1. BigSCM™
Shaping Demand using Supply side Big Data

2. Contents
Executive Summary
Background
Retail Domain at a glance
Retail Domain at a glance
Supply chain demystified
Opportunity dimensions
Introducing BigSCM
BigSCM Product features- Adaptive Inventory with RFID
BigSCM Product features- Predicting Inventory with Geo Loc
BigSCM Product features- Intelligent usage of PoS data
BigSCM Product features- Optimize SCM with Social media inputs
Target Audience
Value Proposition
Vendors in this space
Next Steps
What do we require?
• Assumptions
• Investment Required
• Development Period
Challenges
Questions

3. Executive summary
While retailers are focusing more on understanding the customer preferences to better manage their
merchandise and enhance the business profitability, there is a definite play on leveraging the big data in
supply chain functions as well to enhance operational efficiencies and reduce costs
This presentation prescribes a product concept henceforth known as “BigSCM” which fits in to the market
vacuum of using Big Data technologies in the SCM realm and optimize SCM processes to the tune of 10
million per year and thus ensuring that the retail customer of this product will
• Enhance productivity
• Optimize Supply chain workflows
• Reduce costs
• Improve customer happiness index
• Shape demand
This product consumes Big data exhaust from the retail industry like
• RFID, POS data, Geo location of inventory to optimize directly any lacunae in the SCM
operations
• Social media feeds, customer complains, call center logs, returns logs, warranty issues to fix
any process related issues in the SCM workflows.
•3

4. Background
Pentagon uses social media analytics to Traditionally social media usage has
infer and predict political unrest in other focused mostly on marketing,
countries and take decisions on it. advertising and customer relationship
Google uses search based analytics to management. Relatively few have
comment on the pattern of epidemic tapped into the social networks as a
outbreaks means of communicating both internally
and with key supply-chain partners.
We look at possibility to use Big Data for
supply side rather than demand side.
A more recent report by Aberdeen Companies are in the very early stages
Group, Inc. 44 percent of industry were of adopting Big Data within their supply
already using some types of social media chains. Its hard time tracking down
in some manner to manage their supply great examples outside of customer
chains, while 37 percent said they relations for using Big Data.
intended to begin within the next one to SCM and Big data is Virgin territory and
two years. as an early adaptor we are poised to
gain unassailable lead in the market.
4

5. Retail Domain at a glance
•5

6. Supply chain demystified
•Supply chain management (SCM) is the management of a network of
interconnected businesses involved in the ultimate provision of product and service
packages required by end customers
•Supply Chain Management spans all movement and storage of raw materials,
work-in-process inventory, and finished goods from point-of-origin to point-of
consumption
•6

7. Supply chain in real life
•7

8. Opportunity dimension- Where is Big Data in Retail
•8

9. Opportunity dimension- Mapping domain to opportunity
•9

10. Opportunity dimension- Benefits of Big Data analytics in Retail
•10

11. Introducing BigSCM
SCM
recommendations
• RFID
• GeoLoc
• POS
• Call Center
• Warranty
• Cust Service
• Return Info
• Cust Rating
SCM Optimization suggestions
SCM Hadoop
workflow Processing
Framework
reference
Conversation Tracking framework

12. BigSCM Product features- Adaptive Inventory with RFID
Real time RFID analysis
• BigSCM will enable tracking of real-time inventory of any item in any location:
• BigSCM will help in automated replenishment signals integrating with SCM workflow.
• BigSCM will would aid in automated receiving and verification of items and quantities received at stores
and warehouse,
• BigSCM will make it easy for automated validation of fulfilled orders.
•12

13. BigSCM Product features- Predicting Inventory with Geo Loc
GPS-based location services
• BigSCM will enable Real-time visibility of in-transit inventory and would allow sensing real-time demand
signals to make this inventory “productive”.
• BigSCM will offer Real-time location sensing for better supply-demand match, and reduce the fulfillment
lead-time and inventory levels required without affecting service the customers.
• BigSCM can help combining the RFID for cold-chain perishable goods and real-time GPS location,
improve efficiencies to reduce the goods damaged due to temperature variations and expiration dates.
•13

14. BigSCM Product features- Intelligent usage of PoS data
PoS Data
• BigSCM will use POS data to provide a real-time demand signal with price information. This will help in
intelligent inventory deployments to optimize the inventory in the system,
• BigSCM will use early trends detected for seasonal goods can help better manage the open orders when
demand goes up and reduce potential clearance losses when it goes down.
• BigSCM will help in Price optimization that can be fine-tuned with real-time POS data to optimize the
profitability.
•14

15. BigSCM Product features- Optimize SCM with Social media inputs
Social Media helps SCM
• BigSCM will use Big Data from unstructured data sources like call center logs, customer complaints,
warranty returns, Twitter, Face Book to glean for Customer service related discussions, statuses etc.
• BigSCM will create a context out of it and find the pattern of the discussion, get the gist of it, get the
sentiment, get the prevalent complaint on a product line make a decision if intervention has to be made,
notify to relevant link in the Supply chain workflow.
• By sharing information instantly BigSCM can connect with virtually everyone involved in a supply chain,
retailers can more quickly take actions such as ordering more of the popular products or alerting
warehouses when orders are not getting fulfilled and delivered to customers on time.
•15

