Case 8Case 8Yankee Fork and Hoe CompanyYankee Fork and Hoe Company

By:By:

David SiglerDavid Sigler

Cole CarlsonCole Carlson

John FederiukJohn Federiuk

Melissa KirkmanMelissa Kirkman

HistoryHistory

Alan Roberts, long time President of Yankee…Alan Roberts, long time President of Yankee…

Leading producer of garden toolsLeading producer of garden tools Wheelbarrows, mortar pans, hand trucks, shovels, rakes, Wheelbarrows, mortar pans, hand trucks, shovels, rakes,

trowelstrowels

Use four different product linesUse four different product lines Product Range:Product Range:

Top-of-the-line Hercules tools for the toughest jobsTop-of-the-line Hercules tools for the toughest jobs Garden Helper economy tools for occasional userGarden Helper economy tools for occasional user

Continued…Continued…

Market is increasingly competitive while keeping Market is increasingly competitive while keeping low prices and retaining high quality and low prices and retaining high quality and dependable delivery.dependable delivery.

Early rapid growth has now leveled offEarly rapid growth has now leveled off

Generate new sales and retain old customers by Generate new sales and retain old customers by providing superior customer service and providing superior customer service and producing products with high customer valueproducing products with high customer value

The ProblemsThe Problems

Sales forecasting for Pareto items such as their Sales forecasting for Pareto items such as their Bow Rake have been unreliableBow Rake have been unreliable

Receiving complaints from long-time customers Receiving complaints from long-time customers (Sears and Tru-Value) about late deliveries (Sears and Tru-Value) about late deliveries causing their customers to be unhappycausing their customers to be unhappy

Current Forecasting ProblemsCurrent Forecasting Problems

Ron Adams, Marketing ManagerRon Adams, Marketing Manager

Ron uses shipment data because “they are hard Ron uses shipment data because “they are hard numbers” to forecast sales.numbers” to forecast sales.

Meets with sales managers to modify shipping Meets with sales managers to modify shipping numbers based from:numbers based from: Anticipated PromotionsAnticipated Promotions Changes in the economyChanges in the economy Previous years shortagesPrevious years shortages

Current Forecasting ProblemsCurrent Forecasting Problems

Phil Stanton, Production ManagerPhil Stanton, Production Manager

Phil takes marketing depts. forecast and reduces Phil takes marketing depts. forecast and reduces it by 10% or so “Because they have big egos over it by 10% or so “Because they have big egos over there”there”

This info is used to prepare the monthly final This info is used to prepare the monthly final assembly schedule (MPS)assembly schedule (MPS)

““Forecast is always worst at the end of the year”Forecast is always worst at the end of the year”

Current Forecasting VariationsCurrent Forecasting Variations

MonthMonth

ShipmenShipmentt

VariationVariationYear 1Year 1

DemandDemandYear 1Year 1

ShipmenShipmentt

VariatioVariationn

Year 2Year 2DemandDemandYear 2Year 2

ShipmenShipmentt

VariatioVariationn

Year 3Year 3DemandDemandYear 3Year 3

ShipmenShipmentt

VariatioVariationn

Year 4Year 4DemandDemandYear 4Year 4

11 -15,171-15,171 53,63053,630 -8,474-8,474 51,07851,078 -17,910-17,910 53,97753,977 -10,637-10,637 50,04050,040

22 -10,511-10,511 56,28956,289 -20,338-20,338 59,29859,298 -26,409-26,409 60,99860,998 -25,473-25,473 63,78163,781

33 19,03019,030 17,34517,345 15,33715,337 20,22320,223 19,15219,152 22,56822,568 20,43220,432 23,26623,266

44 12,78812,788 26,19926,199 12,01012,010 25,97025,970 12,99612,996 26,50426,504 12,16912,169 28,14028,140

55 -5,619-5,619 23,09923,099 -1,067-1,067 24,70524,705 2,3372,337 26,93226,932 -319-319 27,56627,566

66 604604 15,70015,700 -5,479-5,479 13,40013,400 3,2593,259 16,42116,421 -4,327-4,327 15,89815,898

77 700700 16,56016,560 4,5874,587 17,78817,788 -2,291-2,291 13,04513,045 4,2934,293 18,20918,209

88 -7,551-7,551 18,20018,200 -3,068-3,068 16,46516,465 2,9182,918 18,99118,991 2,0222,022 17,69017,690

99 7,3807,380 15,51015,510 2,9672,967 17,43317,433 -1,113-1,113 21,60421,604 -8,085-8,085 22,88722,887

1010 -19,371-19,371 55,08855,088 -420-420 57,40057,400 -56,257-56,257 59,29759,297 -16,479-16,479 54,77754,777

1111 21,60521,605 84,18884,188 223223 85,45585,455 -1,217-1,217 81,52181,521 26,54726,547 83,70983,709

1212 4,1214,121 71,08871,088 358358 73,88673,886 3,7333,733 74,69974,699 2,2232,223 75,43275,432

Forecasting ErrorForecasting ErrorBias error due to use of wrong modelBias error due to use of wrong model

