supply chain management. first appearance – financial times importance - → inventory ~ 14% of...

Supply Chain Supply Chain ManagementManagement

Supply Chain Supply Chain Management Management

• First appearance – Financial Times • Importance -

→ Inventory ~ 14% of GDP→ GDP ~ $12 trillion→ Warehousing/Trans ~ 9% of GDP→ Rule of Thumb - $12 increase in sales to = $1 savings in Supply Chain

• 1982 Peter Drucker – last frontier• Supply Chain problems can cause ≤ 11% drop in stock

price• Customer perception of company

SCORSCOR

Reference: www.supply-chain.org

Supply ChainSupply Chain All activities associated with the flow All activities associated with the flow

and transformation of goods and and transformation of goods and services from raw materials to the end services from raw materials to the end user, the customeruser, the customer

A sequence of business activities A sequence of business activities from suppliers through customers from suppliers through customers that provide the products, services, that provide the products, services, and information to achieve customer and information to achieve customer satisfactionsatisfaction

Supply ChainSupply Chain“The global network used to deliver

products and services from raw materials to end customers through an engineered flow of information, physical distribution, and cash.”

APICS Dictionary, 10th ed.

Supply Chain ManagementSupply Chain ManagementSynchronization of activities Synchronization of activities

required to achieve maximum required to achieve maximum competitive benefitscompetitive benefits

Coordination, cooperation, and Coordination, cooperation, and communicationcommunication

Rapid flow of informationRapid flow of informationVertical integrationVertical integration

Supply Chain UncertaintySupply Chain UncertaintyForecasting, lead times, batch Forecasting, lead times, batch

ordering, price fluctuations, and ordering, price fluctuations, and inflated orders contribute to inflated orders contribute to variabilityvariability

Inventory is a form of insuranceInventory is a form of insuranceDistorted information is one of Distorted information is one of

the main causes of uncertainty the main causes of uncertainty Bullwhip effectBullwhip effect

Information in the Information in the Supply ChainSupply Chain

Centralized coordination of Centralized coordination of information flowsinformation flows

Integration of transportation, Integration of transportation, distribution, ordering, and productiondistribution, ordering, and production

Direct access to domestic and global Direct access to domestic and global transportation and distribution transportation and distribution channelschannels

Locating and tracking the movement Locating and tracking the movement of every item in the supply chain - of every item in the supply chain - RFIDRFID

Information in the Information in the Supply ChainSupply Chain

Consolidation of purchasing from all Consolidation of purchasing from all supplierssuppliers

Intercompany and intracompany Intercompany and intracompany information accessinformation access

Electronic Data InterchangeElectronic Data Interchange Data acquisition at the point of origin Data acquisition at the point of origin

and point of saleand point of sale Instantaneous updating of inventory Instantaneous updating of inventory

levelslevels Visibility Visibility

Electronic BusinessElectronic Business Replacement of physical processes Replacement of physical processes

with electronic oneswith electronic ones Cost and price reductionsCost and price reductions Reduction or elimination of Reduction or elimination of

intermediariesintermediaries Shortening transaction times for Shortening transaction times for

ordering and deliveryordering and delivery Wider presence and increased visibilityWider presence and increased visibility

In Theory:

Electronic BusinessElectronic Business Greater choices and more information for Greater choices and more information for

customerscustomers Improved serviceImproved service Collection and analysis of customer data Collection and analysis of customer data

and preferencesand preferences Virtual companies with lower pricesVirtual companies with lower prices Leveling the playing field for smaller Leveling the playing field for smaller

companiescompanies Gain global access to markets & customersGain global access to markets & customers

Electronic Data InterchangeElectronic Data Interchange

Computer-to-computer exchange of Computer-to-computer exchange of business documents in a standard business documents in a standard formatformat

Quick access, better customer service, Quick access, better customer service, less paperwork, better communication, less paperwork, better communication, increased productivity, improved increased productivity, improved tracing and expediting, improves billing tracing and expediting, improves billing and cost efficiencyand cost efficiency

Bar CodesBar Codes

Computer readable codes attached to Computer readable codes attached to items flowing through the supply chainitems flowing through the supply chain

Generates point-of-sale data which is Generates point-of-sale data which is useful for determining sales trends, useful for determining sales trends, ordering, production scheduling, and ordering, production scheduling, and deliver plansdeliver plans

1234 5678

IT IssuesIT Issues Increased benefits and sophistication Increased benefits and sophistication

come with increased costscome with increased costs Efficient web sites do not necessarily Efficient web sites do not necessarily

mean the rest of the supply chain will mean the rest of the supply chain will be as efficientbe as efficient

