Demand sensing in e-business

Ravikumar, K.; Saroop, Atul; Arahari, Hkn; Dayama, Pankaj

doi:10.1007/bf02706250

Cited by 8 publications

(12 citation statements)

References 27 publications

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“…Ravikumar et al 16 and Bursa 17 predicted that Demand Sensing would be one of the competitive advantages that organisations will need for future competition. Bursa 17 extrapolates on this and highlights that in the CPG industry, Demand Sensing can decrease shelf-level OOS, increase demand forecast accuracy and improve customer service.…”

Section: Demand Sensingmentioning

confidence: 99%

“…Ravikumar et al 16 defi ned it as being where organisations sense the customer ' s purchase or choice behaviour, with Cecere, 19 Chase, 1 Fay, 20 Tohamy et al 21 and Griswold and Sterneckert 15 expanding on this defi nition to include the translation of downstream data with minimal latency utilising both customer and channel data. Thus, Demand Sensing is concerned with turning real-time demand into meaningful data to impact planning functions within the business.…”

Section: Demand Sensingmentioning

confidence: 99%

“…Ravikumar et al 16 surmise that Demand Sensing is enabled quite effectively because of customer relationship management (CRM), which is integrated into many organisations. This assertion of CRM enablement of more integrated systems from suppliers to customers, especially in the CPG industry, is quite poignant, and the author ' s stance on e-businesses highlights the enablement of Demand Sensing because of the information available to the retailer and supplier.…”

Section: Demand Sensingmentioning

confidence: 99%

“…This assertion of CRM enablement of more integrated systems from suppliers to customers, especially in the CPG industry, is quite poignant, and the author ' s stance on e-businesses highlights the enablement of Demand Sensing because of the information available to the retailer and supplier. This differs for bricks and mortar companies as downstream data are captured in-store in the form of POS as highlighted by Bursa, 17 Najmi et al 22 and Tohamy et al 21 The essential part of Demand Sensing according to Ravikumar et al 16 is the complex algorithms that help sense demand; however, their extrapolation of this focuses on adjusting price to shape demand rather than sense it.…”

Section: Demand Sensingmentioning

confidence: 99%

“…Bursa 17 builds on the notion of Demand Sensing highlighted by Ravikumar et al 16 in that it is the POS data and even RFID that drive true Demand Sensing. The author argues that demand technology integration within the demand management process allows for better analysis of POS data, thus providing a better sense of consumer demand.…”

Section: Demand Sensingmentioning

confidence: 99%

See 4 more Smart Citations

Estimating benefits of Demand Sensing for consumer goods organisations

Folinas

Rabi²

2012

J Database Mark Cust Strategy Manag

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The main objectives of this article are: fi rst, to present the evolution of collaborative demand planning approaches such as Collaborative Planning, Forecasting and Replenishment and Demand-Driven Value Networks to Demand Sensing, and second, to identify the benefi ts that Demand Sensing can generate for the Consumer Goods Organisations (CPG) industry. After synthesising the relative literature on the above collaborative approaches, three case studies are presented to identify the benefi ts of the Demand Sensing approach. The conclusions of the case studies, as well as the fi ndings of a survey on CPG organisations in the United Kingdom and the United States, are combined in order to design a framework that organises the benefi ts derived from Demand Sensing into various functional areas.

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Section: Demand Sensingmentioning

confidence: 99%

Section: Demand Sensingmentioning

confidence: 99%

Section: Demand Sensingmentioning

confidence: 99%

Section: Demand Sensingmentioning

confidence: 99%

Section: Demand Sensingmentioning

confidence: 99%

See 3 more Smart Citations

Estimating benefits of Demand Sensing for consumer goods organisations

Folinas

Rabi²

2012

J Database Mark Cust Strategy Manag

View full text Add to dashboard Cite

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Dynamic pricing models for electronic business

Narahari

Raju

Ravikumar

et al. 2005

Sadhana

Self Cite

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Abstract. Dynamic pricing is the dynamic adjustment of prices to consumers depending upon the value these customers attribute to a product or service. Today's digital economy is ready for dynamic pricing; however recent research has shown that the prices will have to be adjusted in fairly sophisticated ways, based on sound mathematical models, to derive the benefits of dynamic pricing. This article attempts to survey different models that have been used in dynamic pricing. We first motivate dynamic pricing and present underlying concepts, with several examples, and explain conditions under which dynamic pricing is likely to succeed. We then bring out the role of models in computing dynamic prices. The models surveyed include inventory-based models, data-driven models, auctions, and machine learning. We present a detailed example of an e-business market to show the use of reinforcement learning in dynamic pricing.

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The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods

et al. 2022

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In recent years, machine learning models based on big data have been introduced into marketing in order to transform customer data into meaningful insights and to make strategic decisions by making more accurate predictions. Although there is a large amount of literature on demand forecasting, there is a lack of research about how marketing strategies such as advertising and other promotional activities affect demand. Therefore, an accurate demand-forecasting model can make significant academic and practical contributions for business sustainability. The purpose of this article is to evaluate machine learning methods to provide accuracy in forecasting demand based on advertising expenses. The study focuses on a prediction mechanism based on several Machine Learning techniques-Support Vector Regression (SVR), Random Forest Regression (RFR) and Decision Tree Regressor (DTR) and deep learning techniques-Artificial Neural Network (ANN), Long Short Term Memory B Yigit Kazancoglu

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Demand sensing in e-business

Cited by 8 publications

References 27 publications

Estimating benefits of Demand Sensing for consumer goods organisations

Estimating benefits of Demand Sensing for consumer goods organisations

Dynamic pricing models for electronic business

The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods

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