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Inclusive workplace, social mobility and logistics

Ozbalci Seval

LogForum

2021

Vol. 17, no. 4

547--554

Background: This study practices the priorities of the World Economic Forum, Global Social Mobility Pillars 10th for the inclusive institutions. Global logistics discussed with accessibility needs in theoretical frameworks of Global Social Mobility Index (rankings for 2020 Turkey 64th, and Poland 30th). It is stated with sociological, technological, and economical improvements in line with on the global agenda. Methods: This article provides a data research, which considers the economic effects of Covid-19 for adults (≥18 years of age and employed with high digital literacy) during lockdown. Theory of main synthesis is targeting UN Development Goals and WEF’s Social Mobility Index. It is developed based on international literature and it is defined with total 100 Turkish people opinions and connected individual’s budget with the logistics services. Results: Digital technologies as an enabler of inclusive work are delivering the digital flow with Industry 4.0 by changing the way of logistic services function into another virtual transportation platform. In this paper, with the aim of identifying future directions, more than 100 surveys reviewed focusing on inclusive workplace options. Conclusion: Economies with greater social mobility provide more opportunities with the content of the accessible procedures which useful instrument for each procurement mode; operational, tactical, and strategic. It confirms the efficiency, effectiveness, and experiences of people with the digitalization technologies (SIoV, IoT, Blockchain, RPA, AI, Data Analytics, etc.) It is recommended that the levels explained in this study contribute to future studies by accessible supply chain with inclusive work procedures.

Prediction of the shoppers loyalty with aggregated data streams

Nikulin V.

Journal of Artificial Intelligence and Soft Computing Research

2016

Vol. 6, No. 2

69--79

Consumer brands often offer discounts to attract new shoppers to buy their products. The most valuable customers are those who return after this initial incentive purchase. With enough purchase history, it is possible to predict which shoppers, when presented an offer, will buy a new item. While dealing with Big Data and with data streams in particular, it is a common practice to summarize or aggregate customers’ transaction history to the periods of few months. As an outcome, we compress the given huge volume of data, and transfer the data stream to the standard rectangular format. Consequently, we can explore a variety of practically or theoretically motivated tasks. For example, we can rank the given field of customers in accordance to their loyalty or intension to repurchase in the near future. This objective has very important practical application. It leads to preferential treatment of the right customers. We tested our model (with competitive results) online during Kaggle-based Acquire Valued Shoppers Challenge in 2014.