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

1- Introduction

2- Background – the conceptual elements

3- Interplay – the theoretical framing

4- Application – the exemplification by retail supply chains

5- Conclusion

References

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.

Introduction

Retailers know a lot about end consumers, perhaps more than we know ourselves. In 2012, for example, US retailer Target sent coupons for baby clothes to a high-school girl. Her father was not amused by this type of advertisement, decided to visit a local Target store and requested to see the person in charge. The manager apologized for the inappropriate advertisement then a few days later over the phone. However, the father, seemingly embarrassed, revealed during this phone conversation that he had talked to his daughter: “It turns out there’s been some activities in my house I haven’t been completely aware of. She’s due in August” (Duhigg, 2012). As market expectations, competition, and volatility are rising, retailers are exploring data analytics to address new challenges and opportunities. Data analytics techniques not only provide single companies with greater accuracy, clarity, and insight but also lead to more contextual “intelligence” shared across all supply chains regardless of industry or sector.