OM CHAPTER 4

What are Time Series Components?

TERMS

SERIES COMPONENTS: TREND, CYCLICAL, SEASONAL, RANDOM FORECASTING SEGMENTS DATA TYPES OF Forces

ENTS: DATA TYPES OF FORECASTS, ERRORS RANDON VARIATION UNEXPLAINED DEVIATION IN THE DATA IRKEGULAR VAKIATION: ONE TIME VA

VANTATION: ONE TME VARIATION THAT CAN BE EXPLAINED TYPES OF FORECASTING QUANTITATIVE, QUALITATIVE, DELPHI METHOD QUANTITATIVE WHEN SITUATION IS STABLE AND HISTORICAL DATA EXIST (TIME SEKIES AND CASUAL FORECASTS) QUALITATIVE: WHEN SITUATION IS VAGUE AND LITTLE DATA EXISTS (EXEC JUPGTINENT, HISTORICAL ANALOGY MARKET RESEARCH) DELPHI METHOD: EACH PERSON MUST ANSWER NO REPEATED ANSWERS NAIVE APPROACH: WHAT IT WAS YESTERDAY, IT WILL BE TODAY

- BENEFITS: COST EFFECTIVE GOOD STARTING POINT, HOWEVER NOT THE

MOST ACCURATE MOVING AVERAGE APPROACH DOES NOT FORECAST TRENDS WELL REQUIRES EXTENSIVE HISTORICAL DATA, USES ANG OF A PRIOK PERIOD WEIGHTED MOVING AVERAGE APPROACH: APPLIES WEIGHT BASED ON RECENCY OR IMPORTANCE OF PRIOR DATA EXPONENTIAL SMOOTHING: MOST RECENT DATA WEIGHTED MOST FACTOR IN THE CREDIBILITY OF THE ERROR TRACKING SIGNAL : MEASURES HOW WELL FORECAST IS PREDICTING ACTUAL VALUES REGRESSION SEEKS TO FIT A LINE THROUGH VARIOUS DATA OVEK TINE FORMULAS: MOVING AVGE DEMAND IN PREVIOUS N PERIODS

What are Forecasting Segments?

NP

WEIGHTED MOVING AVG

( WEIGHT X VALUE + WEIGHT2 X VALVE 2) ETC , DEPENDING ON HOW

SUM OF THE WEIGHTS

MANY YOU NEED

EXPONENTIAL SMOOTHINGE Ft = Ft-2 + c (At-2 - Ft-2) REGRESSION - Ý = a + bx USES EXCEL

FORECASTING EKKDKS FORMVILAS

MAD ( MEAN ABSOLUTE DEVIATION)- E ACTUAL - FORECAST

IMSE ( MEAN STANDARD ERROR) =

< (FORECAST ERRORS)

N

MAPE (MEAN ABSOLUTE -

Ž 100 | ACTUALi - Forecast il / Aquali

PERCENT ERKOK)

i = 1

EXPONENTIAL SMOOTHING EXAMPLE: Don't forget about the age old question of the enzyme that links dna nucleotides together to form a new daughter strand is called

820

6055

WEEK PEMAND - 1 820

820 775

820 820 680

815.5 793

2 725.2 750 787.26 683.08

802 783.53 723.23 CALCULATE FOR ol = 0.1 Ft = Ft-1 + al (At-1 - Ft-) F4 = F 3 + 0.11 F4 = 815.5 +0.1(680-815-5) F4 = 815.5 +0.1(-135.5) F4 = 815.5-13.55

F4: 801.95

CALCULATE FOR of - 0.6 F4 = 815.5+0.61680-815.5) F4 = 815.5+ 0.6(-135.5) F4 = 815.5 - 81.3 F4 = 134.2

What is Irregular Variation?

OM CHAPTER 6

QUALITY AWARD FOR AMERICAN COMPANIES

PROVEMENT, PAKTNERSHIPS, SOLE SOURCING

For USE SOMETHINGS NEED TO BE MORE QUALITY THIAN

CHARTS, CHECKSHEE

TERMS QUALITY AWARDS MALCOLM BALOKIDGE NATIONAL QUALITY AWARD FOR AMERI IS09000 INTERNATIONAL QUALITY STANDARDS ISO 1400 ENVIRONMENTAL STANDARDS TATF 16949 FOK AUTOMOTIVE COMPANIES EDWARD DEMMING CONTINUOUS IMPROVEMENT, PAN JOSEPH JUKAN: FITNESS FOR USE, SOMETHIN OTHEKS (EXAMPLE POTATO CHIS VS PACEMAKERS We also discuss several other topics like unh microbiology

CROSBY: ZEKO PE FECTS, EVERY PRODUCT SHOULD BE QUALITY

D FIEGENBAUM TOTAL QUALITY MGMT BOOK. 7 TOOLS: FLOWCHAKTS, KUN

IS, CHECKSHEETS, HISTOG RANIS, PAKETO CHARTS, CAUSE AND EFFECT DIAGRAMS SCATTER CHARTS AND CONTROL CHARTS SIX SIGMA: A MEASUKE OF QUALITY: LES

