BigSCM™

Shaping Demand using Supply side Big Data

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

Contents

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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.

Executive summary

Pentagon uses social media analytics to infer and predict political unrest in other countries and take decisions on it.

Google uses search based analytics to comment on the pattern of epidemic outbreaks

Traditionally social media usage has focused mostly on marketing, advertising and customer relationship management. Relatively few have tapped into the social networks as a 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 Group, Inc. 44 percent of industry were already using some types of social media in some manner to manage their supply chains, while 37 percent said they intended to begin within the next one to two years.

Companies are in the very early stages of adopting Big Data within their supply chains. Its hard time tracking down great examples outside of customer relations for using Big Data.SCM and Big data is Virgin territory and as an early adaptor we are poised to gain unassailable lead in the market.

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Background

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Retail Domain at a glance

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•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

Supply chain demystified

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Supply chain in real life

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Opportunity dimension- Where is Big Data in Retail

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Opportunity dimension- Mapping domain to opportunity

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Opportunity dimension- Benefits of Big Data analytics in Retail

Conversation Tracking framework

SCM workflowreference

Hadoop Processing Framework

SCM Optimization suggestions

SCM recommendations

• RFID• GeoLoc• POS• Call Center• Warranty• Cust Service• Return Info• Cust Rating

Introducing BigSCM

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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.

BigSCM Product features- Adaptive Inventory with RFID

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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.

BigSCM Product features- Predicting Inventory with Geo Loc

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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.

BigSCM Product features- Intelligent usage of PoS data

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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.

BigSCM Product features- Optimize SCM with Social media inputs

BigSCM™

Manufacturers

Customers

Suppliers

Distributors

Marketing and sales

Target Audience

NLP engine at the heart of BigSCM could be patented and generate residual revenues

BigSCM can be used to generate revenues from diverse streams

BigSCM usage result in happier customers, fewer out-of-stock products and lower fulfillment costs,

BigSCM would reduce lead time and would integrate tighter feedback from customer to market demand

Increase in on time shipments compared with to 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 to those not using BigSCM.

The ability to collect, analyze, and use real-time demand and inventory will open new opportunities to optimizing the supply chain operations and provide competitive cost-advantage.

Analytical insights from the customer base can be converted actions that could be applied to fine tune the SCM processes.

Value Proposition

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.

Competition

Vendors in this Space or Ecosystem

BigSCM™

Social data would be relevant to a companies retail workflow

Social data would be relevant to a companies retail workflow

Retail companies rely on RFID Geo Loc and PoS data for their operations

Retail companies rely on RFID Geo Loc and PoS data for their operations

Software and Hardware: Hadoop, Mahout,

People readiness

Competency in Real time Searching, NLP, Optimization algorithms

Collaboration

Probe if any existing NLP stack can be leveraged

Dependency

A retail company’s data assets can be used for building the search index

Assumption Algorithms for NLP is of medium complexity

Assumption Assumption

Development Team

Single Agile team of 8-10 people

Development period

Around 6-8 months for a working pilot in production

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Next Steps

Consulting Solutions - Retail

Business FlowBusiness Flow

Challenges in Retail Challenges in Retail domaindomain

Lack of Supply Chain Management optimization

Decrease in sales

Marketing and sales

Demand forecasting

Logistics

Problem AnalysisProblem 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

InsightsInsights

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

BlueprintBlueprint

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

Data attributesData attributes

Inventory

Inventory_ID Categorical

Order_ID Categorical

Order_Quantity Numeric

Lead_Time Categorical

Warehouse_maintenance_Cost Numeric

Product_Wise_Shelf_Life Numeric

Transportation and distribution costs

Inventory_ID Categorical

Source_Location Categorical

Destination_Location Categorical

Distance Numeric

Time_Taken_To_Travel Numeric

Vehicle_Capacity Numeric

Cost_Per_Trip Numeric

No_of_Trips_Per_Month Numeric

Mode_of_Transport Categorical

Data attributesData attributes

POS

Order_ID Categorical

No_Of_Units_Sold Numeric

Total_Cost Numeric

Item_IDs Categorical

Procurement_Costs

Item_ID Categorical

Cost Numeric

Lead_Time Numeric

No_Of_Items_Produced_In_a_Month Numeric

Solution AnalysisSolution 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.

ROIROI

No. of stores: 150Avg. profit for 80% stores per month = 10 Millions Per storeLess profit stores: 20% = 30 storesAvg. 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.

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