Security problems are very real – Security problems are very real – camera phones, cell phones, thumb camera phones, cell phones, thumb drivesdrives

Collaboration and trust are important Collaboration and trust are important elements that may be new to business elements that may be new to business relationshipsrelationships

SuppliersSuppliers Purchased materials account for about Purchased materials account for about

half of manufacturing costshalf of manufacturing costs Materials, parts, and service must be Materials, parts, and service must be

delivered on time, of high quality, and delivered on time, of high quality, and low costlow cost

Suppliers should be integrated into Suppliers should be integrated into their customers’ supply chainstheir customers’ supply chains

Partnerships should be establishedPartnerships should be established On-demand delivery (JIT) is a frequent On-demand delivery (JIT) is a frequent

requirement - what is JIT and does it requirement - what is JIT and does it work?work?

SourcingSourcing Relationship between customers and Relationship between customers and

suppliers focuses on collaboration and suppliers focuses on collaboration and cooperationcooperation

Outsourcing has become a long-term Outsourcing has become a long-term strategic decisionstrategic decision

Organizations focus on core Organizations focus on core competenciescompetencies

Single-sourcing is Single-sourcing is increasingly a part increasingly a part of supplier relationsof supplier relations

How doessingle source

differ from solesource?

DistributionDistribution The actual movement of products and The actual movement of products and

materials between locationsmaterials between locations Handling of materials and products at Handling of materials and products at

receiving docks, storing products, receiving docks, storing products, packaging, and shippingpackaging, and shipping

Often called logisticsOften called logistics Driving force today Driving force today

is speedis speed Particularly important Particularly important

for Internet dot-comsfor Internet dot-coms

Distribution Centers Distribution Centers and Warehousingand Warehousing

DCs are some of the largest business DCs are some of the largest business facilities in the United Statesfacilities in the United States

Trend is for more frequent orders in Trend is for more frequent orders in smaller quantitiessmaller quantities

Flow-through facilities and automated Flow-through facilities and automated material handlingmaterial handling

Final assembly and product Final assembly and product configuration configuration (postponement) (postponement) may may be done at the DCbe done at the DC

Warehouse Management Warehouse Management SystemsSystems

Highly automated systemsHighly automated systems A good system will control item A good system will control item

slotting, pick lists, packing, and slotting, pick lists, packing, and shippingshipping

Most newer systems include Most newer systems include transportation management (load transportation management (load management/configuration), order management/configuration), order management, yard management, labor management, yard management, labor management, warehouse optimizationmanagement, warehouse optimization

Vendor-Managed InventoryVendor-Managed Inventory Not a new concept – same process used by Not a new concept – same process used by

bread deliveries to stores for decadesbread deliveries to stores for decades Reduces need for warehousingReduces need for warehousing Increased speed, reduced errors, and Increased speed, reduced errors, and

improved serviceimproved service Onus is on the supplier to keep the shelves Onus is on the supplier to keep the shelves

full or assembly lines runningfull or assembly lines running variation of JITvariation of JIT Proctor&Gamble - Wal-MartProctor&Gamble - Wal-Mart DLA – moving from a manager of supplies to DLA – moving from a manager of supplies to

a manager of suppliersa manager of suppliers Direct Vendor Deliveries – loss of visibilityDirect Vendor Deliveries – loss of visibility

Collaborative Distribution Collaborative Distribution and Outsourcingand Outsourcing

Collaborative planning, forecasting, and Collaborative planning, forecasting, and replenishment (CPFR) started by Nabiscoreplenishment (CPFR) started by Nabisco

Allows suppliers to know what is really needed Allows suppliers to know what is really needed and whenand when

Electronic-based exchange of data and Electronic-based exchange of data and informationinformation

Significant decrease in inventory levels and Significant decrease in inventory levels and more efficient logistics - maybe not!more efficient logistics - maybe not!

