1、时间数列分析(英文)2022-12-22O:/Network/Path/Filename.ppt-2-Benefits and Uses of Time Series vBenefits of time seriesMonitor sales performance over time remove variation in monthly sales caused by calendar differences and seasonality that can conceal potential problems with sales Accurately determine the dir
2、ection and rate of growth/decline in sales Quickly identify changes in sales trends and correlate them to factors affecting sales industry,company,competitionImprove decision making regarding sales and marketing actionsvUses of time seriesAssess current sales performance and evaluate the effectivene
3、ss of sales programsDetermine underlying sales trend and project year end salesEstablish appropriate budgets for next year and estimate monthly budget spreadsTime series analysis is the primary sales analysis technique at A-B2022-12-22O:/Network/Path/Filename.ppt-3-Time Series AnalysisvWhat is Time
4、Series Analysis?vHow are Time Series plots developed?vWhat are the advantages of Time Series Analysis?vWhat are Time Series used for?2022-12-22O:/Network/Path/Filename.ppt-4-What is Time Series Analysis?vTime series analysis is a statistical technique used to analyze and monitor sales volume over ti
5、me.2022-12-22O:/Network/Path/Filename.ppt-5-Why Time Series?Beer Sales050100150200250199819992000200120022003Thousand BarrelsvBeer sales are highly seasonalvIt is very difficult to evaluate monthly sales over time.2022-12-22O:/Network/Path/Filename.ppt-6-How do time series work?vMonthly variation in
6、 sales is caused by two major factorsSeasonalitySelling Days(calendar effects)vTime Series technique statistically removes the effects of these two factorsvTime Series technique uses the X-11 procedure for seasonal adjustmentsThe X-11 procedure was developed by the U.S.Bureau of Census in the 1950s.
7、It was brought to A-B in the early 1960s and has become the standard for reporting sales.2022-12-22O:/Network/Path/Filename.ppt-7-How do time series adjust sales?vA selling day adjustment factor for each month is computed and applied to the raw sales This factor allows you to compare months as if th
8、ey had the same number of selling days e.g.accurately compare the June this year vs.June last yearvA seasonal factor is computed and applied to the selling day adjusted sales This factor,when applied,gives you monthly data directly comparable to any other month e.g.accurately compare June this year
9、with May this year2022-12-22O:/Network/Path/Filename.ppt-8-Selling Days All other things being equal,sales in Aug-03 would decrease 4.8%because of one less selling day.In order to compare the two months Aug-03 sales will have to be adjusted up+4.8%.SMTWTFSSMTWTFS121231.00.01.01.00.03456789456789100.
10、01.01.01.01.01.00.00.01.01.01.01.01.00.010111213141516111213141516170.01.01.01.01.01.00.00.01.01.01.01.01.00.017181920212223181920212223240.01.01.01.01.01.00.00.01.01.01.01.01.00.024252627282930252627282930310.01.01.01.01.01.00.00.01.01.01.01.01.00.0310.0August 2003August 2002Aug-2003 has 21 selling
11、 daysAug-2002 has 22 selling days2022-12-22O:/Network/Path/Filename.ppt-9-Seasonality vSeasonality is expressed as an index for a month compared to an average month.vA month where sales were 20%higher than average would have a seasonal factor of 120.vA month which was 10%lower than average would hav
