4/30/2008 mit field intelligence lab 1web.mit.edu/edmund_w/www/seminar12008schuster.pdf ·...

4/30/2008 1MIT Field Intelligence Lab

IMPROVED NEW PRODUCT FORECASTING THROUGH VISUALIZATION OF SPATIAL DIFFUSIONTHROUGH VISUALIZATION OF SPATIAL DIFFUSION

SEMINAR 1

APRIL 28, 2008

Edmund W. Schuster

MIT Field Intelligence Lab

Stuart J. Allen

Penn State Erie – The Behrend College

H.G. (Ken) LeeH.G. (Ken) Lee

MIT Laboratory for Manufacturing and Productivity


WHAT I WILL DISCUSS TODAYSCUSS O

I. An introduction to the Field Intelligence Lab

II. Marketing, New Product Introductions, Spatial Diffusion

fIII. The M Language for Free Form Text Analysis

IV. Introduction to the Open System for Master Production Scheduling (OSMPS), Tues. Topic


Please feel free to ask questions during the presentation, or to make relevant comments

4/30/2008 MIT Field Intelligence Lab 4

The Danger of Incremental Thinking in Engineering and Business

Space Station Design

Goodyear Aircraft Corporation, 1960’s


MIT LABORATORY FOR MANUFACTURING AND PRODUCTIVITY DEPT OF MECH ENGAND PRODUCTIVITY, DEPT. OF MECH. ENG.

• The Data Center Program– Broad effort to build an interoperable data and mathematical modeling network – M

Language– Currently focused on the defense industry– The “prototype” for the M Language is at mlanguage.mit.edu

• MIT Field Intelligence Lab– New organization at MIT

F d f – Focused on four areas• Agricultural systems productivity (green field)• Environmental sampling• The megalopolis program (eventually independent)

Marketing spatial diffusion• Marketing spatial diffusion– Broad treatment of spatial analysis

• A “field”• Latin - tractus


MY GENERAL VIEW OF THE FUTUREG O U U

Th i i f 1) k i i 2) i i h l dThe integration of 1) marketing science, 2) engineering technology, and3) supply chain management.

Supply chains that sense and respond to the physical world, and include an assessment of risk.

This requires an Intelligent Infrastructure for management, control, automation and interaction.


THREE WAYS TO GROW REVENUES O G O U

• Stimulate sales of existing products through improved pricing, promotion, and advertising

– At best an incremental solution for revenue enhancement because of intense competition, domestically and globally

• Acquisition of businesses lines or entire companies– The majority of acquisitions and mergers are not successful within five years– It is difficult to integrate two different businesses, although the process is becoming

more efficient

• Introduction of new productsIntroduction of new products– Great potential, however, also high risk– Consumer goods industry survives by introducing new products– Most business models assume some level of new product activityp y


THE ROLE OF THE BAR CODEO O CO

• Provide product data on the volume sold through retailers

• The bar code was instrumental in creating an entire family of market mix models

– Measure customer response to promotion and advertising– Data for better trade-offs

• Marketing remains a “fact-based art.”


CHANGES IN MARKETINGC G S G

• The traditional notion of mass marketing and the concept of the economies of scale for advertising are changing rapidly.

• In practice, only about half of all advertising is successful. – Cable television, Internet, On-demand programming, social networks, TiVo,

Video games, GOOGLE

• Advertisers no longer “treat consumers as homogeneous masses ” Advertisers no longer treat consumers as homogeneous masses.

• Network television is less effective

• Improved Internet search will further decrease the influence of traditional advertising

– The M Language is a basis for the next generation of search toolsg g g


MARKETING INNOVATIONSG O O S

• Walt Disney Corporation – portable media players for movies

• In-store, interactive marketing– Promotional message at the instant of purchaseg p– Interactive signage, increase sales by 10%

• Northwest Airlines, Billboards, 2-D Bar Codes, and Cell Phones– More than 30 millions cell phones in Japan equipped to read 2-D bar codes


NEW PRODUCT LAUNCHESO UC U C S

• The average customer encounters over 1 million different stock keeping units (SKUs) across all channels of distribution

• The typical family gets 85% of their needs from only 150 SKUsyp y g y

• Each year there are over 10,000 new product introductions in the non-durable consumer goods segment alonedurable consumer goods segment alone

– For durable products, the number of product launches is much higher

• Sales “take-off” critical to cover slotting allowancesSales take off critical to cover slotting allowances


SPATIAL DIFFUSIONS US O

• Geographic forces that affect adoption by individual customers– rate and pattern of adoption – Based on type of advertising, demographics, distance to retail outlets– Pricing, promotion, tactical product positioning

• “Managers who understand the geography of the processes by which consumers change their behaviors can be much more successful in launching new initiatives and can make much better use of their resources while doing so.”Allaway, Arthur W., David Berkowitz and Giles D’Souza (2003), ”Spatial Diffusion of a New Loyalty Program Through a Retail

Market,” Journal of Retailing, Vol. 79, pp 137 – 151.

