Data Based Decision Making in Manufacturing Supply Chains

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Data Based Decision Making in

Manufacturing Supply Chains

N. Viswanadham

INSA Senior Scientist Computer Science and Automation Indian Institute of Science, Bangalore

July 3, 2014

Big Data Public Lecture N. Viswanadham

Department of Computer Science and Automation

Big Data Public Lecture Series

Input-Output model of a

Manufacturing System

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Integrated Manufacturing Supply Chain Network

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Supplier OEM Distributor

Customer B2B Logistics Chain _{B2C Logistics Chain}

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Service Center Logistics

Integrated supply chains are a network of Suppliers, Contract manufacturers, Distributors, Retailers, Logistics providers, Repair & Maintenance providers.

Multi Tier Supply Chain Network

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Recent Advances:Internet of Things,..

 IoT technologies can be categorized into Tagging things, Sensing things and Embedded things .

– The tagging things provide item identification, things can be

connected to the databases.

– The sensing things enable us to measure and detect changes in the

physical status of our environment.

– The embedded things yield information about the status of the embedding object.

 Cyber Physical Systems

 Systems of Systems

 Network of Networks

Analytics 1.0: Decision Making using Internal Data

 Several long term and short term decisions are made

– Sourcing: which country & from whom

– Demand estimation using sales data

– How much to manufacture, inventory levels at various places to match the demand

 ERP, APS, TMS,WMS etc make decisions analysing internal data: sales, shipments, inventory, etc .

 Control using PLCs, Robots, BPOs etc.

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Big Data Public Lecture N. Viswanadham ERP Finance HR MRP SUPPLIERS APS Global Logistics Manufacturing Scheduling Demand Planning Production Planning

WMS

TMS Demand Forecasting _YMS Carriers Sales History Manufacturin g Schedule Inter-Site Transfers Completed Inter-site Transfers Production Picks Purchase Orders ASNs Customer Orders Orders for Routing Inventory Summary ASNs Customer Orders Customer Orders EDI Biddin g Vehicle Routes Exceptions Pick Detail Receipt Detail Carrier Discrepancy POD ASNs ASNs POD POD POD Duty Load & Dock Detail Customers

Integrated Information Systems

Sudden and Synchronized Trade Collapse

Trade flows dropped by more than 20% from 2008Q2- 09Q2.

The synchronization was due to the connectivity of global supply chains that reacted “just in time” to the collapse in demand

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Big Dat

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Big data Enabled Business Processes

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Procurement: Supplier & Logistics provider

selection, Inventory management

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Dispersed Cognitive Manufacturing: Embedded

Machines, Smart parts, Cognitive PLCs

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Distribution & Retail: Warehousing, B2C

Logistics, Recommender systems

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Service Chains:

Logistics networks,

Repair & Maintenance of Machines, Trucks, etc.,

Traceability and Product recalls

Risk Mitigation

Examples: Changing Face of

Auto and Logistics Industries

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The Auto Industry is going through

Resource Revolution

 Cars are the second biggest capital expenditure we make . – They are parked 96 % of the time. Average occupancy is 1.6/5. – In the rest 4% is spent looking for parking, waiting at the traffic lights

and in driving

 Machine that Changed the world

 Good drivers are in a minority. Millions of accidents and deaths a year & 33 % of drivers didn’t touch the brakes before collision.

– Anti-collision technologies face liability issues if control of a car is taken away from the driver.

Mobile Application Start-ups Making History

BMW and Daimler say they are transportation companies

 Zipcar acquired by Avis in 2013 for $500 million, lets people rent

cars by the hour in major cities; each Zipcar replaces 21 cars.

 Uber lets people summon a car and driver via a smart phone app.

Google invested $258 million in Uber.

 mGaadi is bringing Uber-like convenience to auto rickshaw riders

 UberX in Bangalore, Delhi and Hyderabad, directly competes with

startups such as Ola Cabs and TaxiForSure.

 RelayRides and Getaround provide marketplaces where one can

rent their cars to others.

 Inrix gathers location information from millions of mobile devices and feeds information on traffic flow and optimal routes to drivers.

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Uber for Logistics is Happening in Asia

 Gogovan and Easyvan provide a peer-to-peer app that

connects van drivers with individuals or businesses who need their stuff shipped quickly

 Trucking firms use data from new sensors—monitoring fuel

levels, location and capacity, driver behaviour, etc. in their optimization. The goal is to improve the company’s route network, lower fuel cost, and decrease the of accidents

 Uber Isn’t a Car Service. It’s the Future of Logistics

Genpact: Control Tower for Penske

 Genpact Orchestrates the logistical services of Penske

 Genpact BPO workers in India and Mexico

– Check the customer’s credit status and arrange necessary permits. – Assign Trucks and Drivers based on driver’s choice and also the

maintenance record of the truck

– If the truck gets stuck at a weigh station for permits, the BPO staff transmit the necessary documentation .