12、e a seasonal factor of 90.JanFebMarApr MayJunNo Seasonality100100100100100100Strong Seasonality607580120140120Jul Aug SepOct Nov DecNo Seasonality100100100100100100Strong Seasonality118807562120150020406080100120140160JanFebMarAprMayJunJulAugSepOctNovDecStrong SeasonalityNo Seasonality2022-12-22O:/N
13、etwork/Path/Filename.ppt-10-Adjusting Sales Raw Sales X Selling Day Factor Seasonal FactorSeasonally Adjusted Sales=MonthActual Sales(M bbls)Selling Day FactorSeasonal FactorAdjusted SalesJun-03211X1.0041.210=175 Jul-03221X0.9581.212=175 Aug-03196X1.0541.190=174 Sep-03160X1.0040.948=169 2022-12-22O:
14、/Network/Path/Filename.ppt-11-How do time series work?Raw SalesSelling Day AdjustedSeasonally Adjusted2022-12-22O:/Network/Path/Filename.ppt-12-Dissecting a Time Series Plot 0200400600800100012001400160018002000199419951996199719981999Annualized Sales in M bblsAnnualized Sales tells us how big the m
15、arket is.Trend Line tells us the direction of sales based on past&present performanceIrregular variations shows us the impact of market place actionsSTRs;Ontario STCsData Description tells us the type of data plotted2022-12-22O:/Network/Path/Filename.ppt-13-Advantages of Time Series vAdvantages of t
16、ime series:Removes variation in monthly sales caused by calendar differences and seasonalityHelp us to accurately estimate the direction and rate of sales growth/declineThey are an improvement over other methods such as year-over-year growth or moving averages because they show us what is happening
17、sooner an early warning of changing sales conditionsvTime series significantly improve decision makingAllows us to take corrective action soonerAllows us to take the right corrective actionHelps to establish appropriate sales objectives2022-12-22O:/Network/Path/Filename.ppt-14-Advantages of Time Ser
18、iesvIf the time series shows a relative smooth pattern from one year to the next the trend and the year over year growth would provide roughly the same reading.vBut,if there was a significant market event or change,the year over year trends will be misleading051015202519981999+50%2022-12-22O:/Networ
19、k/Path/Filename.ppt-15-Misleading Growth Rates024681012141619981999Positive Trend:Flat%Change0%024681012141619981999Trend Flat;Positive%Change+21%2022-12-22O:/Network/Path/Filename.ppt-16-More Misleading Growth Rates 024681012141619981999Trend Flat;Negative%Change-21%051015202519981999Trend Negative
20、;Positive%Change+15%2022-12-22O:/Network/Path/Filename.ppt-17-What are time series used for?vAt A-B we use time series toAssess current sales performanceDevelop current year sales projectionsPYE(projected year-end)Forecast next year sales develop budgets and monthly spreadsOther quantitative sales a
21、nalysis2022-12-22O:/Network/Path/Filename.ppt-18-Assessing Sales Performance Beer Sales050100150200250199819992000200120022003Thousand BarrelsHow is our YTD performance?2022-12-22O:/Network/Path/Filename.ppt-19-Assessing Sales PerformanceBeer Sales05001,0001,5002,0002,500199819992000200120022003Annu
22、alized Sales in US bbls(in 1000s)Budget:2,160M bblsPYE:2,060M bbls%Change vs.Year AgoSep-03:+4.1%;SDA-0.9%YTD Sep-03:+1.2%;SDA+1.2%2022-12-22O:/Network/Path/Filename.ppt-20-Beer Sales05001,0001,5002,0002,500199819992000200120022003Annualized Sales in US bbls(in 1000s)Estimating PYE If there is no ch
