AI and Its Applications in Manufacturing

Dr. Biplav Srivastava IBM Research – India Presentation to MEL 423 (Computers in Manufacturing Class) IIT Delhi, November 12, 2014

Outline l  Artificial

Intelligence l  Sample Applications l  Manufacturing Domain and Planning l  Improving Manufacturing with Data and Extracted Knowledge

Resources l  AI –  Summary of the sub-areas:

http://aitopics.org/topic/ai-overview –  Russell and Norvig, AI – A Modern Approach –  http://en.wikipedia.org/wiki/ Artificial_intelligence

Artificial Intelligence (AI) l  Intelligent

Agents - Build useful systems

–  Symbolic: logic, reasoning, search –  Statistical: machine learning, Bayes rules

l  Cognition

– Understand working of brain

Sample Systems l  ASIMO

Copies Dance Moves (video) l  Chat with Ramona (link) l  IBM Watson (video)

Sample AI Applications · · · · · · · · · · · · · · · · · · · · · · ·

Agriculture & Natural Resources Archaeology Architecture & Design Art Artificial Noses Assistive Technologies Astronomy & Space Exploration Automatic Programming Automotive Industry Autonomous Vehicles Aviation Banking & Finance Bioinformatics Biometrics Business & Manufacturing Chatbots Chemistry Decision Support Systems Earth & Atmospheric Science Engineering Design Fraud Detection Hazards & Disasters Knowledge Management

· · · · · · · · · · · · · · · · · · ·

Law Law Enforcement & Public Safety Machine Storytelling Marketing Medicine Military Music Networks Oil & Gas Politics & Foreign Relations Recommender Systems Robots in the Home Robots in the Workplace Science & Mathematics Smart Houses & Appliances Social Science Software Engineering Spam Filtering Surveillance

· Telecommunications · Transportation & Shipping

Software System Referenced Data

Move More Business Logic To Declarative Data (policy)

Business Logic Processing

Reading Input

Produce Output

Environment Input 7

Output © 2014 IBM Corporation

Example: Taking Care of a Baby Individual’s Extension

Agent Expected behavior: • 

•  8

Inform •  •  •  Do •  •  • 

Alert when crying Alert when awake Alert when idle Raise temperature of room Play music …

© 2014 IBM Corporation

Example: Taking Care of a Senior Assisted Cognition

Agent Expected behavior: • 

•  9

Inform •  •  •  Do •  • 

Alert when idle Alert when away from known locations Alert when checkup/ medicines due Send body parameters periodically … © 2014 IBM Corporation

Example: Taking Care of Oneself Personal Digital Assistants Agent

Expected behavior: • 

• 

10

Inform •  When missing meetings •  When missing social commitments •  Reminding of priorities •  … Do •  Make all cancellations / re-bookings when schedule changes •  Find alternatives to current decisions and give choices (e.g., traffic) •  …

© 2014 IBM Corporation

What Are Intelligent Agents? Agents are active, persistent software components that perceive, reason, act, and communicate. (Huhns and Singh) Software that assists people and acts on their behalf

Agents can help people and processes Agents are used for automation and control finding and filtering information personalizing your environment negotiating for services automating tedious tasks taking actions you delegate learning about you over time collaborating with other agents capturing individual and organizational knowledge sharing knowledge

01/23/12

finding and fixing problems automating complex procedures finding "best fit" procedures pattern recognition and classification predictions and recommendations negotiate and cooperate with other organizations' agents

11

IBM ABLE Agents Overview

Planning & (Classical Planning) (Static) Environment (Observable)

Goals perception (perfect)

action (deterministic) What action next?

I = initial state [I]

G = goal state Oi

Oj

(prec) Ok

Slide courtesy: Prof. Subbarao Kambhampati, ASU

Om

Oi

(effects)

[G] Dr. Biplav Srivastava

Simple Planning Example Blocks World Robot arm

A A

B

Initial State

B

Blocks

Goal State

All robots are equivalent

Representation A

B

States: ((On-Table A) (On-Table B) …) Actions: ((Name: (Pickup ?block ?robot) Precondition: ((Clear ?block) (Arm-Empty ?robot) (On-Table ?block)) Add: ((Holding ?block ?robot)) Delete: ((Clear ?block) (Arm-Empty ?robot)))…)

A

B

Planning Process Pick-up B R1 Clear A Clear A

Pick-up B R2

Clear B

Pick-up A R1

Clear B

On-Table A

On-Table B

On-Table B

Arm-Empty R1 Arm-Empty R2

Put-down A R2

Holding A R1

On-Table A

Pick-up A R2

Put-down A R1

Stack A B R1 Stack A B R2

Holding A R2 Arm-Empty R1 Arm-Empty R2

Initial State Level P-0

On A B

Level A-0

Level P-1

Level A-1

Goal State Level P-2

Manufacturing Illustration

Slide courtesy Shart Sood’s slideshare presentation on Production and Operations Management, At: http://www.slideshare.net/technomgtsood/production-operations-management

Planning in a Manufacturing Process

IMACS, A System for Computer-Aided Manufacturability Analysis Slide by Marc Berhault, 2003

Complexity Example

IMACS, A System for Computer-Aided Manufacturability Analysis Slide by Marc Berhault, 2003

IMACS Approach Illustration

IMACS, A System for Computer-Aided Manufacturability Analysis Details at: http://www.cs.umd.edu/projects/cim/imacs/

Destination Control of Elevators l 

Schindler Lifts. –  Central panel to enter

your elevator request. –  Your request is scheduled and an elevator is assigned to you. –  You can’t travel with someone going to a secure floor, emergency travel has priority, etc. l 

Modeled as a planning problem and fielded in one of Schindler’s high end elevators.