E OF QUALITY: LESS THAN 3.4 DEFECTS PER MILLION MADE OKE: METHODS TO AVOID SIMPLE HUMAN ERROR (FOOLPROOF) QUALITY ATTRIBUTES KELIABILITY. TANGIBLES, RESPONSIVENESS, ASSN AND EMPATHY (KELIABILITY IS MOST IMPORTANT) CONCEPTS DEFINE

WALTER SHE WHARTS PDCA: COSTS OF QUALITY MEASURE

1 PLAN

- APPRAISAL COSTS ANALYZE

2 DO

- PREVENTION COSTS IMPROVE

3 CHECK

- INTERVAL FAILURE COSTS ICONTROL

+ ACT

- EXTERNAL FALURE COSTS

QUALITY ATTRIBUITES BY CONTACT:

LOW CONTACT

HIGH CONTACT

LOW LABOR HIGH CAPITAL

SERVICE TANGIBLES RESPONSIVENESS

SERVICE SHOP EMPATHY ASSURANCE

HIGH LABORE LOW CAPITAL

MASS SERVICE TANGIBLES RESPONSIVENESS

PROFESSIONAL

SEK VICE

EM PATHY

ASSURANCE

SIX SIGNA EXAMPLE:

BAD

1

GOOD

IBAD

NORMAL

BAD

I

MORE GOOD

I

BAD

STX SIGMA

IDEAL:

MATHEMATICAL EXAMPLE: AIRLINE LOSES AN AVERAGE OF 3 BAGS PER 4.000 CUSTOMEES IN ONE MON THE AVERAGE PERSON TRAVELS WITH 1.6 BAGS We also discuss several other topics like fcfn

(318,000) x 1,000,000 = 375 DEFECTS PER UNIT DPMO - 318.000 x 1.6 x 1,000,000 = 234.375 ERRORS THIS AIRLINE WOULD NOT BE CONSIDERED SIGMA QUALITY, If you want to learn more check out chemistry 111 final exam review

OM CHAPTER 65

SS CONTROU FIND OUT BEFORE THE PROCESS GETS OUT OF HAND MPUNG: "AFTER THE FACT" SEARCH THROUGH A SAMPLE TO

TERMS STATISTICAL PROCESS CONTROL ACCEPTANCE SAMPUNG: "AFTEKTI

ECIDE WHETHER THE BATCH IS GOOD OR BAD WHY DONT WE INSPECT EVERY PRODUCT? 100 ER DESTRUCTIVE TESTING (TESTING WOULD RUIN THE VO NO GO GAUGES MAKES TESTING EASIEK (THINK KINO

ARE TALL ENOUGH OK NOT EASIER THAN MEASURING EACH RIVE

EKKOR : SEND BACK BATCH YOU SHOUO HAVE KEPT Y I EKKOK: KEEP BATCH THAT YOU SHOULD HAVE SENT BICE omMoN CAUSET. A KAN DON CHANCE VARIATION If you want to learn more check out akathesi

ABLE CAUSE: THERE IS A CLEAK KEASON FOR THE VARIATION CONCEPTS

EVERY PRODUCT? TOO EXPENSIVE ) TIME CONSUMING (TESTING WOULD RUIN THE PRODUCT)

G EASIEK (THINK KINGS ISLAND HEIGHTSTICK, If you want to learn more check out chaigos

ASSIGNABLE CAUSE : !

ABSOLUTE TRUTH

INSPECTON DECISION QUALITY

DEFECTIVE

LOT IS GOOD

CORRECT

TYPE I ERROR

LOT IS BAD

TYPE II ERROR

Coreєст

OPERATIONS CHAKACTERZISTICS CURVE:

PRODUCERS KISK

ACCEPTANCE PROBABILITY

CONSUMER RISK

PEKCENT DEPECTIVE

IN CONTROL

1

OUT OF CONTROL

ADJUST

MISTAKE

Сок кест

LEAVE ALONE

CORRECT

MISTAKE

X AND R CHAKES:

99.770

Loi - 30

OF DATA K

HERE

-

UCL 3o

OUT OF CONTROL PATTERNS : - POINT OUTSIDE CONTROL LIMITS - SUDDEN SHIFT IN PROCESS AVERAGE -CYCLES -TRENDS - HUGGING CENTER LINE - HUGGING CONTROL LIMITS - INSTABILITY

HUGGING CONTROL LIMITS ERROR:

V 213 CONSECUTIVE PTS IN AN OUTER 1/3 RD V 4/5 CONSECUTIVE PTS IN AN OUTER 2/3RD V 8 CONSECUTIVE POINTS ON ONE SIDE OF THE MEAN 06/7 CONSECUTIVE POINTS INCREASING OR DECREASING

UCLT

.

.

CENTER

LOLF

FORMULAS UCL = + Az Ő UCLR=D4 R

LCLĂ - - AZŘ LOLĒ= D3 R

= AVERAGE OF ALL MEANS R - AVERAGE OF ALL RANGES