Selecting Optimal Overseas Warehouse Location in Global Supply Chain: An Application of Binary Integer Programming

Overseas warehouses are crucial nodes in today's worldwide supply chain. They offer timely delivery of products to remote customers at a low cost and with few shipping issues. A foreign warehouse is a cross-border solution that may be used to solve cross-border B2B and B2C transactions. Strategically positioned warehouses allow organizations to effortlessly cross borders across multiple countries, regardless of where company headquarters and production or assembly units are situated. We argued that selecting an overseas warehouse site has always come down to determining where the majority of a company's foreign clients are located such that when a customer places an order, it is fulfilled by the distribution warehouse closest to that client. In this study, a warehouse site optimization framework was developed using Binary Integer Programming to help decision-makers choose the best region, or sites, to build an overseas warehouse facility to meet expected customer demands. The proposed model is focused on determining the appropriate placement of the warehouse from a set of potential locations in order to decrease the travel distance between warehouse facilities and overseas customers. The overseas customers are assumed to be served by the warehouse that is closest to them geographically. When the number of to be overseas customers served is large, they might be organized into clusters. This pre-processing is based on the assumption that the warehouse charged with servicing the overseas customers of a certain cluster would care for all of them in that cluster. We presented four distinct case situations with varying characteristics. The k-means approach is used within the context of Binary Integer Programming to divide C overseas customers into G distinct and non-overlapping subgroups. Warehouse site influences transportation costs, and when new supply routes are introduced or a current supply chain is re-engineered, a thorough warehouse placement analysis is required. The proposed model would hope to help choose the best location for warehouses as well as other immovable infrastructure in the global supply chain

International Segmentation of Countries Using Unsupervised Machine Learning Algorithms

As markets grow more global, global market segmentation becomes increasingly important for creating, marketing, and selling items globally. Operating a business in many nations introduces unique obstacles. International markets are more diverse than domestic markets due to differences in historical, institutional, cultural, and political environments among nations. Clustering algorithms enable multinational companies to give better-personalized products and customer services to their foreign customers. By gaining a deeper understanding of the nation’s characteristics, companies may identify the appropriate products or services to distribute, choose the best marketing channels for the target consumers, discover new and important insights, and launch new business strategies. This study used unsupervised machine learning algorithms such as Affinity Propagation, DBSCAN, and Hierarchical clustering to segment 150 countries. The segments of countries were established based on their brand awareness, gross national income, and trade liberalization, which are considered to be some of the most relevant qualities to employ when determining the macro segmentation of countries in the context of international business. This research emphasizes and recommends that various machine learning technologies be used to construct segmented and countrywide business strategies and marketing tactics in order to advance the global market expansion

The Role of Artificial Intelligence In Accelerating International Trade: Evidence From Panel Data Analysis

Technology has historically played a role in shaping international trade, but the current explosion in Artificial Intelligence has the potential to radically alter global commerce in the years ahead. In this research, we hypothesized that the AI capability of a nation has a major impact on international trade. This study discusses different ways in which technological advancements in the AI domain are improving global trade. We tested the hypothesis using the WDI, Government AI Readiness Index panel dataset of 150 countries for the years 2018-2021. Fixed effect, and Random effect panel models were applied.   The results show that the AI capability of a nation has a major positive influence on trade. The findings also show that GDP and exchange rate have significant positive impacts, and inflation and trade restrictions have negative and significant impacts on trade. The findings of this study recommend strengthening the nation’s AI capacity to increase its trade volume. AI will stimulate better economic development and open up new avenues for international trade to the extent that it fosters productivity growth. However, governments will need time to adapt and employ new AI technology, since doing so requires significant financial investments, access to skilled people, and a shift in how international companies are operated

Investigating The Relationship Between Pricing Strategies And International Customer Acquisition In The Early Stage Of SaaS: The Role Of Hybrid Pricing