16. Target Audience
Suppliers
Customers Distributors
BigSCM™
Marketing and
Manufacturers
sales

17. Value Proposition
NLP engine at the heart of BigSCM BigSCM would reduce lead time and would integrate
could be patented and generate tighter feedback from customer to market demand
residual revenues
BigSCM can be used to generate Increase in on time shipments compared with to
revenues from diverse streams those not BigSCM.
An average out-of-stock rate of lessened compared
to for those not using BigSCM;
A remarkable increase in fulfillment costs, compared
BigSCM usage result in happier to those not using BigSCM.
customers, fewer out-of-stock products
and lower fulfillment costs,
Analytical insights from the customer The ability to collect, analyze, and use real-time demand
base can be converted actions that and inventory will open new opportunities to optimizing the
could be applied to fine tune the SCM supply chain operations and provide competitive cost-
processes. advantage.

18. Vendors in this Space or Ecosystem
Competition
As far as I searched, competitors in the SCM ecosystem
have not adopted a similar platform to enable SCM
optimization.
There are pockets in academia that are performing
research on the core NLP technology that would be useful.

19. Next Steps
BigSCM™
Software and People readiness Collaboration
Hardware: Hadoop,
Mahout, Competency in Real Probe if any existing
time Searching, NLP, NLP stack can be
Optimization algorithms leveraged
Dependency
Assumption Assumption Assumption
A retail company’s data Algorithms for NLP is of Social data would be
Social data would be Retail companies rely
Retail companies rely
assets can be used for medium complexity relevant to aa
relevant to on RFID Geo Loc and
on RFID Geo Loc and
building the search companies retail
companies retail PoS data for their
PoS data for their
index workflow
workflow operations
operations
Development Team Development period
Single Agile team of 8- Around 6-8 months for
10 people a working pilot in
production
19

20. Consulting Solutions - Retail

21. Business Flow

22. Challenges in Retail
domain
Lack of Supply Chain
Management optimization
Decrease in sales
Marketing and sales
Demand forecasting
Logistics

23. Problem Analysis
Analyze sales data and cost incurred by the stores.
 Analyze if there is any seasonal pattern in the demand /
sales.
 Analyze demographic locations and shipping information
of the stores.
Objective: Reduce operational costs

24. Insights
 Forecast future inventory demand and safety stock per
product per store thereby optimizing inventory costs.
 Supply Chain Management optimization.

25. Blueprint
 By analyzing the sales information from each store we
predict the future demand for each store and will come up
with safety stock per product per store thereby optimizing
inventory costs.
 Analyze the transport route, source and destination,
capacity of the vehicle and cost for each trip and suggest
ways for optimizing transportation cost.
 Analyze vendor data and come up with a
recommendation on which vendor to procure products for
low cost thereby optimizing the procurement cost

26. Data attributes
Transportation and distribution costs
Inventory
Inventory_ID Categorical
Inventory_ID Categorical
Source_Location Categorical
Order_ID Categorical
Destination_Location Categorical
Order_Quantity Numeric
Distance Numeric
Lead_Time Categorical
Time_Taken_To_Travel Numeric
Warehouse_maintenance_Cos
t Numeric Vehicle_Capacity Numeric
Cost_Per_Trip Numeric
No_of_Trips_Per_Month Numeric
Product_Wise_Shelf_Life Numeric Mode_of_Transport Categorical

27. Data attributes
Procurement_Costs
POS
Item_ID Categorical
Order_ID Categorical
Cost Numeric
No_Of_Units_Sold Numeric
Lead_Time Numeric
Total_Cost Numeric
No_Of_Items_Produced_In_a_
Item_IDs Categorical Month Numeric

28. Solution Analysis
 Class of the problem: Optimization
Techniques:
PCA, Random Forests to reduce dimensions.
Multi objective optimization (Goal Programming), Linear
Programming.
Supply chain problems are characterized by decisions that are
conflicting by nature. Modeling these problems using multiple
objectives gives the decision maker a set of pareto optimal solutions
from which to choose.

29. ROI
No. of stores: 150
Avg. profit for 80% stores per month = 10 Millions Per store
Less profit stores: 20% = 30 stores
Avg. profit for remaining 20% stores per month = 2 Millions
Per store.
Let us assume optimization will increase revenue by 100%
Each of 20% stores will be making a profit of 10 Millions,
there is a 200% increase in the profit for these 30 stores.

30. International School of Engineering
2-56/2/19, Khanamet, Madhapur, Hyderabad - 500 081
For Individuals: +91-9177585755 or 040-65743991
For Corporates: +91-9618483483
Web: http://www.insofe.edu.in
Facebook: http://www.facebook.com/insofe
Twitter: https://twitter.com/INSOFEedu
YouTube: http://www.youtube.com/InsofeVideos
SlideShare: http://www.slideshare.net/INSOFE
LinkedIn: http://www.linkedin.com/company/international-
school-of-engineering
•This presentation may contain references to findings of various reports available in the public domain. INSOFE makes no representation as to their accuracy or that the
organization subscribes to those findings.
•The best place for students to learn Applied Engineering