A New Model for Integrating Big Data into Phases of Decision-Making Process

Chiheb, Fatma; Boumahdi, Fatima; Bouarfa, Hafida

doi:10.1016/j.procs.2019.04.085

Cited by 24 publications

(9 citation statements)

References 14 publications

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“…Enterprises should transform their own information into enterprise knowledge to assist in subsequent decisions [30]. Data can provide accurate information for enterprises, facilitating faster and wiser decisions [34].…”

Section: Digital Transformation Plan Generationmentioning

confidence: 99%

Constructing a Sustainable and Dynamic Promotion Model for Fresh Foods Based on a Digital Transformation Framework

Lin

Liu

et al. 2021

Sustainability

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The emergence of digital technology has compelled the retail industry to develop innovative and sustainable business models to predict and respond to consumer behavior. However, most enterprises are crippled with doubt, lacking frameworks and methods for moving forward. This study establishes a five-step decision-making framework for digital transformation in the retail industry and verifies it using real data from convenience stores in Taiwan. Data from residential type and cultural and educational type convenience stores, which together account for 75% of all stores, underwent a one-year simulation analysis according to the following three decision models for promotions: the shelf-life extended scrap model (SES), the fixed remaining duration model (FRD), and the dynamic promotion decision model (DPD). The results indicated that the DPD model reduced scrap in residential type stores by 12.88% and increased profit by 15.43%. In cultural and educational stores, the DPD model reduced scrap by 10.78% and increased profit by 7.63%. The implementation of the DPD model in convenience stores can bring additional revenue to operators, and at the same time address the problem of food waste. With the full use of resources, sustainable operation can be turned into a concrete and feasible management decision-making plan.

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Section: Digital Transformation Plan Generationmentioning

confidence: 99%

Constructing a Sustainable and Dynamic Promotion Model for Fresh Foods Based on a Digital Transformation Framework

Lin

Liu

et al. 2021

Sustainability

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“…Chiheb et. al in (Chiheb;Boumahdi;Bouarfa 2019) the potential to help organization making smarter and faster decisions that make a real difference in these should be centered in strengthening the decisionmaking process. Big companies like Amazon, Google, eBay, UPS and so on are the best examples that show the potential of Big Data.…”

Section: Value-creation and Decision-making Using Big Datamentioning

confidence: 99%

“…With every individual owning at least a laptop or a PC, or both, a smart phone, and perhaps other smart devices, the amount of data generated reached an exceptional speed. This continuous data generation resulted in the emergence of a new phesive attention both by people and business world (Ahsaan; Kaur; Naaz 2020), (Chiheb;Boumahdi;Bouarfa 2019). These unprecedented changes in data generation lead to changes and improvements in technology as but it caused a revolution in the overall approach of working and the way businesses are conducted (Chiheb; Boumahdi; Bouarfa 2019), (McNeely;Hahm 2014).…”

Section: Introductionmentioning

confidence: 99%

Integrating Big Data Into Decision-Making in Real Estate Industry

Ali

Siniak

2020

Vadyba

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With the developments in technology everything we use became smarter which resulted in an outbreak in data generation, which in turn demanded innovations in technology. The new technologies did not only affect the social life but also changed the dynamics in the way businesses are conducted. Compared to before, today people have platforms where they can state their opinions publicly. As positive opinions can increase the reliability of a product, person, brand or etc. negative opinions can decrease the reliability. The increase in the use of such platforms and smart devices resulted in an unprecedented increase in data generation. Hereby a new phenomenon, called Big Data, emerged. In parallel with these developments, business world came to a point where traditional business models and strategies run short to challenge the requirements of clients. At this point it is important to realize that the only way to stay in the game is to accept the paper the meaning and the importance of the Big Data phenomena is discussed through its effect on value-creation and decision-making. The process of integrating Big -making processes is investigated with an emphasis on the importance of value-creation from Big Data. As a result of the conducted literature review, success factors for a successful integration process are suggested. One of the industries that has mostly affected from the emergence of Big Data is real estate industry. A case study on the owner occupation rates in Europe was conducted using the annual report for 2019 of European Mortgage Federation (EMF) with the aim to point out to the advantages of using Big Data and analysis over the traditional methods and to emphasize the significance of adopting data analytics technologies.

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“…The data analytics techniques to be applied are chosen based on the objectives of the model and the type of data available. Subsequently, data sets for testing, training, and production purposes are developed and the model is built and executed [32,33]. Data knowledge incorporates the provisions of the first four steps in [28]'s five Steps to DDDM and the first eight Steps in [9]'s 10 Steps to DDDM.…”

Section: Proposed Dddm Model For 3plsmentioning

confidence: 99%

“…Furthermore, they determine the relevance and the impact of the identified alternatives. In addition, the what-if scenarios, reporting, dashboards, simulation of solutions and other techniques can be used to evaluate these alternatives [33]. Possible courses of action are derived, developed and analysed to determine their suitability in making the decision and achieving the favoured outcome.…”

Section: Proposed Dddm Model For 3plsmentioning

confidence: 99%

A Data-Driven Decision-Making Model for the Third-Party Logistics (3PL) Industry

Moyo¹,

Scholtz²,

Mohammed³

Kalpa Publications in Computing

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The evolution of the supply chain has resulted in a growth in the usage of technology and data generated and distributed within the industry. Third-party logistics (3PL) companies operating within the supply chain industry are not maximising the capabilities of data to make well- informed decisions. The purpose of this paper is to address this gap and to develop a prescriptive, theoretical model for data-driven decision-making (DDDM). To address the gap, a literature review of DDDM in 3PL industry and in other contexts was conducted. The proposed model is built based on the consideration of existing DDDM models and frameworks; data and data analytics principles to collect, store, manage and analyse data; and the Cynefin framework. Existing models and frameworks for DDDM do not provide explicit guidelines on how to apply DDDM in a 3PL and supply chain context. The proposed DDDM model constitutes of three phases, namely: (1) the setup phase, that considers data knowledge and decision-making knowledge; (2) execution phase; and (3) the learning phase. The application of the model in 3PL companies can support the decision- making process in these companies, with a consideration of the challenges and opportunities that exist in the supply chain. The decision-makers in 3PLs can thus make better-informed decisions that positively impact their enterprises and the supply chain.

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A New Model for Integrating Big Data into Phases of Decision-Making Process

Cited by 24 publications

References 14 publications

Constructing a Sustainable and Dynamic Promotion Model for Fresh Foods Based on a Digital Transformation Framework

Constructing a Sustainable and Dynamic Promotion Model for Fresh Foods Based on a Digital Transformation Framework

Integrating Big Data Into Decision-Making in Real Estate Industry

A Data-Driven Decision-Making Model for the Third-Party Logistics (3PL) Industry

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