Modern cloud infrastructures make it possible for SaaS businesses to provide their services to clients all over the world. As a result, it is easy for a SaaS company to operate on a worldwide scale in an early stage. Innovative SaaS services are more difficult to price than regular products. Poor pricing may lead to a misleading impression of the product, while a well-thought-out price plan can assist a business in achieving its immediate and long-term revenue objectives while also satisfying its customers. The goal of this study is to investigate which pricing strategy helps SaaS organizations attract more customers. Correlation, Random Forest Regression, and Pairwise Multiple Linear regression were applied. The correlation heatmap shows that the sales volume is highly and positively associated with hybrid pricing. This indicates that the implementation of the hybrid pricing technique is associated with more sales volume.  The majority of SaaS companies in the study sample used freemium, high-low, and hybrid. The skimming and the penetration pricing techniques were the least employed pricing tactics in SaaS.  The regression model with hybrid pricing has also shown a high explanatory performance. With an overall score of 91.89 percent, the findings of this empirical study showed a sufficient degree of accuracy. According to the random forest results, among other techniques, hybrid pricing is the most significant pricing technique in increasing sales volume in SaaS.  This study recommends that the SaaS business should employ a hybrid pricing approach in order to attract more consumers, enhance the entire experience they deliver, and therefore increase SaaS sales revenues

Impact of Augmented Reality on Purchase Intention of Foreign Products Online

Augmented reality (AR) is a significant technology that holds the promise to transform how consumers interact with products before purchasing. It creates immersive experiences that enable people to engage with digital material in a more intuitive and straightforward manner. When used effectively, AR can be influential in every stage of customer journey including purchase intention stage. Assessing purchase intention of international consumers is critical for organizations because it allows them to plan and make choices about marketing, inventory, and expenses. Purchase intent provides international companies with information on what their global consumers are willing to purchase enabling them to modify their marketing and goods to better fit their customers' demands.  This research examined how augmented reality increase the purchase intention of global customer using the data, which includes data for 810 different overseas visitors of an e-commerce site.  We collected these data from visitors of a global e-commerce shop that integrated augmented reality (AR) into their smartphone app to enable users to imagine how they would appear with various items.  The study performed a Robust Least Squares Method-estimation. Our research's findings provide some early proof that using AR increases the level of purchase intention of foreign products.  The findings also indicate that price, and the number of positive reviews increase the purchase intention of foreign products.  Customers' buying intentions may help firms predict future trends and organize their strategy appropriately. Businesses must also understand the elements that drive purchase intent, such as immersive experience with AR, consumer demographics, nationality, product attributes, pricing, and customer experience. &nbsp

Smart Cities and FDI

Smart cities have emerged as a worldwide trend, progressing from the implementation of sensors and technologies to enhance infrastructures and service delivery to the development of city-wide policy through the utilization of big data analysis. The goal of a "Smart City" is to improve standard of life by acquiring knowledge from information gathered from people, technologies, and networked sensors. This research argues that smart cities may attract inflows Foreign Direct Investment FDI by influencing the investment choices of global corporate players in the new age by facilitating the flow of data, technology, innovations, and best practices while offering a livable and productive environment. When deciding where to invest, foreign investors will take new criteria into account. These factors include how sociable the environment is, how stable the economic condition is, and how digitally advanced the destination is. These variables will outweigh conventional investment considerations like inexpensive labor, abundant resources, and a large population. For developing nations and rising economies where businesses need capital and knowledge to increase their worldwide sales, foreign direct investment is crucial. To maintain high growth rates the countries should attract international investors, and, most importantly, provide its citizens with a good standard of living, and therefore, should speed up its investments in sustainable smart cities. &nbsp

Predicting Brand Switching from Local to Global Brands: The Role of Glocalization

Customer loyalty on the international level is a crucial indicator of a business's success. Businesses in the interconnected world are competing for customers not only in their own country, but all around the world. Almost all nations have undertaken substantial trade liberalization in the last twenty years and are fast integrating into the global economy. Understanding why some consumers favor global brands while many others choose local, hybrid, or other brands is crucial. Building customer loyalty is a clear objective of any international organization if it wants to keep foreign customers for the long term. Customers will continue to be a loyal client if the product fits and fulfills their needs, as seen by their post-purchase behavior. Consumers in today's highly competitive market have more sophisticated switching behaviors than ever before because of the widespread availability of information about competing product brands. This research implemented three different classification algorithms, namely, Logistic Regression, K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) to learn the switching behavior of consumers from local brands to international brands. The Support Vector Machine (SVM) performed better among the three algorithms. Glocalization is an intelligent adaptation of thoughts and ideas to local and regional demands, as opposed to having the same products and services everywhere. This study recommends that manufacturers and businesses aim at a worldwide or trans-regional market yet tailored to local laws or culture. Thus, to increase the local interest in global brands, a corporation can follow glocalization and can a global controlling strategy but applies localized methods for target markets depending on their interests, social values, languages, and so on

