In the presentation, you will find a supply chain overview, problem and solution element, process flow, and roadmap. Supply chains are increasing in complexity due to the number of relationships with companies and their partners that supply chains have. These challenges have created a demand for efficient and accurate supply chain management strategies that can re-shape the way companies can work. This demand has led to the emergence of technologies such as Predictive Analytics. can be used in sales forecasting.

1. Subrat Kumar Dash
Case Study on
Supply Chain

2. Agenda
OVERVIEW
01
PROBLEM AND SOLUTION
02
ROADMAP
03

3. Overview
Supplier Manufacture Warehouse Distributor Customer
Value/Product Flow/Transport
Risk/Information
Finance

4. KPI of Supply Chain
Supplier Manufacture Warehouse Distributor Customer
Raw Material
Price
Time of Accurate
Delivery
Raw Material
Availability/Quality
Production Plan
Vs
Production
Length of filling
Order
Availability of
Finished Goods
In Time
Fully / Without
Error
Sales Vs Plan
Availability

5. Decompose Problem

6. ASSUMPTION
01 Goods are produced according to actual
demand as opposed to forecasts
PULL PROCESS
02 Uncertainty of demand during each month
of a year
UNCERTAINTY
DEMAND
03 Crucial to increasing efficiency and
eliminating waste in manufacturing
LEAN
METHODOLOGY
04 Data was still siloed and not available or
understandable to everyone within the chain
DATA
FRAGMENTATION

7. Elements of Supply Chain

8. Sale
Forecasting
Resource
Planning
Production
Planning
Procurement
Plan
Negotiation
and Contract
Maintenance
Planning
Corporate
Budget
Target Setting
Salesforce
Why Sale Forecasting

9. Vision
Problem
being Solved
Benefit to
Business
Success
Criteria
Create a single source of truth for
demand forecasting across
Consumer
Low accuracy and reliability
Late forecasts
Reduction in Resource time
Improvements in accuracy
impact to revenue
Centralized, easy-to-use
platform that supports multiple
inputs, models, reporting
features, and connectivity
Streamline and automate
multiple, manual processes and
data inputs for generating
forecasts across different regions
Manual data aggregation
and manipulation
Insufficient signals or
inputs to anticipate
changes in the market
Improvements in supply
signals will give Company a
stronger negotiating
position with suppliers.
Unified view of demand
supported by an automated
aggregation of various granular
models
Infuse Machine Learning to
generate highly accurate, on-
demand forecasts
Process Inconsistencies
based on Excel-driven
solutions
Integration with other
processes, allowing greater
ability to steer the business
with improved visibility and
forecast accuracy
ML-driven forecasting engine
that leverages an ensemble of
algorithms to strengthen model
accuracy

10. Marketing
Signal
Car Sale Data
TAM
Marketing
Spending
Google
Trend
Many More
Forecast
Database
ML
Forecast
Prediction DB
Forecast Engine
Train/ Test Model Selection
User Interface
Demand forecasting landing
page/New Plan/User Specific
parameter/Intergartion
Process
Flow

11. Better Handle Of
Disruptions
Manage known risks
Scenario-based risk assessment
Communicate More Closely with Suppliers
Expanding and assuring transparency
entire network
Moving Responsive Agile Supply Chain

12. Roadmap

13. Thank you!
Subrat Kumar Dash
https://www.linkedin.com/in/subrat-dash-pm/