– After the trip, the driver’s log is shipped to a Genpact facility

 Penske processes lot of info: numerical, text, voice, past records of 15K trucks and equal number of drivers

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Google has Created a Race in Driverless Cars.

 Google’s driverless car has a license to operate in California, Florida, and Nevada. Driven 700,000 miles & No accident.

 They use 360 deg sensors, lasers, learning algorithms and GPS

 AI software in the Google car learns from every experience of every car and will generate a real-time map of road conditions

 Cars could be managed as a network in the future

 Nissan & Daimler are committed for driverless cars by 2020.

 Rio Tinto’s driverless trucks moved 100 million tonnes

 Huge implications in social , industrial & military sectors

Service, Maintenance, and Repair

 75% of power plants run on natural gas, oil, coal or nuclear

 Using Big data analytics, Aircraft will tell maintenance crews the status and which parts need replacement

 GE can predict failure of gas turbines weeks in advance (IOT)

 A shift from current practice of Maintenance being carried out on a set timetable or reactively

 GE invested $105 million in Pivotal, a Big Data company formed by EMC and VMware.

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Big Dat

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Retailing: Disruptive Changes

 Retailers watch the shoppers in the store, where they go, in what order and understand how all these map to actual sales.

 Recommender Systems suggest to consumers products based on their browsing, searches and earlier purchases

– Netflix uses recommender system for each subscriber.

– Target predicted pregnancy in a Teen based on her buying patterns.

 Focus shift from Sales & Marketing to Predictive Analytics using Industry Knowledge, Consumer preferences,

Connections with the Stakeholders , Social media analytics

 Privacy is at stake ???

Data science is the next big thing in

Agriculture

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Monsanto acquired Climate Corporation,

maker of a software platform that crunches

weather-related data to help farmers grow

crops more effectively for $930 million.

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Implications for SCNs

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Demand for Services not just products: Power by Hour

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New services such as information networks &

protocols for roads and control of traffic are needed

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Happening in mines and military

Big Data Ecosystem

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Big data should aid in Decision Making that

results in Desired Business Outcomes

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"What is the most desired business outcome and

what marriage of data and algorithms gets us there?"

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The Big Question “what data from suppliers,

customers, governments, and local & economic

environment should one collect and analyze”

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What data you analyze every day, every week, every

month.

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A framework is needed to answer this question

Make Sure It’s Relevant.

•Examine a close alignment

between NYSE and London Stock Exchange indices and the amount of solar energy hitting the earth.

•One might draw a

conclusion that the amount of solar energy drives stock prices based on this data.

• It just happened to be a coincidence over a relatively narrow window.

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Big data Service Chain De li very Services Re sou rces Institutions

The Basic Ecosystem

Big Data Ecosystem

Central /State Governments

Regulatory Bodies Citizen Groups,_{Social Activists}

Institutions

Legal Licenses & Privacy Issues

Business Organizations

Communication Tools AI Based Decision support systems

BPO Deli very Techn ol ogi es & Mech ani sms

Feedback & Correction

Training Staff

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R&D and Educational Institutions Wireless and Smart phone service providers

Software Clusters Cloud & other Storage

Resources Human Resources with

new skill sets

Decision Making tools

Data Service Chain Vertical based

Content

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The Procurement Process: Data

Based Decision Making

B2B Procurement

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Strong ties with Trusted suppliers

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Total landed cost

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Focus on supplier ecosystem not just product price

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Supply Chain D el ivery Services Infra struc ture Res ourc es Institutions

The Basic Ecosystem

Investment Climate

Procurement Ecosystem Del iv er y Se rv ic e M ech an ism s

IOT and Supply Hubs Logistics & IT

companies Delivery Channels

Decision Making Tools,

BPO, Control Towers Resou

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Cloud Social Media, Recommender systems

Human, Financial & Natural Resources Location Factors

Infrastructure: Ports, Airports, Roads, Industry Clusters Customs , Export & Trade

Other Govt. Regulators

Institutions

Quality Control & Environmental Issues

Social, Legal and Privacy issues , Labor Unions

Procurement Chain

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Supplier Selection using Transaction Costs

Delivery

Institutions

Shipping, Inventory, Asset specific

Hard & Soft Infrastructure Taxes, Tariffs, SEZs, FTAs, Social groups Transaction Cost

Resource Clusters, Human, Asset Specific Financial, Power

Supply Chain Production,

Quality, Transport

Coordination Costs Broker fees

Conclusions

 Our framework identifies the data to collect and analyze to make the needed decisions

 Data formats need to be standardized for easy collection

 Attention is needed in creating apps for disintermediation

– The Indian truck market, where 80 % of operators own less than 10 trucks & Majority of them are owner-drivers with a single truck and is organized by transport middlemen or goods booking agents

– Same is true for SMEs

 Farmers ,Commission Agents ,Traders , Industries , Retailers, Wholesalers, Consumers , Mandi Staff form the social network.

 Applicable to service value networks and public networks such as

infrastructure, public health and food security.

 Talent is working on other’s problems. Attention to Indian

Data Based Decision Making in Manufacturing Supply Chains