• “a friend, expert, or relative” influences up to 80% of all purchases.”Dichter, Ernest (1966), “How Word of Mouth Advertising Works,” Harvard Business Review, Nov. – Dec., p. 147.

• A great need for information technology and models of spatial diffusion4/30/2008 MIT Field Intelligence Lab 14

SPATIAL DIFFUSION (CONTINUED)S US O (CO U )

• Bass models and the analysis of product “take-off” in developing markets

– Approaches rely on the aggregation of data

• The First Study– Whyte, William H., Jr. (1954), “The Web Word of Mouth,” Fortune, November, pp. 140.– The first treatment of space as part of marketing

Th i t f “ l t ”– The importance of “clusters”


THREE IMPORTANT STEPSO S S

1. Lead adopters

2. Neighborhood effects

3. Consolidation of adoption


From Rogers et al., 2006

Garber, Tal, Jacob Goldenberg, Barak Libai, and Eitan Muller (2004), “From Density to Destiny: Using Spatial Dimension of Sales Data for Early Prediction of New Product Success,” Marketing Science, Vol. 23, No. 3, pp. 419-428.


IMPLICATIONS FOR SUPPLY CHAIN MANAGERSC O S O SU C G S

• Rate of diffusion is very important in determining the overall success of a new product

– Early warning of a success or a “dud”

• Change marketing strategies such as advertisement, in-store promotion, and pricing

• New product forecasting– Time series method– Inventory, obsolescence, and out-of-stock

• Optimization of spending on advertisement– The stage of diffusion?


VISUALIZING SPATIAL DIFFUSIONSU G S US O


ALLAWAY, BERKOWITZ, AND GILES 2003, ,


THE DATA

• Data for new product sales must contain some aspect of time and location for a specific geographical area.

• Single Source Data - contains information on several independent i bl d ll f l l i t th i t f ff t ith th variables and allows for causal analysis at the point of effect, either the

store or the household level.– impact of promotions, advertising, coupons, or the local competitive dynamics

between stores. The level of detail is often on an individual product basis.

• The credit card approach– Video and the Holiday Season

• Spatial sampling the best practical approach– Prof. Sanjay Sarma, Ajay A. Deshpande, and the Field Intelligence Lab– Fruit fly detection – Grapefruit and Fly Net…Citrus Greening and Cankery p y g


IN-STORE INFORMATIONAL KIOSKS O O O OS

Self-service, interactive, networked terminals in the

• Product information

aisles for:

• Comparisons• Targeted marketing• Promotions

Adapted from ReadyTouch, Incwww.readyTouch.comy


MACRO APPROACH - BLOGS AND FREE FORM TEXT ANALYSISFORM TEXT ANALYSIS

• CASCADES PROJECT: Cost-effective Outbreak Detection in Networks– http://www.cs.cmu.edu/~jure/blogs/– Top 100 Blogs to read – “The goal of our system when looking at blogs is to detect the big stories as early on

and as close to the source as possible ” and as close to the source as possible, – Ajay A. Deshpande of MIT Field Intelligence Lab identified this resource as related to

our work in spatial diffusion.

• This approach is valuable, however, it assumes you can read everything quickly


“FOR 2004, SHIPMENTS OF STORAGE DEVICES EQUALED , QFOUR TIMES THE SPACE NEEDED TO STORE EVERY WORD EVER SPOKEN DURING THE ENTIRE COURSE OF HUMAN

HISTORY ” HISTORY.

LYONS, DANIEL (2004), “TOO MUCH DATA,” FORBES, DECEMBER 13.


SEVERAL TYPES OF WEBS

• The Web of Information– HTML and the World Wide Web

• The Web of Things– Linking physical objects together using the EPCGlobal Network and RFID

• The Web of Abstractions– Building a network of mathematical models– Link models together– Link data to models– Computer languages & protocols to create a free flow of models in a network

(Internet or Intranet)


PROBLEMPROBLEM

40% to 60% annual data increase


SPEED READING

Princeton Physics and Mathematics Library, Princeton University

FREE FORM TEXT ANALYSISO S S

GLOBAL RFID: The Value of the EPCGlobal Network for Supply Chain ManagementNetwork for Supply Chain Management

Edmund W. SchusterSt t J AllStuart J. AllenDavid L. Brock


TRANSLATED FROM FRENCH TO ENGLISH (A l )ENGLISH (Amazon translator)

Total Rfid: Been worth The of the Epcglobal Network for Supply

Chain Management (Connected)Chain Management (Connected)