23、ange in the business environment sales will continue on current trend.Points off the trend line can be used to estimate monthly sales.Underlying TrendPredicted2022-12-22O:/Network/Path/Filename.ppt-21-Underlying Trend Underlying trend is a trend line that best describes the current sales growth rate
24、.It is the collective representation of all underlying factors that are influencing sales industry,competition,and company specific,etc.It is determined using a best-fit line to a set of points on the time series.The points are selected based on in-depth understanding of the underlying factors influ
25、encing sales,how they have changed over time,and how they will likely change in the future.Points of inflection on the time series often signal changes in the underlying factors and hence the underlying trend.2022-12-22O:/Network/Path/Filename.ppt-22-Beer Sales05001,0001,5002,0002,500199819992000200
26、120022003Annualized Sales in US bbls(in 1000s)Estimating PYEPredictedActual SalesPredictedSeasonally Adj.Sales Selling Day Factor X Seasonal FactorMonthly Sales=ActualTrend(AnnualizedSDAF SeasonalMonthPYE&Deseasonalized)EstimateJ120 120 F118 118 M138 138 A166 166 M195 195 J211 211 J221 221 A196 196
27、S160 160 O2,106 0.9590.932 12171 171 N2,110 1.1100.914 12145 145 D2,113 1.0041.239 12217 217 2,059 2003 PYE Estimate(M bbls)2022-12-22O:/Network/Path/Filename.ppt-23-Beer Sales05001,0001,5002,0002,5001998199920002001200220032004Annualized Sales in US bbls(in 1000s)%Change vs.Year AgoSep-03:+4.1%;SDA
28、-0.9%YTD Sep-03:+1.2%;SDA+1.2%Establishing Budgets and Spreads Given our YTD Sep-2003 performance what would be an appropriate budget for next year and how should that volume be spread by month?2022-12-22O:/Network/Path/Filename.ppt-24-Establishing Budgets and Spreads Beer Sales2,12305001,0001,5002,
29、0002,5001998199920002001200220032004Annualized Sales in US bbls(in 1000s)2003Trend(AnnualizedSDAF SeasonalMonth%SDA&Deseasonalized)Estimate vs.2003J120 2,117 1.0040.675 12119 +3.4%F118 2,121 1.0540.754 12126 +6.8%M138 2,124 0.9170.852 12165 +10.0%A166 2,128 1.0540.995 12167 +0.7%M195 2,131 1.0541.08
30、2 12182 -1.9%J211 2,135 0.9591.210 12225 +1.6%J221 2,138 1.0041.214 12215 +1.8%A196 2,142 1.0041.185 12211 +2.4%S160 2,146 1.0040.952 12170 +6.2%O171 2,149 1.0540.932 12158 +1.9%N145 2,153 1.0040.914 12163 +2.2%D217 2,156 1.0041.239 12222 +2.0%2,059 2,123 +3.1%2004 Budget(M bbls)2022-12-22O:/Network
31、/Path/Filename.ppt-25-Another Example Using Time Series0.06.412.819.225.632.038.444.851.257.664.0Price IncreaseAnnualized STRs in M BBLS199819992000200120022003Elasticity CalculationP1:18.99;P2:20.45 i.e.+7%V1:44.5;V2:38.5 i.e.-14%Elasticity=-2.0Estimating the price elasticityPrice=P1Volume=V1Price=
32、P2Volume=V22022-12-22O:/Network/Path/Filename.ppt-26-Conclusions Time Series technique is a very useful sales analysis tool it is the standard for reporting and analyzing sales at A-B It is a powerful decision making tool for assessing sales performance,making accurate forecasts,and establishing app
33、ropriate budgets and spreads.2022-12-22O:/Network/Path/Filename.ppt-27-Time Series Thru September3,552.005001,0001,5002,0002,5003,0003,5004,0004,5001998199920002001200220032004Annualized Sales Metric Tons in Thousands%Change:+61.2%+20.9%Change vs.Year AgoSep-03:-2.5%;SDA-7.2%YTD Sep-03:+9.0%;SDA+9.0
