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38 Cards in this Set
- Front
- Back
billy beane method
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rethink normal baseball
instead find inefficiencies and exploit them -- scientific data driven approach to key decisions analyze data differently -- better models of performance = better aquisition - player acquisition - using existing players |
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method of improvement for white sox case
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goal = win more games w/ less $$
ex: more runs = better hitting vs. batting average hold opponents to few runs scored player aquisition -- work w/ undervalued assets |
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emergence of mgt.
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prior to 1900 - no pro. mgt.
today: performance of important institutions depends on pro. mgt. |
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mgt. 1.0
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mgr. as gunslinger
shoot from the hip/ gut feeling performance depends on scarce mgt. strict hierarchy |
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mgt. 2.0
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face based decisions
DDS: decision support system (database, decision models, decision-marker insight, better tech.) -- evolution: better data, better models, better tech., pushed lower in company less reliance on superstar mgt. DDS allows ordinary people to perform at extraordinary levels what we think vs. what we know competing on analytics |
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competing on analytics
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sophisticated IS and analysis across all company functions
PRIMARY FOCUS = development and maintenance of analytics FACT BASED DECISIONS IMPORTANT PART OF COMPANY CULTURE hire people w/ best analytical skills prop. metrics for eval. business processes share data and analysis with customers and suppliers TEST AND LEARN CULTURE: conduct experiments |
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objective and deliverables of Prime Supermarket case
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obj: improve performance of FJD category at prime
deliverables: - growth and mkt. share analysis for each juice and brand - variety/product mix analysis for each type of juice and brand - data analysis program to help the company make future decisions |
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changes in retail industry
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POWER SHIFT TO LARGE RETAILERS: from mfg. "push" to consumer "pull"; bulk buying, large stores, economies of scale, control of shelves at extended store chains; store bran recognition and acceptance
PRODUCT PROLIFERATION: excess demand for shelf space, slotting fees, excessive product promotions INTEGRATION OF SUPPLY CHAIN PARTNERS new system more complex -- scan data |
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retail performance review process
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retailer strategy and positioning
growth and mkt. share analysis product mix/variety analysis recommendations |
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prime's competitive positioning
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differentation: better variety, customer service, everyday low prices on key items
target mkt: food and pharmacy customers (women 25-54) primary category role: profit contributor |
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DDS concept
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database
decision models decision makers insights interactive analytical modeling process |
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DDS design principles
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reliability: output correct and consistent
audibility: user able to trace steps to generate output modifiability: capable of easy modification elegant simplicity: not complex, approp. degree of automation, approp. calculation/processing method |
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DDS effectiveness test
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purpose -- goals? what questions DDS answers?
data - type and qty./source? calculations -- which parts are calculated? what formulas are used? changes -- easy to modify model or change data |
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structured approach vs. ad hoc approach
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STRUCTURED: SDLC appropriate, simple model, many deliverables, large data set, BUILDER IS NOT THE USER
AD HOC: smaller data set, few deliverables, simple model, BUILDER IS THE USER |
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DDS design and development process
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1. design
2. build and test 3. document |
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DDS design 4 step process
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1. OUTPUT FORMS
-- METRICS: what to measure and how -- TABLES AND GRAPHS: layouts and results groupings 2. PROCESSING LOGIC -- MODELS to generate output metrics -- EXCEL TOOLS/ METHODS to populate output form 3. DATA MODEL -- SOURCES -- STRUCTURE: table row and col. design -- LINKS: among tables 4. EXCEL MULTISHEET LAYOUT -- WHAT GOES WHERE in file |
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best DDS design processes
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goal: elegant simplicity
approach: art, not science process: iterate among 4 steps until the goal is met |
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DDS design tips
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work backwards from end product
catch and fix problems early so they don't spiral out of control ACID TEST: total stranger must be able to replicate analysis with new data set with no help from system developer |
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prime supermarkets avail. data
