1. Building Intelligent systems The mjunction way

2. Sweeping Mega Trends Data in Our Lives IOTS AI Automation
FB Almost 1 M Logins , 4,50,000 Tweets , 156 M s & 15 M Texts every MINUTE
Big Data Growing at 40 % Annually
IOTS
75 Billion Connected Devices in 2 Years time AI
Programs for Facial Recognition, Analyzing Social Media , Listening
Automation
Machines taking over the 4 Ds – Dull , Dangerous , Dirty and Dear

3. Sweeping Mega Trends Robots
2020 Global Personal Robots Market estimated at 17 Billion
Assisted Living , Household Care Products , Teaching Assistants , Companions ( Nannies , Personal Assistants
Steep Fall in Pricing from 1,50,000 USD to < 1000 USD

4. Top Emerging Industries 2020
Virtual Commerce
3 D
Big Data
Logistics
Cyber Security
Cloud Compute
Growth Potential
Managed Services
Wellness
Market Size

5. The Challenges facing the corporates Today
How much should I produce given a set of constraints ?( Production capacity, profit margins volatile prices )
How do I know what my dealer will purchase next month and the month after that and what quantity , which SKU etc ?
Can I Know How will my customers respond to any Stimuli before doing the activity
How much Inventory should I order given the Production target and the default tendencies of vendors ?
Can I Know beforehand which of my customers are going to churn
The Challenges facing the corporates Today

6. Can An Intelligent system Tell me all of these at the click of a Button ?
Yes all of these are possible and mjunction addressing these issues by designing intelligent systems

7. How do we use Intelligent Systems to benefit our Customers
150 + forward Auctions of secondary steel alone , around 100 reverse Auctions per day of various materials
How do I estimate the prices that these auctions would throw up ?
How do I contact the right Bidder for the Right Auction ?
Machine learning systems which would learn from each auctions, take in external factors and recommend us:
The most likely set of bidders for an Auction, the opportunity for cross selling.
The Prices that might result from an Auction.
A centralized data warehousing system repository of data to make data available across categories across clients- To make better price forecasting and better buyer/seller recommendations possible

8. Automated Systems in Supply Chain for Better Decision making
Balance higher Inventory holding vs loss of production on account of shortages . Automatically raising of P.O’s based on shortages and future demands.
Vendor default predictions to optimize Inventory holding costs.
Price Planning – Automated price recommenders - increase/decrease in demand
ML Systems to predict shortages in stock /going out of stock( Finished Goods)
ML Systems to  recommend products in excess and automatically reduce price to clear inventory. Past buying patters by customers
ML Systems for Fraud detections/Exception Analytics

9. ML Based Fraud Detections Systems
Finding application in various Industries like retail, banking, FMCG, manufacturing etc
Bidder Collusions in procurement process
Identification of Fictitious Vendor
Submission of fake Invoices
Inflation in procurement prices
Misreporting of sales- channel stuffing
Fudging book of accounts

10. Changing paradigms of Channel Engagement
Heavily reliant on human and the knowledge that they bring in to
Manually estimate the expected sales
Conceptualize activities which would bring in the results in terms of promotions, schemes and other marketing spends
Primarily a one scheme fit all method for promotions
Ready Informations about the channel propensity to respond to any promotional Activity
ML Algorithms to predict churn tendency even for the channel
Ability to forecast sales down to the SKU levels for each channel partner.
Intelligent systems to cluster the channel partners as per their expected behavior.

11. THANK YOU
By:
Dipankar Mukherjee
mjunction services limited