Ed d W h kEdmund W. shoemakersStuart J. everythingDavid into L. break

4/30/2008 MIT Field Intelligence Lab 3011/6/1007 MIT Field Intelligence Lab 30

TRANSLATED FROM FRENCH TO ENGLISH (AOL translator)

TOTAL RFID: the Value of Network EPCGlobal for the Administration

of Spare Chain


AMAZON.COM – KEYWORD “AUTO-ID”O CO O U O

#74 in Automotive Accessories


BlahBlahFish comBlahBlahFish.com


M TECHNOLOGIESC O OG S

M Ontologiesvehicle.1

missle 1

type ofM Ontologies missle.1

A “centralized dictionary;” with unambiguous relationships

M Data

Interoperable data; understanding what data tags mean

M Machines m dataf( )m data

4/30/2008MIT Field Intelligence Lab

34Interoperable mathematical modes, one model with many different applications

WORDS AND SEMANTICSO S S CS

Cell n. – a manufacturing cell, in which a group of workers and/or machines work together as a team to produce and/or machines work together as a team to produce dedicated set of products or assemblies.

Cell n. – usually microscopic structure containing nuclear and cytoplasmic material enclosed by a semi-permeable membrane and in plants a cell wall; the basic structural unit of all and, in plants, a cell wall; the basic structural unit of all organisms.


M DICTIONARYC O

date.1 n. – particular day specified as the time something happens. July 4, 1776 was the date of the signing of the Declaration of Independence. The date of the election is set by lawlaw.

Data Format ISO 8601 (string) the international standard for date and time issued ISO 8601 (string) – the international standard for date and time issued

by the International Organization for Standardization (ISO). pattern: ([0-9]{4})(-([0-9]{2})(-([0-9]{2})(T([0-9]{2}):([0-pattern: ([0 9]{4})( ([0 9]{2})( ([0 9]{2})(T([0 9]{2}):([09]{2})(:([0-9]{2})(\.([0-9]+))?)?(Z|(([-+])([0-9]{2}):([0-9]{2})))?)?)?)?


MRP EXPLOSION FOR WORDSOS O O O S

Ontology – relationships between words

vehicle.1“type of”

automobile.1

address.1“attribute of”

ZIP_code.1


NATURAL LANGUAGE PROCESSING(FROM THE MIT DATA CENTER PROGRAM)(FROM THE MIT DATA CENTER PROGRAM)


QUERY LANGUAGE(FROM THE MIT DATA CENTER PROGRAM)(FROM THE MIT DATA CENTER PROGRAM)

[relation abbreviation:depth:+/-,relation abbreviation:depth:+/-,...]


NEWS ANALYSIS(FROM THE MIT DATA CENTER PROGRAM)(FROM THE MIT DATA CENTER PROGRAM)


M – THE BIG PICTUREM THE BIG PICTURE

• Sensors“the number of deployed sensors will dwarf the number of personal

computers by a thousand fold in 2010”

Ferguson, Glover, Sanjay Mathur and Baiju Shah (2005), “Evolving From Information to Insight ” Sloan Management Review 46:2 p 52Information to Insight, Sloan Management Review 46:2, p. 52.

• Interoperable Data– Something like Adobe AcrobatSomething like Adobe Acrobat

• A Network of Models– Capture 50 years of modeling, link everything togetherp y g, y g g– Something like eBay– The future of ERP…Packaged Software?– SAP and DEC, Analog Devices


M – THE BIG PICTURE (COMPUTER SCIENCE)G C U (CO U SC C )

• An open system• M works with existing data• The language is designed to be used with existing standards, including

the W3C• Achieve communication when target is un-known• Address the “many-to-many” problem• A way to deal with semantics that is different from previous Artificial • A way to deal with semantics that is different from previous Artificial

Intelligence approaches


TOPIC FOR TUESDAYO C O U S

• The Open System for Master Production Scheduling– A practical application of the M Language– First commercial grade system

• Make-to-stock Industry– Consumer goods– Repetitive manufacturing

• Software as a Service– The next stage for ERP


SUMMARYSU

• New Initiatives at MIT – Field Intelligence Lab

• Consumer goods do not sell evenly through space

• Systems to detect spatial diffusion are a growth market

f f• Blogs provide useful information

• The M Language


MLANGUAGE MIT EDUMLANGUAGE.MIT.EDU

DATACENTER.MIT.EDU

FIL.MIT.EDU (JUNE 1, 2008)

WWW.ED-W.INFO


Edmund W. SchusterField Intelligence Lab and Data Center ProgramLaboratory for Manufacturing and ProductivityMassachusetts Institute of Technology77 Massachusetts Avenue, 35-135Cambridge, MA [email protected](c) 603 759 5786(c) 603-759-5786


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