Case Study
- Order Spikes Analysis
- Product Data Hub Implementation
- EXADATA Hardware Migration - X9 to X10
- Legacy System Migration to Oracle with Golden Gate
- Migration of PeopleSoft HCM to Workday HCM
- Standardizing Item, UOM and XRef Processes with EBS
- EBS Item-Vendor Sync Resolution
- PlannerSync PDH
- Dynamic System Integration
- Agile OU Operations
- IaC Enablement Process
- Oracle EBS Optimization Initiative
- Security Patching with Ansible
- Automated Patch Management
- MC Product Hub
- Scalable Division Setup Without Hard Coding
- Data Cleanup
- Multi Unit Integration Under Single Responsibility
- CDH Enablement for Grocery
- OMS Online
- SourceLink, UAM, MESA - Angular and Java
- SailPoint IIQ
- QA Test Automation
- Devops
- Optimizing Testing Strategies in the LedgerIQ
- Java Revamp
- Enhancing Academic Efficiency Through Digitization
- API Management
- OMS Batch Modernization
- Linux Server Automation with Ansible
- Securing Servers through Standardized Hardening
- Azure Recoverability Initiative
- DC Ops Analytics
- Katalon Automation
- Linux Administration
- Leveraging AI in IoT Systems
- Intelligent Discrepancy Resolution Assistant
- AI-Driven Supply Chain Anomaly Detection and Risk Insights
- AI-Driven Traceability Platform
Order Spikes Analysis
Order Spikes Analysis is the process of identifying, analyzing and understanding sudden increases in order volume within a specific timeframe. These spikes can occur due to various factors, such as seasonal demand, promotional campaigns, supply chain disruptions or unexpected market trends.
Problem
Statement
Identifying anomalies in historical order data over a 52-week period, excluding the most recent week.
The goal is to detect significant deviations in order quantities for various CUSTOMER and ITEM combinations, allowing for better insights into purchasing behaviors and potential operational issues.
Solution
Proposed
01
Data Preparation
Filtering: The historical order data was filtered to exclude the most recent week, ensuring that only relevant data for analysis was considered. Also, prepared another data for the same time period last year.
02
Z Score Calculation
Baseline Establishment
For each CUSTOMER_ITEM combination, the average order quantity and standard deviation were calculated using the historical data.
Z Score Calculation:
The Z score was computed to measure the number of standard deviations and observation is from the mean.
03
Anomaly Detection
Threshold Comparision
The calculated Z scores were compared against a predefined threshold to flag orders as anomalies.
Comparison of Anomalies
The latest week's order quantities were compared to the historical same-time period averages to identify significant spikes in order quantities, providing seasonality context for anomalies.
04
Percentage Change
The percentage change in order quantity was calculated for each CUSTOMER_ITEM combination. This metric quantified the deviation from the average, providing a clear view of how much the latest orders differed from historical trends.
Business
Values
The implementation of an anomaly detection system for historical order data provides significant business value across several dimensions:
01
Enhanced Decision-Making
By identifying significant deviations in order quantities, the business can make informed decisions based on real-time insights. This enables proactive responses to change in customer behavior, market trends and inventory needs.
02
Improved Customer Satisfaction
Understanding purchasing behaviors through anomaly detection enables the business to tailor its offerings and marketing strategies. By addressing unexpected spikes or drops in orders, the company can better meet customer expectations and enhance satisfaction.
03
Cost Savings
By quickly identifying anomalies that could indicate overstock or stockouts, the business can reduce excess inventory costs and minimize lost sales opportunities. This financial efficiency directly impacts the bottom line.
04
Long-Term Strategic Insights
Over time, the data gathered from anomaly detection can reveal patterns and trends that inform long-term business strategies.