Companies work together for benefit of all of Companies work together for benefit of all of the supply chainthe supply chain

TransportationTransportationCommon methods are railroads, Common methods are railroads,

trucking, water, air, intermodal, trucking, water, air, intermodal, package carriers, and pipelinespackage carriers, and pipelines

RailroadsRailroads150,000 miles in US150,000 miles in USLow cost, high-volumeLow cost, high-volume Improving flexibilityImproving flexibility

intermodal serviceintermodal service double stackingdouble stacking

Complaints: slow, inflexible, large loadsAdvantages: large/bulky loads, intermodal

Award-Winning Service Award-Winning Service RecognitionRecognition

Wal-Mart Stores, Inc.Carrier of the Year – 5 years in a row

TargetOnly rail carrier to receive the Vice President’s Award

American Honda Motor CompanyPremier Partner – 4 consecutive years

Toyota’s North American Partsand Logistics Division (NAPLD)Rail Carrier of the Year – 3 consecutive years

KIACarrier of the Year

SchneiderCarrier of the Year – 3 consecutive years

Federal ExpressOnly rail carrier to receive outstandingsupplier award - 2 years in a row

United Parcel Service99.5% failure free, damage free and on-time rating from United Parcel Service every year since 1995

Most used mode in US -75% of total Most used mode in US -75% of total freight (not total weight)freight (not total weight)

Flexible, small loadsFlexible, small loadsConsolidation, Consolidation,

Internet load match sitesInternet load match sitesSingle sourcing reduces number of Single sourcing reduces number of

trucking firms serving a companytrucking firms serving a companyTruck load (TL) vs. Less Than Truck Load Truck load (TL) vs. Less Than Truck Load

(LTL)(LTL)

TruckingTrucking

AirAirRapidly growing segment of Rapidly growing segment of

transportation industrytransportation industryLightweight, small itemsLightweight, small itemsQuick, reliable, expensive Quick, reliable, expensive

(relatively expensive depending (relatively expensive depending on costs of not getting item there)on costs of not getting item there)

Major airlines and US Postal Major airlines and US Postal Service, UPS, FedEx, DHLService, UPS, FedEx, DHL

Package CarriersPackage Carriers FedEx, UPS, US Postal Service, DHLFedEx, UPS, US Postal Service, DHL Significant growth driven by Significant growth driven by

e-businesses and the move to smaller e-businesses and the move to smaller shipments and consumer desire to have it shipments and consumer desire to have it NOWNOW

Use several modes Use several modes of transportationof transportation

Expensive - relative!!Expensive - relative!! Fast and reliable - relative!!Fast and reliable - relative!! Innovative use of technologies in some Innovative use of technologies in some

casescases Online tracking – some better than othersOnline tracking – some better than others

IntermodalIntermodalCombination of several modes of Combination of several modes of

transportationtransportationMost common are truck/rail/truck Most common are truck/rail/truck

and truck/water/rail/truckand truck/water/rail/truckEnabled by the use of containers – Enabled by the use of containers –

the development of the 20 and 40 the development of the 20 and 40 foot containers significantly foot containers significantly changed the face of shippingchanged the face of shipping

~2% of all US cargo via intermodal~2% of all US cargo via intermodal

WaterWaterOne of oldest means of transportOne of oldest means of transportLow-cost, high-volume, slow Low-cost, high-volume, slow

(relative)(relative)Security - sheer volume - millions of Security - sheer volume - millions of

containers annuallycontainers annuallyBulky, heavy and/or large itemsBulky, heavy and/or large itemsStandardized shipping containers Standardized shipping containers

improve serviceimprove serviceThe most common form of The most common form of

international shippinginternational shipping

PipelinesPipelinesPrimarily for oil & refined oil Primarily for oil & refined oil

productsproductsSlurry lines carry coal or kaolinSlurry lines carry coal or kaolinHigh initial capital investmentHigh initial capital investmentLow operating costsLow operating costsCan cross difficult terrainCan cross difficult terrain

Global Supply ChainGlobal Supply ChainFree trade & global opportunitiesFree trade & global opportunitiesNations form trading groupsNations form trading groupsNo tariffs or dutiesNo tariffs or dutiesFreely transport Freely transport

goods across bordersgoods across bordersSecurity!!Security!!

Global Supply Chain Global Supply Chain ProblemsProblems

National and regional differencesNational and regional differencesCustoms, business practices, and Customs, business practices, and

regulationsregulationsForeign markets are Foreign markets are

not homogeneousnot homogeneousQuality can be a Quality can be a

major issuemajor issue

Security Security • ~ 10+ million containers annually• Customs-Trade Partnership Against Terrorism (C-

TPAT)• Port Security – SAFE Ports Act; Scanning of all

Containers• Cost - $2 billion closing of major port• 66% of all goods into US comes through 20 major

ports• 44% through LA/Long Beach• Cost of attack on major port estimated at $20

Billion

Chapter 11Chapter 11

ForecastingForecasting

Forecasting SurveyForecasting Survey• How far into the future do you

typically project when trying to forecast the health of your industry?