Customer Segmentation of Cross-border E-commerce based on FRMD Using Unsupervised Machine Learning

As buyers buy things from beyond national borders, cross-border e-commerce has quietly gathered significant momentum. E-commerce, which may be loosely described as the usage of the Internet as a medium for commercial transactions and the dissemination of market information, is expected to play an increasingly significant role in fueling economic expansion throughout the world. Under the assumptions of traditional mass marketing, consumers are all the same. The business has a unified strategy for producing, delivering, and engaging with customers, enabling it to save time and money while expanding its customer base to new heights. Companies relied much more heavily on mass marketing before consumer data was easily accessible. Big data has caused the proliferation of market segmentation. Market segmentation in the context of cross-border e-commerce is the process of identifying distinct groups of international customers and categorizing them so that targeted advertising campaigns may be developed. We extend the traditional FRM (frequency, recency, and monetary value) analysis to include the geographical distance of the foreign customers from the e-commerce company. This research used 3,000 overseas customer data from 8 different e-commerce stores. The unsupervised hierarchical clustering is based on four dimensions, namely, frequency of shopping, recency of transaction, monetary values of the purchased items, and finally, the geographical distance of the foreign customers. Companies of all sizes utilize market segmentation to hone their strategy and provide the highest quality products for their specific target populations

ADDING PERSISTENCE TO MAIN MEMORY PROGRAMMING

Unlocking the true potential of the new persistent memories (PMEMs) requires eliminating traditional persistent I/O abstractions altogether, by introducing persistent semantics directly into main memory programming. Such a programming model elevates failure atomicity to a first-class application property in addition to in-memory data layout, concurrency-control, and fault tolerance, and therefore requires redesign of programming abstractions for both program correctness and maximum performance gains. To address these challenges, this thesis proposes a set of system software designs that integrate persistence with main memory programming, and makes the following contributions. First, this thesis proposes a PMEM-aware I/O runtime, NVStream, that supports fast durable streaming I/O. NVStream uses a memory-based I/O interface that integrates with existing I/O data movement operations of an application to accelerate persistent data writes. NVStream carefully designs its persistent data storage layout and crash-consistent semantics to match both application and PMEM characteristics. Specifically, we leverage the streaming nature of I/O in HPC workflows, to benefit from using a log-structured PMEM storage engine design, that uses relaxed write orderings and append-only failure-atomic semantics to form strongly consistent application checkpoints. Furthermore, we identify that optimizing the I/O software stack exposes the PMEM bandwidth limitations as a bottleneck during parallel HPC I/O writes, and propose a novel data movement design – PHX. PHX uses alternative network data movement paths available in datacenters to ease up the bandwidth pressure on the PMEM memory interconnects, all while maintaining the correctness of the persistent data. Next, the thesis explores the challenges and opportunities of using PMEM for true main memory persistent programming – a single data domain for both runtime and persistent applicationstate. Such a programming model includes maintaining ACID properties during each and every update to applications persistent structures. ACID-qualified persistent programming for multi-threaded applications is hard, as the programmer has to reason about both crash-consistency and synchronization – crash-sync – semantics for programming correctness. The thesis contributes new understanding of the correctness requirements for mixing different crash-consistent and synchronization protocols, characterizes the performance of different crash-sync realizations for different applications and hardware architectures, and draws actionable insights for future designs of PMEM systems. Finally, the application state stored on node-local persistent memory is still vulnerable to catastrophic node failures. The thesis proposes a replicated persistent memory runtime, Blizzard, that supports truly fault tolerant, concurrent and persistent data-structure programming. Blizzard carefully integrates userspace networking with byte addressable PMEM for a fast, persistent memory replication runtime. The design also incorporates a replication-aware crash-sync protocol that supports consistent and concurrent updates on persistent data-structures. Blizzard offers applications the flexibility to use the data structures that best match their functional requirements, while offering better performance, and providing crucial reliability guarantees lacking from existing persistent memory runtimes.Ph.D