Industrial Management Advances

"Emograph: transforming customer sentiment into actionable insights for smarter purchasing"

"Emograph: Transforming Customer Sentiment into Actionable Insights for Smarter Purchasing" introduces a cutting-edge approach to understanding customer sentiments and translating them into practical strategies for informed decision-making. In simpler terms, it's about using advanced technology to figure out how customers feel about products or services, and then using that information to make smarter choices.Imagine you're a business owner. You want to know what your customers think about your products. Emograph helps you do that. It uses fancy tools and techniques to analyze data from customers - like what they say on social media, in reviews, or even in surveys. Then, it breaks down all that information to figure out if people like your stuff or not. Basically, Emograph is like having a super-smart assistant who listens to what your customers are saying, translates it into plain English, and then gives you advice on what to do next. It's like having a secret weapon for making your business even better. And in today's world, where understanding customers is key to success.

Predictive analysis for construction site using AI

This paper presents a prescient investigation system for development destinations utilizing fake insights (AI) advances. The essential center of this inquire about is to create prescient models that use AI calculations to expect and moderate potential dangers and optimize development forms. Key components of the venture incorporate information collection from development destinations, highlight designing, demonstrate preparing, and assessment. Different machine learning and AI strategies are utilized to analyze chronicled information, distinguish designs, and figure future occasions such as extend delays, material deficiencies, and security risks. The proposed system points to upgrade decision-making forms in development administration by giving noteworthy experiences inferred from data-driven prescient analytics.

Supplier selection through multicriteria analysis in a pharmacy network

The effective management of pharmaceutical supply chains is essential for ensuring the availability of critical products, such as medicines, which are directly linked to public health. Supplier selection plays a pivotal role in maintaining a stable supply chain, requiring an evaluation framework that balances multiple, often conflicting, criteria. This paper addresses three research questions: (RQ1) How can a multicriteria decision-making framework effectively prioritize suppliers in a dynamic and competitive supply chain context? (RQ2) What are the critical factors influencing supplier evaluation in the pharmacy industry, and how can their relative importance be quantified? (RQ3) How robust is the proposed methodology when subjected to sensitivity analyses across varying criteria weights? To answer these questions, we propose a multicriteria decision-making approach for ranking suppliers within a pharmacy network to enhance decision-making and foster strategic partnerships. The study focused on five suppliers of a pharmacy network operating in the state of Paraná, Brazil. The Best-Worst Method (BWM) was used to determine the weights of the criteria and subcriteria, while the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) ranked the suppliers. The criteria related to economic efficiency and delivery reliability were given the highest weights, as they are critical to ensuring the stability of the pharmaceutical supply chain. The analysis of the suppliers showed that Supplier 3 consistently ranked first, with Supplier 1 and Supplier 2 completing the top three. Despite differences in their performance across various criteria, the results highlight the importance of using robust multicriteria frameworks to identify supplier strengths and weaknesses, mitigate supply chain risks, and support more informed and strategic decision-making processes.