less than 4 months 3% 4-6 months 12%

7-12 months28% > 12 months

57%Fortune Council survey, Nov 2005

Indices to forecast health Indices to forecast health of industryof industry

• Consumer price index 51%• Consumer Confidence index 44%• Durable goods orders 20%• Gross Domestic Product 35%• Manufacturing and trade inventories and

sales 27%• Price of oil/barrel 34%• Strength of US $ 46%• Unemployment rate 53%• Interest rates/fed funds 59%

Fortune Council survey, Nov 2005

Forecasting ImportanceForecasting Importance• Improving customer demand forecasting

and sharing the information downstream will allow more efficient scheduling and inventory management

• Boeing, 1997: $2.6 billion write down due to “raw material shortages, internal and supplier parts shortages” Wall Street Journal, Oct 23, 1987

Forecasting ImportanceForecasting Importance• “Second Quarter sales at US Surgical

Corporation decline 25%, resulting in a $22 mil loss…attributed to larger than anticipated inventories on shelves of hospitals.” US Surgical Quarterly, Jul 1993

• “IBM sells out new Aetna PC; shortage may cost millions in potential revenue.” Wall Street Journal, Oct 7, 1994

Principles of ForecastingPrinciples of Forecasting• Forecasts are usually wrong• every forecast should include an

estimate of error• Forecasts are more accurate for

families or groups• Forecasts are more accurate for

nearer periods.

Important Factors to Important Factors to Improve ForecastingImprove Forecasting

• Record Data in the same terms as needed in the forecast – production data for production forecasts; time periods

• Record circumstances related to the data

• Record the demand separately for different customer groups

Forecast TechniquesForecast Techniques• Extrinsic Techniques – projections

based on indicators that relate to products – examples

• Intrinsic – historical data used to forecast (most common)

ForecastingForecasting• Forecasting errors can increase the total

cost of ownership for a product- inventory carrying costs- obsolete inventory

- lack of sufficient inventory- quality of products due

to accepting marginal products to prevent stockout

ForecastingForecasting• Essential for smooth operations of

business organizations• Estimates of the occurrence, timing,

or magnitude of uncertain future events

• Costs of forecasting: excess labor; excess materials; expediting costs; lost revenues

ForecastingForecastingPredicting future eventsPredicting future eventsUsually demand behavior Usually demand behavior

over a time frameover a time frameQualitative methodsQualitative methods

Based on subjective methodsBased on subjective methodsQuantitative methodsQuantitative methods

Based on mathematical formulasBased on mathematical formulas

Impact of Just-in-TimeImpact of Just-in-Timeon Forecastingon Forecasting

• Just in time as a inventory method• Just in time as a Continuous process

improvement program• Just in time - one on the shelf• Usage factors • Single order vs. Case order

Strategic Role of Strategic Role of ForecastingForecasting

Focus on supply chain managementFocus on supply chain management Short term role of product demandShort term role of product demand Long term role of new products, Long term role of new products,

processes, and technologiesprocesses, and technologiesFocus on Total Quality ManagementFocus on Total Quality Management

Satisfy customer demandSatisfy customer demand Uninterrupted product flow with no Uninterrupted product flow with no

defective itemsdefective itemsNecessary for strategic planningNecessary for strategic planning

Total Quality ManagementTotal Quality Management• Management approach to long term

success through customer satisfaction• Total Quality Control - process of

creating and producing quality goods and services that meet the expectations of the customer

• quality - conformance to requirements or fitness for use

Trumpet of DoomTrumpet of Doom• As forecast horizon increases, so does the

forecasting error (i.e., accuracy decreases) – shorten horizon by shortening of cycles or flow times

• Law of Large Numbers – as volume increases, relative variability decreases – forecasting error is smaller: goal – forecast at aggregate levels; collaborate; standardize parts

• Volume and activity increase at end of reporting periods – Krispy Kreme

Time FrameTime Frame Short-range, medium-Short-range, medium-

range, long-rangerange, long-range Demand BehaviorDemand Behavior

Trends, cycles, seasonal Trends, cycles, seasonal patterns, randompatterns, random

Components of Components of Forecasting DemandForecasting Demand

Time FrameTime FrameShort-range to medium-rangeShort-range to medium-range

Daily, weekly monthly forecasts of Daily, weekly monthly forecasts of sales datasales data

Up to 2 years into the futureUp to 2 years into the futureLong-rangeLong-range

Strategic planning of goals, products, Strategic planning of goals, products, marketsmarkets

