International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 7, Issue 4 (July-August 2025) Submit your research before last 3 days of August to publish your research paper in the issue of July-August.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 7 Issue 4
July-August 2025

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Real-Time Automation of Cake Shop Operations Using PowerBI and AutomationEdge RPA: A Customer Behavior-Driven Approach

Author(s) Mr. Prithviraj Nitin Vichare, Prof. Dr. Manisha Prakash Bharati
Country India
Abstract In this research, a comprehensive, low-code automation framework is presented, specifically designed to address the operational challenges faced by small-scale cake shops. The system combines Power BI for real-time data visualization, advanced demand forecasting models, and AutomationEdge’s Robotic Process Automation (RPA) capabilities to optimize critical business processes such as billing, inventory management, customer interaction, and feedback handling.

A dataset of over 25,000 customer transactions recorded between 2020 and 2025 was analyzed to identify distinct buying behaviors. Based on frequency, spending patterns, and product preferences, four consumer types were recognized: Habitual Buyers, Variety-Seeking Consumers, Complex Custom Order Clients, and First-Time or Hesitant Buyers. Each category is automatically mapped to specific workflows designed to personalize engagement, improve service delivery, and streamline operations.

Forecasting of item demand was conducted using ARIMA time series models, achieving a Mean Absolute Percentage Error (MAPE) of approximately 12%. Additionally, VADER-based sentiment analysis was employed to monitor customer reviews and promptly respond to dissatisfaction, reducing response time significantly. Key performance indicators showed notable improvements: invoice accuracy increased to 99%, stockouts were reduced by 75%, and customer order fulfillment times improved by 30\%. Furthermore, a measurable 18% increase in repeat purchases was observed.

The entire framework leverages AutomationEdge’s hyperautomation platform, integrating AI, OCR, chatbots (CogniBot), and cloud-based RPA-as-a-Service (RPAaaS). This scalable and cost-efficient solution demonstrates significant potential for enhancing operational efficiency and customer satisfaction in small-to-medium-sized retail environments.
Field Engineering
Published In Volume 7, Issue 3, May-June 2025
Published On 2025-06-30
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 7 Isu 3Cover Page Vol 7 Isu 2Cover Page Vol 7 Isu 1

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijfmr.com