Product whole life-cycle and omni-channels data convergence oriented enterprise networks integration in a sensing environment

Li, Qing; Luo, Haiyun; Xie, Pei-Xuan; Feng, Xiao-Qian; Du, Rui-yang

doi:10.1016/j.compind.2015.01.011

Cited by 49 publications

(34 citation statements)

References 29 publications

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“…The growing popularity of Industrial IoT is highlighted in a survey by the authors in [48]. Many other authors have used IoT in various applications of industry such as supply chain management [44], product life cycle management [25][26], quality management [32], and many others [11][12]22,30,34,41,43]. Data generated by industrial IoT devices need to be processed and converted to information and knowledge.…”

Section: Related Workmentioning

confidence: 98%

Cognitive decision making in smart industry

Kaur

Sood

2015

Computers in Industry

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Section: Related Workmentioning

confidence: 98%

Cognitive decision making in smart industry

Kaur

Sood

2015

Computers in Industry

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“…The parameter reflects the organizational and operational readiness of an omnichannel retailer, i.e., how aligned the concerned retailer currently is to facilitate integration of services. Integrated organizational structure instead of channel-separated units (Hübner et al, 2016), well-aligned channel incentives , ICT-based enterprise-wide cooperation and collaboration systems (Li et al, 2015), and optimized channel-operations (Simone & Sabbadin, 2017) are some of the factors which differentiate an omnichannel-ready retailer from others. A lower value refers to higher organizational and operational readiness and hence more cost-effective omnichannel implementation, and vice versa.…”

Section: Cost Of Omnichannel Integration and Consequent Market Share Ofmentioning

confidence: 99%

Look Before You Leap: Economics of Being an Omnichannel Retailer

Saha

Bhattacharya

2020

OSCM: An Int. Journal

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This paper investigates how the market gets segmented when an omnichannel retailer enters a market hitherto served by non-omnichannel retailers. The added value derived from the omnichannel flexibility is expected to draw more customers to an omnichannel retailer, luring them away from the incumbent non-omnichannel peers. However, the customers are expected to be heterogeneous in their valuation of the omnichannel flexibilities offered. Also, since acquiring omnichannel flexibility entails additional costs for service integration, the omnichannel aspirant is expected to charge a higher price from the customers than its non-omnichannel competitors. Our model considers both these factors, and derives analytical expression for the expected market share of the omnichannel retailer as a function of the price charged by the non-omnichannel peers, the extent of flexibility provided, and the cost of providing the same. Also, given the cost of service integration, we estimate the level of flexibility that an omnichannel retailer must offer if it wants to acquire a specific market share. The findings indicate that the associated integration cost imposes a restriction on the maximum market share an omnichannel entrant can aspire to capture.

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“…For instance, Big Data from developed technologies is useful in product lifecycle management (Q. Li, Luo, Xie, Feng, & Du, 2015). As information technologies develop, Big Data can now originate from various sources, and facilitates a sensing environment for enterprises (Q.…”

Section: Eco-innovation Configuration Strategymentioning

confidence: 99%

“…As information technologies develop, Big Data can now originate from various sources, and facilitates a sensing environment for enterprises (Q. Li et al, 2015). Furthermore, the increase in cloud computing, mobile internet, and Internet of things contribute to Big Data, originating from different sources.…”

Section: Eco-innovation Configuration Strategymentioning

confidence: 99%

“…This needs organizations to meet the new challenges posed from the variety of the data such as converging, fusing and processing all the data from the various sources (Q. Li et al, 2015). Big Data is also an asset for enterprises as it helps in problem solving, activity coordination, plan development and strategy configuration (Cummins, 2017).…”

Section: Eco-innovation Configuration Strategymentioning

confidence: 99%

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Eco-Innovation and Industry 4.0: A Big Data Usage conceptual model

Munodawafa

Johl

2018

SHS Web Conf.

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Driven by Cyber Physical Systems, Big Data Analytics, Internet of Things and Automation, Industry 4.0 is expected to revolutionize the world. A new era beckons for enterprises of all sizes, markets, governments, and the world at large as the digital economy fully takes off under Industry 4.0. The United Nations has also expressed its desire to usher in a new era for humanity with the Sustainable Development Goals 2030 (SDG’s) replacing the Millennial Development Goals (MDG’s). Critical to the achievement of both of the above-mentioned ambitions is the efficient and sustainable use of natural resources. Big Data Analytics, an important arm of Industry 4.0, gives organizations the ability to eco-innovate from a resource perspective. This paper conducts an analysis of previously published research literature and contributes to this emerging research area looking at Big Data Usage from a strategic and organizational perspective. A conceptual framework that can be utilized in future research is developed from the literature. Also discussed is the expected impact of Big Data Usage towards firm performance, particularly as the world becomes more concerned about the environment. Data driven eco-innovation should be in full motion if organizations are to remain relevant in tomorrow’s potentially ultra-competitive digital economy.

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Product whole life-cycle and omni-channels data convergence oriented enterprise networks integration in a sensing environment

Cited by 49 publications

References 29 publications

Cognitive decision making in smart industry

Cognitive decision making in smart industry

Look Before You Leap: Economics of Being an Omnichannel Retailer

Eco-Innovation and Industry 4.0: A Big Data Usage conceptual model

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