Planning beyond 2 years into the futurePlanning beyond 2 years into the future

Demand BehaviorDemand BehaviorTrendTrend

gradual, long-term up or down gradual, long-term up or down movementmovement

CycleCycle up & down movement repeating over up & down movement repeating over

long time framelong time frameSeasonal patternSeasonal pattern

periodic oscillation in demand which periodic oscillation in demand which repeatsrepeats

Random movements follow no patternRandom movements follow no pattern

Forms of Forecast MovementForms of Forecast Movement

TimeTime(a) Trend(a) Trend

TimeTime(d) Trend with seasonal pattern(d) Trend with seasonal pattern

TimeTime(c) Seasonal pattern(c) Seasonal pattern

TimeTime(b) Cycle(b) Cycle

Dem

and

Dem

and

Dem

and

Dem

and

Dem

and

Dem

and

Dem

and

Dem

and

Random Random movementmovement

Figure 8.1Figure 8.1

Forecasting MethodsForecasting MethodsTime seriesTime series

Regression or causal modelingRegression or causal modelingQualitative methodsQualitative methods

Management judgment, expertise, opinionManagement judgment, expertise, opinion Use management, marketing, purchasing, Use management, marketing, purchasing,

engineeringengineeringDelphi methodDelphi method

Solicit forecasts from expertsSolicit forecasts from experts

Time Series MethodsTime Series MethodsStatistical methods using historical Statistical methods using historical

datadata Moving averageMoving average Exponential smoothingExponential smoothing Linear trend lineLinear trend line

Assume patterns will repeatAssume patterns will repeatNaive forecastsNaive forecasts

Forecast = data from last periodForecast = data from last period

Moving AverageMoving AverageAverage several periods of dataAverage several periods of dataDampen, smooth out changesDampen, smooth out changesUse when demand is stable with no Use when demand is stable with no

trend or seasonal patterntrend or seasonal patternstock market analysis - trend stock market analysis - trend

analysisanalysis

Moving AverageMoving AverageAverage several Average several

periods of dataperiods of dataDampen, smooth out Dampen, smooth out

changeschangesUse when demand is Use when demand is

stable with no trend stable with no trend or seasonal patternor seasonal pattern

Sum of Demand In n Periods

n

Simple Moving AverageSimple Moving Average

JanJan 120120FebFeb 9090MarMar 100100AprApr 7575MayMay 110110JuneJune 5050JulyJuly 7575AugAug 130130SeptSept 110110OctOct 9090

ORDERSORDERSMONTHMONTH PER MONTHPER MONTH

Example 8.1Example 8.1

JanJan 120120FebFeb 9090MarMar 100100AprApr 7575MayMay 110110JuneJune 5050JulyJuly 7575AugAug 130130SeptSept 110110OctOct 9090

ORDERSORDERSMONTHMONTH PER MONTHPER MONTH


MAMAnovnov = = 33

== 90 + 110 + 13090 + 110 + 13033

= 110 orders for Nov= 110 orders for Nov


Daug+Dsep+Doct

JanJan 120120 ––FebFeb 9090 – –MarMar 100100 – –AprApr 7575 103.3103.3MayMay 110110 88.388.3JuneJune 5050 95.095.0JulyJuly 7575 78.378.3AugAug 130130 78.378.3SeptSept 110110 85.085.0OctOct 9090 105.0105.0NovNov – – 110.0110.0

ORDERSORDERS THREE-MONTHTHREE-MONTHMONTHMONTH PER MONTHPER MONTH MOVING AVERAGEMOVING AVERAGE



JanJan 120120 ––FebFeb 9090 – –MarMar 100100 – –AprApr 7575 103.3103.3MayMay 110110 88.388.3JuneJune 5050 95.095.0JulyJuly 7575 78.378.3AugAug 130130 78.378.3SeptSept 110110 85.085.0OctOct 9090 105.0105.0NovNov – – 110.0110.0

ORDERSORDERS THREE-MONTHTHREE-MONTHMONTHMONTH PER MONTHPER MONTH MOVING AVERAGEMOVING AVERAGE


MAMA55 = =

55

ii = 1= 1 DDii

55

== 90 + 110 + 130 + 75 + 5090 + 110 + 130 + 75 + 5055

= 91 orders for Nov= 91 orders for Nov




JanJan 120120 –– – –FebFeb 9090 – – – –MarMar 100100 – – – –AprApr 7575 103.3103.3 – –MayMay 110110 88.388.3 – –JuneJune 5050 95.095.0 99.099.0JulyJuly 7575 78.378.3 85.085.0AugAug 130130 78.378.3 82.082.0SeptSept 110110 85.085.0 88.088.0OctOct 9090 105.0105.0 95.095.0NovNov – – 110.0110.0 91.091.0