Reference: https://user.enterpriselab.ch/~takoehle/ publications/elev/elev.html

Improving Manufacturing with Knowledge (Beyond Robotics) l  Enterprise

Resource Planning l  Enterprise Integration l  Open Data and Analytics

(Manufacturing) Key Information Flow

Slide courtesy Shart Sood’s slideshare presentation on Production and Operations Management, At: http://www.slideshare.net/technomgtsood/production-operations-management

Analogical  Example:  Documenta3on  in  Health  Care  

Doctor’s Office Medical History

Prescription

Medical Claims, Prescription

Patient Bills, Policy Updates

Pharmacy Drug Claims, Prescription

Insurance Problem: Hand-offs between role-players are inefficient and failure-prone

Enterprise  Resource  Planning:  Packaged  Applica3on   l 

Packaged  Applica3on     –  Off-­‐the-­‐shelf  soBware  to  manage  common  business  func3ons  like  accoun3ng,  

payment  and  receivables,  order  management,    customer  management;  or  industry-­‐ specific    func3ons  like  clinical  trial  (pharmaceu3cal),  drilling  (mining,  oil  &  gas)   –  Businesses  buy    these  soBware  and  then  engage  service  providers  to  tailor  them   –  Enterprise  Resource  Planning  (ERP)  is  a  specific    class  of  packaged  applica3ons   l 

Market  size  (according  to  AMR  Research  [AMR  2008])   –  The  total  market  size  for  ERP  soBware  is  currently  $34.4B.  SAP  leads  with  42  %,  

followed  by  Oracle  (23%),  The  Sage  Group  (7%),  MicrosoB  Dynamics  (4%),  and   others.     –  Spending  on  services  including  consul3ng,  integra3on  and  support  for  Oracle,  SAP,   and  other  business  applica3on  vendors,  called  packaged  enterprise  applica3on   services,  was  $103B  for  2007,  and  expected  to  reach  $174B  by  2012.       –  IBM  is  a  prominent  service  provider  for  SAP  and  Oracle.   l 

Trend  is  to  move  from  Legacy  applica3ons  to  Packaged  Applica3ons  

Packaged  Applica3ons:  

Pre-­‐built,  Configurable,  Business  Processes  Automa3on  SoBware     Packaged Application

SAP

Oracle

Cross-industry: ERP, CRM, SCM, PLM, SRM, HCM, eProcurement

√

√

Industry specific solutions

> 50: Aerospace &Defense, Automotive

> 50: Aerospace &Defense, Automotive

(3), Chemicals, Consumer Products (5), Construction, High Tech (4), Industrial, Life Sciences, Mill Products (5), Mining, Oil & Gas, Media (4), Services (2), Telco, Utilities (5), Waste & Recycling, Travel & Logistics (4), Banking, Insurance, Public Sector (2), Defense (2), Healthcare, Education & Research (2), Retail, Wholesale Distribution

(2), Chemicals, Consumer Products, Construction, High Tech (6), Industrial (3), Life Sciences, Mill Products (5), Mining, Oil & Gas (3), Media (4), Services (4), Telco, Utilities (4), Waste & Recycling, Travel & Logistics (5), Banking, Insurance, Public Sector (3), Defense (2), Healthcare (2), Education & Research (3), Retail, Wholesale Distribution

Packaged Application SW Revenue: $38B (2008)

Source: AMR Research 2008

Market Share Analysis: ERP Software Worldwide, 2012 authored by Chris Pang, Yanna Dharmasthira, Chad Eschinger, Koji Motoyoshi and Kenneth F. Brant.

Health with Data.gov.in Data (Since Feb 2013)

Conjectures: - DS1 and DS2: - Is gap in staff (e.g., pharmacists) correlated with gap in infrastructure (e.g., primary health centers)? - Similarly other variants on staff and infrastructure Manufacturing Implication – manufacture vaccines that can taken without staff - DS3 and DS4: - Is increase in booster dose correlated with children mortality? That is, do states having more women covered also have lower mortality? Manufacturing Implication – where to market booster doses - DS1 and DS3: - Is gap in staff (e.g., pharmacists) correlated with number of women getting booster shots? - DS2 and DS4: - Is gap in infrastructure (e.g., health centers) correlated with children mortality? 26

Data sets * DS1: http://data.gov.in/dataset/ pharmacists-laboratorytechnicians-and-nursing-staffprimary-health-centrescommunity-health * DS2: http://data.gov.in/dataset/ shortfall-healthinfrastructure-2011population-provisionalindiaas-march-2011 * DS3: http://data.gov.in/dataset/ number-women-giventt2booster * DS4: http://data.gov.in/dataset/ under-5-mortality-rate-u5mr

Putting All Together for Manufacturing Use AI for deciding l  l  l  l 

What should one manufacture? For whom, at what price point and with what process? How should the product be maintained? How does one reduce wastage? How can we bundle services with it and increase service quality?

Relevant Key AI Areas Knowledge Representation l  Ontology l  Reasoning and Logic l  Machine Learning l  Optimization, Scheduling l  Planning l  Mechanism Design, Auction l