methodology/approach – A conceptual structure based on the design-science paradigm is applied to create categories for big data analytics. Existing theories from the scientific literature are synthesized with industry knowledge through experience and intuition. Accordingly, a reference frame is developed using three steps: (i) description of conceptual elements of the frame utilizing justificatory knowledge, (ii) specification of principles of the theoretical frame to explain the interplay between elements, and (iii) creation of a matching frame by conducting investigations within the retail industry. Findings – The developed framework could serve as a first guide for meaningful big data analytics initiatives in the supply chain. The paper illustrates that integration of different data sources in demand forecasting is feasible but requires data scientists to perform the job, an appropriate technological foundation, and technology investments. Originality/value – So far, no scientific work has analyzed the relation of forecasting methods to big data analytics; previous works have described technologies, types of analytics, and forecasting methods separately. This paper, in contrast, combines insights and provides advice on how enterprises can employ big data analytics in their operational, tactical, or strategic demand plans.

Limitations and outlook

Further research is required to analyze the use cases of BDA in relation to forecast situations to evaluate whether the matching is accurate. Such research would help to estimate the usefulness of diverse applications and support businesses in setting priorities. Moreover, an analysis of the costs of different BDA techniques would support businesses’ investment decisions. There are, of course, several limitations of this study. First, in addition to scientific articles, the literature review includes numerous non-academic sources from the information technology industry. However, they are essential to understand the techniques and technologies. To avoid bias, the sources were integrated with additional caution. Second, when analyzing the techniques’ potential concerning forecast demand in retail supply chains, the authors limited the spectrum of demand forecast to primary demand, excluding secondary and tertiary demand, because the former often lets businesses directly determine the latter two. Third, the list of applications is not exhaustive; only applications that seemed meaningful or were encountered during the literature search were included. Fourth, the two analyses lack empirical proof because of the third limitation. Fifth, the usefulness of each application is assumed to be equal, but this does not reflect their actual usefulness.