ORDERSORDERS THREE-MONTHTHREE-MONTH FIVE-MONTHFIVE-MONTHMONTHMONTH PER MONTHPER MONTH MOVING AVERAGEMOVING AVERAGE MOVING AVERAGEMOVING AVERAGE

150 150 –

125 125 –

100 100 –

75 75 –

50 50 –

25 25 –

0 0 –| | | | | | | | | | |

JanJan FebFeb MarMar AprApr MayMay JuneJune JulyJuly AugAug SeptSept OctOct NovNov

Ord

ers

Ord

ers

MonthMonthFigure 8.2Figure 8.2

Smoothing EffectsSmoothing Effects

Smoothing EffectsSmoothing Effects150 150 –

125 125 –

100 100 –

75 75 –

50 50 –

25 25 –

0 0 –| | | | | | | | | | |


Ord

ers

Ord

ers

MonthMonth

ActualActual

Figure 8.2Figure 8.2


125 125 –

100 100 –

75 75 –

50 50 –

25 25 –

0 0 –| | | | | | | | | | |


3-month3-month

ActualActual

Ord

ers

Ord

ers



125 125 –

100 100 –

75 75 –

50 50 –

25 25 –

0 0 –| | | | | | | | | | |


5-month5-month

3-month3-month

ActualActual

Ord

ers

Ord

ers


Weighted Moving AverageWeighted Moving Average

Adjusts moving average Adjusts moving average method to more closely method to more closely reflect data fluctuationsreflect data fluctuations

Weighted Moving AverageWeighted Moving Average

WMAWMAnn = = ii = 1 = 1 WWii D Dii

wherewhere

WWii = the weight for period = the weight for period ii, , between 0 and 100 between 0 and 100 percentpercent

WWii = 1.00= 1.00

Adjusts Adjusts moving moving average average method to method to more closely more closely reflect data reflect data fluctuationsfluctuations

Weighted Moving Weighted Moving Average ExampleAverage Example

MONTH MONTH WEIGHT WEIGHT DATADATAAugustAugust 17%17% 130130SeptemberSeptember 33%33% 110110OctoberOctober 50%50% 9090


Weighted Moving Weighted Moving Average ExampleAverage Example

MONTH MONTH WEIGHT WEIGHT DATADATAAugustAugust 17%17% 130130SeptemberSeptember 33%33% 110110OctoberOctober 50%50% 9090

November forecastNovember forecast WMAWMA33 = = 33

ii = 1 = 1 WWii D Dii

= (0.50)(90) + (0.33)(110) + (0.17)(130)= (0.50)(90) + (0.33)(110) + (0.17)(130)

= 103.4 orders= 103.4 orders3 Month = 110 5 month =

91

Averaging method Averaging method Weights most recent data more Weights most recent data more

stronglystrongly Reacts more to recent changesReacts more to recent changes Widely used, accurate methodWidely used, accurate method

Exponential SmoothingExponential Smoothing

FFt t +1 +1 = = DDtt + (1 - + (1 - ))FFtt

wherewhereFFt t +1+1 = =forecast for next forecast for next periodperiodDDtt ==actual demand actual demand for present periodfor present periodFFtt ==previously previously determined forecast determined forecast for present periodfor present period

== weighting factor, weighting factor, smoothing constantsmoothing constant

Averaging method Averaging method Weights most Weights most

recent data more recent data more stronglystrongly

Reacts more to Reacts more to recent changesrecent changes

Widely used, Widely used, accurate methodaccurate method


Forecast for Next PeriodForecast for Next Period

• Forecast = (weighting factor)x(actual demand for period)+(1-weighting factor)x(previously determined forecast for present period)

0 > <= 1Lesser

reactionto recent demand

Greaterreaction

to recent demand

FORECAST, FORECAST, FFtt + 1 + 1

PERIODPERIOD MONTHMONTH DEMANDDEMAND (( = 0.3) = 0.3) (( = 0.5) = 0.5)

11 JanJan 3737 –– ––22 FebFeb 4040 37.0037.00 37.0037.0033 MarMar 4141 37.9037.90 38.5038.5044 AprApr 3737 38.8338.83 39.7539.7555 May May 4545 38.2838.28 38.3738.3766 JunJun 5050 40.2940.29 41.6841.6877 Jul Jul 4343 43.2043.20 45.8445.8488 Aug Aug 4747 43.1443.14 44.4244.4299 Sep Sep 5656 44.3044.30 45.7145.71