34、%2004 Forecast:3,552.0M Met.Tons;+7.9%vs.032003 PYE:3,290.8M Met.Tons;+10.2%vs.02Trend+8.2%Shipments2022-12-22O:/Network/Path/Filename.ppt-28-Time Series Thru OctoberShipments3,471.805001,0001,5002,0002,5003,0003,5004,0004,5001998199920002001200220032004Annualized Sales Metric Tons in Thousands%Chan
35、ge:+61.2%+20.9%Change vs.Year AgoOct-03:-6.6%;SDA-6.6%YTD Oct-03:+7.9%;SDA+7.9%2004 Forecast:3,471.8M Met.Tons;+8.0%vs.032003 PYE:3,215.7M Met.Tons;+7.6%vs.02Trend+6.7%2022-12-22O:/Network/Path/Filename.ppt-29-Qingdao RegionShipments772.501002003004005006007008009001998199920002001200220032004Annual
36、ized Sales Metric Tons in Thousands%Change:+1.9%+17.8%Change vs.Year AgoOct-03:-15.7%;SDA-15.7%YTD Oct-03:+2.5%;SDA+2.5%2004 Forecast:772.5M Met.Tons;+8.3%vs.032003 PYE:713.5M Met.Tons;+2.0%vs.02Trend+3.9%2022-12-22O:/Network/Path/Filename.ppt-30-South RegionShipments630.2010020030040050060070080019
37、98199920002001200220032004Annualized Sales Metric Tons in Thousands%Change:+102.3%+47.8%Change vs.Year AgoOct-03:+6.9%;SDA+6.9%YTD Oct-03:+1.9%;SDA+1.9%2004 Forecast:630.2M Met.Tons;+3.9%vs.032003 PYE:606.7M Met.Tons;+2.4%vs.02Trend+3.1%2022-12-22O:/Network/Path/Filename.ppt-31-North RegionShipments
38、741.601002003004005006007008009001998199920002001200220032004Annualized Sales Metric Tons in Thousands%Change:+174.3%+11.0%Change vs.Year AgoOct-03:-5.7%;SDA-5.7%YTD Oct-03:+15.2%;SDA+15.2%2004 Forecast:741.6M Met.Tons;+7.8%vs.032003 PYE:687.9M Met.Tons;+14.0%vs.02Trend+8.8%2022-12-22O:/Network/Path
39、/Filename.ppt-32-Luzhong RegionShipments322.201002003004005006001998199920002001200220032004Annualized Sales Metric Tons in Thousands%Change:-3.4%-9.2%Change vs.Year AgoOct-03:+10.1%;SDA+10.1%YTD Oct-03:+49.5%;SDA+49.5%2004 Forecast:322.2M Met.Tons;+48.4%vs.032003 PYE:217.1M Met.Tons;+49.8%vs.02Tren
40、d+42.9%2022-12-22O:/Network/Path/Filename.ppt-33-Huaihai RegionShipments210.90501001502002503001998199920002001200220032004Annualized Sales Metric Tons in Thousands%Change:+82.3%+14.6%Change vs.Year AgoOct-03:-28.6%;SDA-28.6%YTD Oct-03:-8.8%;SDA-8.8%2004 Forecast:210.9M Met.Tons;+2.4%vs.032003 PYE:2
41、06.0M Met.Tons;-8.4%vs.02Trend-4.1%2022-12-22O:/Network/Path/Filename.ppt-34-East RegionShipments227.70501001502002503003504001998199920002001200220032004Annualized Sales Metric Tons in Thousands%Change:+74.7%+2.9%Change vs.Year AgoOct-03:-18.1%;SDA-18.1%YTD Oct-03:-7.5%;SDA-7.5%2004 Forecast:227.7M
42、 Met.Tons;-9.2%vs.032003 PYE:250.7M Met.Tons;-7.5%vs.02Trend-9.2%2022-12-22O:/Network/Path/Filename.ppt-35-Southeast RegionShipments221.40501001502002503001998199920002001200220032004Annualized Sales Metric Tons in Thousands%Change:+56.6%Change vs.Year AgoOct-03:+7.9%;SDA+7.9%YTD Oct-03:+39.8%;SDA+3
43、9.8%2004 Forecast:221.4M Met.Tons;+20.2%vs.032003 PYE:184.1M Met.Tons;+41.3%vs.02Trend+35.2%2022-12-22O:/Network/Path/Filename.ppt-36-Northeast RegionShipments159.20204060801001201401601801998199920002001200220032004Annualized Sales Metric Tons in Thousands%Change:+140.7%+29.1%Change vs.Year AgoOct-03:+12.9%;SDA+12.9%YTD Oct-03:+17.1%;SDA+17.1%2004 Forecast:159.2M Met.Tons;+16.7%vs.032003 PYE:136.3M Met.Tons;+15.6%vs.02Trend+17.3%
