Transformed raw business data into dashboards and analytical insights that reduce manual reporting efforts and support informed management decisions.
Unique Tooling Solutions — Business Analytics
Unique Tooling Solutions — Business Analytics
PROJECT:
Business Analytics
CATEGORY:
Data Science / Business Intelligence
CLIENT:
Unique Tooling Solutions
Business Challenge
For a business such as Unique Tooling Solutions (UTS), SAP systems capture a tremendous amount of operational information. However, transactional systems are primarily designed to manage day-to-day business operations rather than provide management with an easily accessible analytical view. As a result, gaining clear insights across multiple operational functions can require significant manual effort.
Management often needs answers to critical business questions across multiple areas:
Sales
- Which products are selling the most?
- Which customers are contributing the most revenue?
- What are the sales trends across regions?
- Which products or customers are growing or declining?
Inventory
- What inventory is currently available?
- Which products are moving slowly?
- Which products have high demand?
- Where are inventory levels becoming a concern?
Procurement
- What are the procurement trends?
- Which suppliers or products require attention?
- How well is procurement aligned with demand?
From SAP transactions to clear business insights across sales, customers, inventory, procurement, and regional performance for better data-driven decision-making.
Scope of Work
S2MS worked across the data analytics lifecycle, from understanding the available business information through to dashboarding and management insights.
Sales Performance Analytics: Sales Performance Analytics provides management with a consolidated dashboard to analyze revenue, sales volume, customer, regional, and product trends. This moves beyond individual SAP transactions to identify underlying business performance patterns.
Customer Analytics: Customer Analytics delivers visibility into customer contribution, purchase trends, repeat patterns, and product preferences across regional networks. This enables the sales team to understand customer purchasing behavior and business trends.
Product & SKU Analytics: Product & SKU Analytics provides a data-driven view across a broad portfolio of over 2,500 item varieties by tracking best-selling products, slow-moving inventory, regional demand, and revenue contributions. This helps identify the products contributing to overall business performance.
Inventory Analytics: Inventory Analytics connects stock movement, demand, and sales into a consolidated view of fast-moving items, stock availability, potential stock-outs, and excess inventory. This enables teams to monitor inventory movement and demand patterns over time.
Procurement Analytics: Procurement Analytics transforms SAP purchasing data into actionable insights covering procurement trends, supplier performance, and purchase values. This helps procurement teams align purchasing decisions with demand and inventory movement.
Regional & Branch Analytics: Regional & Branch Analytics aggregates data across Maharashtra, Gujarat, Andhra Pradesh, Madhya Pradesh, North Karnataka, and Goa to connect regional sales, customers, and inventory. This enables management to compare branch performance, identify high-growth regions, and pinpoint areas requiring focused attention.
Management Dashboard: Management Dashboard consolidates critical KPIs across business performance, sales, inventory, procurement, and regional operations into a single executive view. This provides leadership with a clear summary of key business metrics.
What We Improved
Built a Dedicated Analytics Layer Over SAP: Kept SAP as the system of record while creating a dedicated analytical layer optimized for business intelligence.
Streamlined the End-to-End Analytics Flow: Transformed raw transactional data into a structured pipeline covering SAP data ingestion, data transformation, analytics data modeling, BI dashboards, and business insights.
Upgraded Data Quality & Decision Support: Implemented data quality standardization, automated data pipelines, KPI development, and historical trend analysis to support data-driven business decisions.
The solution improves business visibility, reporting consistency, performance monitoring, customer analysis, product analysis and planning. It also creates a foundation for future forecasting and predictive analytics.
Data Science & Analytics
The analytics layer can be utilized to understand sales patterns, customer behavior, product performance, and business trends. Data preparation and analysis help identify meaningful patterns within the available business information.
Key analytical areas include sales trend analysis, product and category performance, customer analysis, order-volume trends, repeat business patterns and management reporting. These insights provide a consolidated view of business performance and help teams move from manual reporting toward data-driven decision-making.
Business Intelligence Dashboard
SAP is designed to capture and process enterprise transactions, while transforming transactional data into strategic business intelligence requires a dedicated analytical approach. S2MS bridges this gap by providing a structured analytics layer for business insights.
The Traditional Approach (Before S2MS): SAP Transactions ➔ Manual Extraction ➔ Excel / Reports ➔ Analysis ➔ Decision
The Challenge: Teams often spent significant time extracting data, preparing spreadsheets, and assembling reports before the information could be analyzed.
The S2MS Approach (With S2MS): SAP ➔ Automated Data Pipelines ➔ Analytics Layer ➔ Dashboards ➔ Insights ➔ Decisions
The Improvement: Automated data pipelines and dedicated BI dashboards help teams reduce manual reporting effort and focus more on understanding trends, monitoring performance, and supporting data-driven decisions.
Client Benefits
Better Business Visibility: Management gets a consolidated view of important business KPIs instead of relying on disconnected reports.
Improved Decision Making: Sales, customer, product, inventory, and regional trends can be identified more efficiently, helping management make decisions using business data.
Improved Sales Visibility: Management can analyze product, customer, and regional sales performance to better understand sales trends and business performance.
Better Inventory Understanding: Analytics provides visibility into inventory movement, fast-moving and slow-moving products, and potential stock availability concerns.
Reduced Manual Reporting: Management can identify important customers, repeat-business patterns and high-performing products to support sales planning.
Scalable Analytics Foundation: The analytics layer provides a foundation for future forecasting, predictive analytics, and AI-assisted business insights.
Process & Results
The project follows a structured data analytics lifecycle:
SAP(Source) → Data Engineering(ETL/Pipeline) → Analytics(Processing) → BI(Reporting/Dashboards) → AI-Assisted Analytics(Advanced Insights)
Real-Time Monitoring: Enabled centralized monitoring of vehicles, incidents, and environmental events.
Improved Vehicle Identification: Camera-based ANPR supports vehicle identification through captured number plates.
Better Incident Visibility: Provides location-based information for accidents, vehicle breakdowns, and other incidents.
Environmental Monitoring: Supports monitoring of weather changes and flood-related situations.
Centralized Smart City Platform: Brings multiple monitoring capabilities together to provide better visibility into city activities.
Business Visibility
Data from SAP is presented through a unified analytical view, providing management with visibility into sales, customer, product, inventory, and regional performance.