1010 OctOct 5252 47.8147.81 50.8550.851111 NovNov 5555 49.0649.06 51.4251.421212 Dec Dec 5454 50.8450.84 53.2153.211313 JanJan –– 51.7951.79 53.6153.61



yy = = aa + + bxbx

wherewhereaa == intercept (at period 0)intercept (at period 0)bb == slope of the lineslope of the linexx == the time periodthe time periodyy == forecast for demand for period forecast for demand for period xx

Linear Trend LineLinear Trend Line

Seasonal AdjustmentsSeasonal Adjustments

Repetitive increase/ Repetitive increase/ decrease in demanddecrease in demand

Use seasonal factor Use seasonal factor to adjust forecastto adjust forecast

Seasonal AdjustmentsSeasonal Adjustments

Repetitive increase/ Repetitive increase/ decrease in demanddecrease in demand

Use seasonal factor Use seasonal factor to adjust forecastto adjust forecast

Seasonal factor = Seasonal factor = SSii = =DDii

DD= demand for period/sum of demand

Seasonal AdjustmentSeasonal Adjustment

1999 12.61999 12.6 8.68.6 6.36.3 17.517.5 45.045.02000 14.12000 14.1 10.310.3 7.57.5 18.218.2 50.150.12001 15.32001 15.3 10.610.6 8.18.1 19.619.6 53.653.6Total 42.0Total 42.0 29.529.5 21.921.9 55.355.3 148.7148.7

DEMAND (1000’S PER QUARTER)DEMAND (1000’S PER QUARTER)YEARYEAR 11 22 33 44 TotalTotal


1999 12.61999 12.6 8.68.6 6.36.3 17.517.5 45.045.02000 14.12000 14.1 10.310.3 7.57.5 18.218.2 50.150.12001 15.32001 15.3 10.610.6 8.18.1 19.619.6 53.653.6Total 42.0Total 42.0 29.529.5 21.921.9 55.355.3 148.7148.7


SS11 = = = 0.28 = = = 0.28 DD11

DD42.042.0

148.7148.7

SS22 = = = 0.20 = = = 0.20 DD22

DD29.529.5

148.7148.7 SS44 = = = 0.37 = = = 0.37 DD44

DD55.355.3

148.7148.7

SS33 = = = 0.15 = = = 0.15 DD33

DD21.921.9

148.7148.7


1999 12.61999 12.6 8.68.6 6.36.3 17.517.5 45.045.02000 14.12000 14.1 10.310.3 7.57.5 18.218.2 50.150.12001 15.32001 15.3 10.610.6 8.18.1 19.619.6 53.653.6Total 42.0Total 42.0 29.529.5 21.921.9 55.355.3 148.7148.7


SSii 0.280.28 0.200.20 0.150.15 0.370.37


1999 12.61999 12.6 8.68.6 6.36.3 17.517.5 45.045.02000 14.12000 14.1 10.310.3 7.57.5 18.218.2 50.150.12001 15.32001 15.3 10.610.6 8.18.1 19.619.6 53.653.6Total 42.0Total 42.0 29.529.5 21.921.9 55.355.3 148.7148.7


SSii 0.280.28 0.200.20 0.150.15 0.370.37

Forecast for 2002 using simple 3 year moving ave

4550.153.6

148.749.56667

50*.28 14Forecast for 1st qtr 2002

Forecast AccuracyForecast Accuracy

Find a method which minimizes Find a method which minimizes errorerror

Error = Actual - ForecastError = Actual - ForecastMean Absolute Mean Absolute

Deviation (MAD)Deviation (MAD)

Mean Absolute Mean Absolute Deviation (MAD)Deviation (MAD)

wherewhere tt = the period number= the period number

DDtt = demand in period = demand in period tt FFtt = the forecast for period = the forecast for period tt

nn = the total number of periods= the total number of periods= the absolute value= the absolute value

DDtt - - FFtt nnMAD =MAD =

Forecast ControlForecast ControlReasons for out-of-control forecastsReasons for out-of-control forecasts

Change in trendChange in trend Appearance of cycleAppearance of cycle Weather changesWeather changes PromotionsPromotions CompetitionCompetition PoliticsPolitics