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04-05 data for product SKU for Prime and for all firms combined competing in SE region:
-- annual sales, annual vol in sales (gal), ave. price/gal for all sales (w/ and w/out marketing promotion), data classified into 4 subcategories (generic type of juice) and 8 mfg. brands |
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growth rate metric
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% growth rate for mkt: (mkt. sales 05 - mkt. sales 04)/mkt sales 04
% growth rate for prime: (prime sales 05 - prime sales 04)/prime sales 04 |
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mkt. share metric
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prime's mkt. share = (primes sales in 05)/(total mkt. sales in 05)
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variety metrics
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variety index ratio = (# prime SKUs)/(# mkt. SKUs)
-- calculate for total categories, subcategories, and brands TOP 10/BOTTOM 10 comparison: 10 SKUs carried by prime with LOWEST sales for yr & 10 SKUs carried by prime with HIGHEST yearly sales that PRIME DOES NOT CARRY |
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uses for spreadsheets
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small simple databases
grid for drawing (flow charts) financial models stat. analysis project mgt. (task/timeline) |
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alt. methods for deliverable processing
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brute force: sort, output tables = data source for graph/chard
conditional calculations: ex: "sumif" pivot table: report and chart |
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data model
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describes how data is organized, managed, and accessed
efficient and effective organization of data: database principles tempered by analysis reqs. of excel based DSS |
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database
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collection of tables that organizes and stores related info.
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table
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lists of rows and cols. containing data about a subject
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columns
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field/attributes of a subject
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rows
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records of individual instance
set of attributes |
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database design principles
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MINIMIZE REDUNDANCY: duplicated data wastes space; increases likelihood for error
EFFICIENT USE OF STORAGE SPACE AND PROCESSING CAP. ENSURE CORRECT INFO.: reports based on wrong data lead to bad choices |
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good database design
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info. divided into subject based table to reduce redundant area
tables are linked together to retrieve data ensure accurate info. DATA MODEL IND. OF ANALYSIS AND PROCESSING REQS. |
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DBMS design process
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PURPOSE: range of expected uses
SOURCES: identify and collect required data DIVIDE INFO. INTO TABLES: each subject is separate table DEFINE COLS./FIELDS FOR EACH TABLE: info. attributes assoc. with each entity/subject SET UP TABLE RELATIONSHIPS: common fields to link across tables |
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DBMS vs. excel DSS
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DBMS: linked tables consolidated through queries
-- FIELD STRUCTURE: based on subject attribute relationships inherent in data -- independent of analysis tools and reqs. EXCEL DSS: fewer tables, consolidate all info. to be analyzed in a single table; -- FIELD STRUCTURE: based on analysis reqs. for grouping/filtering -- EXCEL ANALYSIS TOOL/method influences design |
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why analyze growth?
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doesn't stand still -- usually grows or declines
steady state is declining relative to to competitors that are growing growth masks mistakes and incompetence |
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why analyze mkt. share?
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economies of scale advantage
brand recognition -- go for the best leverage with suppliers correlated with LT profitability new venture myth of capturing small amt. of mkt. share in a big mkt. GE rule: if not 1,2,3 -- get out -- disadvantages too great to overcome |
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quadrant charts
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plot growth/mkt. share info. as quadrant bubble charts
purpose: facilitate interpretation through visualization -- 4 quadrants = benchmarks to eval. results |
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Quad. chart midpoint selection criteria
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choose midpts. for x and y axis that yield most meaningful characteristics of bubble positions in chart based on overall goal of analysis
-- look for benchmark values for x and y that provide the best context for comparison for data plotted -- choose measure external to or broader than scope of data plotted if you can ** overall mkt. share for comp. ** all stores in chain as benchmark for comparing performance |
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why analyze product mix?
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important part of consumer shopping choice
very important in space constrained environment source of differentation and comp. advantage one retail mkt. mix elements: price, promotion, place/dist., product/shelf space utilization |