Tracking SignalTracking Signal• Tracking Signal establishes control limits -

usually +/- 3 MAD• The greater the tracking signal the more

the demand exceeds the forecast• Sum(Demand-Forecast)/Mean Absolute

Deviation• Sometimes called Running Sum of

Forecasting Error

Tracking SignalTracking SignalCompute each periodCompute each periodCompare to control limitsCompare to control limitsForecast is in control if within limitsForecast is in control if within limits

Use control limits of Use control limits of +/- 2+/- 2 to to +/- 5 MAD+/- 5 MAD

Tracking signal = =Tracking signal = =((DDtt - - FFtt))

MADMADEE

MADMAD

Tracking Signal ValuesTracking Signal Values

11 3737 37.0037.00 –– –– ––22 4040 37.0037.00 3.003.00 3.003.00 3.003.0033 4141 37.9037.90 3.103.10 6.106.10 3.053.0544 3737 38.8338.83 -1.83-1.83 4.274.27 2.642.6455 4545 38.2838.28 6.726.72 10.9910.99 3.663.6666 5050 40.2940.29 9.699.69 20.6820.68 4.874.8777 4343 43.2043.20 -0.20-0.20 20.4820.48 4.094.0988 4747 43.1443.14 3.863.86 24.3424.34 4.064.0699 5656 44.3044.30 11.7011.70 36.0436.04 5.015.01

1010 5252 47.8147.81 4.194.19 40.2340.23 4.924.921111 5555 49.0649.06 5.945.94 46.1746.17 5.025.021212 5454 50.8450.84 3.153.15 49.3249.32 4.854.85

DEMANDDEMAND FORECAST,FORECAST, ERRORERROR EE = =PERIODPERIOD DDtt FFtt DDtt - - FFtt ((DDtt - - FFtt)) MADMAD


11 3737 37.0037.00 –– –– ––22 4040 37.0037.00 3.003.00 3.003.00 3.003.0033 4141 37.9037.90 3.103.10 6.106.10 3.053.0544 3737 38.8338.83 -1.83-1.83 4.274.27 2.642.6455 4545 38.2838.28 6.726.72 10.9910.99 3.663.6666 5050 40.2940.29 9.699.69 20.6820.68 4.874.8777 4343 43.2043.20 -0.20-0.20 20.4820.48 4.094.0988 4747 43.1443.14 3.863.86 24.3424.34 4.064.0699 5656 44.3044.30 11.7011.70 36.0436.04 5.015.01

1010 5252 47.8147.81 4.194.19 40.2340.23 4.924.921111 5555 49.0649.06 5.945.94 46.1746.17 5.025.021212 5454 50.8450.84 3.153.15 49.3249.32 4.854.85

DEMANDDEMAND FORECAST,FORECAST, ERRORERROR EE = =PERIODPERIOD DDtt FFtt DDtt - - FFtt ((DDtt - - FFtt)) MADMAD

TS3 = = 2.006.103.05

Tracking signal for period 3


11 3737 37.0037.00 –– –– –– ––22 4040 37.0037.00 3.003.00 3.003.00 3.003.00 1.001.0033 4141 37.9037.90 3.103.10 6.106.10 3.053.05 2.002.0044 3737 38.8338.83 -1.83-1.83 4.274.27 2.642.64 1.621.6255 4545 38.2838.28 6.726.72 10.9910.99 3.663.66 3.003.0066 5050 40.2940.29 9.699.69 20.6820.68 4.874.87 4.254.2577 4343 43.2043.20 -0.20-0.20 20.4820.48 4.094.09 5.015.0188 4747 43.1443.14 3.863.86 24.3424.34 4.064.06 6.006.0099 5656 44.3044.30 11.7011.70 36.0436.04 5.015.01 7.197.19

1010 5252 47.8147.81 4.194.19 40.2340.23 4.924.92 8.188.181111 5555 49.0649.06 5.945.94 46.1746.17 5.025.02 9.209.201212 5454 50.8450.84 3.153.15 49.3249.32 4.854.85 10.1710.17

DEMANDDEMAND FORECAST,FORECAST, ERRORERROR EE = = TRACKINGTRACKINGPERIODPERIOD DDtt FFtt DDtt - - FFtt ((DDtt - - FFtt)) MADMAD SIGNALSIGNAL

ForecastingForecasting• Long Term – location, capacity, new

product design• Short Term – production, inventory

control, labor levels, cost controls

Questions?

Next Class – After your Next Class – After your BreakBreak

• Chap 6• Chapter 12• The Beer Game

supply chain management. first appearance – financial times importance - → inventory ~ 14% of...

Documents