RSFE = ARSFE = Att – F – Ftt = Error = Error

Month 1 Actual (At) 1 Forecast (Ft) RSFE 2 Actual (At) 2 Forecast (Ft) RSFE

1 38,459 53,630 -15,17115,171 42,604 51,078 -8,474-8,474

2 45,778 56,289 -10,51110,511 38,960 59,298 -20,33820,338

3 36,375 17,345 19,030 35,560 20,223 15,337

4 38,987 26,199 12,788 37,980 25,970 12,010

5 17,480 23,099 -5,6195,619 23,638 24,705 -1,067-1,067

6 16,304 15,700 604 7,921 13,400 -5,479-5,479

7 17,260 16,560 700 22,475 17,788 4,587

8 10,649 18,200 -7,551-7,551 13,397 16,465 -3,068-3,068

9 22,890 15,510 7,380 20,400 17,433 2,967

10 35,717 55,088 -19,371-19,371 56,980 57,400 -420420

11 105,793 84,188 21,605 85,678 85,455 223

12 75,209 71,088 4,121 74,244 73,886 358

8,005 -3,364-3,364

Tracking SignalsTracking Signals

Only in range 5 times in 2 yearsOnly in range 5 times in 2 years

Month RSFE Tracking Signal RSFE Tracking Signal

1 -15,171-15,171 -22.7 -8,474-8,474 -30.2

2 -10,511-10,511 -15.8 -20,338-20,338 -72.6

3 19,030 28.5 15,337 54.7

4 12,788 19.2 12,010 42.8

5 -5,619-5,619 -8.4 -1,067-1,067 -3.8

6 604 0.9 -5,479-5,479 -19.5

7 700 1.0 4,587 16.4

8 -7,551-7,551 -11.3 -3,068-3,068 -10.9

9 7,380 11.1 2,967 10.6

10 -19,371-19,371 -29.0 -420-420 -1.5

11 21,605 32.4 223 0.8

12 4,121 6.2 358 1.3

         

Total Error 8,005   -3,364  

MAD 667   280  

Our New Forecast MethodOur New Forecast Method

Use demand and capacity to produce data not Use demand and capacity to produce data not shipment datashipment data

Do not reduce by 10% or soDo not reduce by 10% or so

Incorporate seasonality on a month by month Incorporate seasonality on a month by month basisbasis

Used simple moving average of last two years to Used simple moving average of last two years to get demand for year 5get demand for year 5

Our Modified ForecastOur Modified Forecast

Using demand data versus supply dataUsing demand data versus supply data

MonthMonth Year 1Year 1

SeasonaSeasonall

FactorFactor Year 2Year 2

SeasonSeasonalal

FactorFactor Year 3Year 3

SeasonSeasonalal

FactorFactor Year 4Year 4

SeasonaSeasonall

FactorFactor

AverageAverageSeasonSeason

alalFactor Factor

Year 5Year 5ForecasForecas

tt

11 53,63053,630 1.4211.421 51,07851,078 1.3241.324 53,97753,977 1.3591.359 50,04050,040 1.2471.247 1.3381.338 54,72154,721

22 56,28956,289 1.4911.491 59,29859,298 1.5371.537 60,99860,998 1.5361.536 63,78163,781 1.5901.590 1.5381.538 62,93062,930

33 17,34517,345 0.4600.460 20,22320,223 0.5240.524 22,56822,568 0.5680.568 23,26623,266 0.5800.580 0.5330.533 21,80021,800

44 26,19926,199 0.6940.694 25,97025,970 0.6730.673 26,50426,504 0.6670.667 28,14028,140 0.7010.701 0.6840.684 27,97827,978

55 23,09923,099 0.6120.612 24,70524,705 0.6400.640 26,93226,932 0.6780.678 27,56627,566 0.6870.687 0.6540.654 26,76726,767

66 15,70015,700 0.4160.416 13,40013,400 0.3470.347 16,42116,421 0.4130.413 15,89815,898 0.3960.396 0.3930.393 16,08616,086

77 16,56016,560 0.4390.439 17,78817,788 0.4610.461 13,04513,045 0.3280.328 18,20918,209 0.4540.454 0.4210.421 17,20117,201

88 18,20018,200 0.4820.482 16,46516,465 0.4270.427 18,99118,991 0.4780.478 17,69017,690 0.4410.441 0.4570.457 18,69418,694

99 15,51015,510 0.4110.411 17,43317,433 0.4520.452 21,60421,604 0.5440.544 22,88722,887 0.5710.571 0.4940.494 20,21920,219

1010 55,08855,088 1.4601.460 57,40057,400 1.4871.487 59,29759,297 1.4931.493 54,77754,777 1.3651.365 1.4511.451 59,36959,369

1111 84,18884,188 2.2312.231 85,45585,455 2.2142.214 81,52181,521 2.0532.053 83,70983,709 2.0872.087 2.1462.146 87,78587,785

1212 71,08871,088 1.8841.884 73,88673,886 1.9151.915 74,69974,699 1.8811.881 75,43275,432 1.8801.880 1.8901.890 77,30477,304

AVGAVG 37,74137,741 38,59238,592 39,71339,713 40,11640,116 40,90540,905

TotalTotalDemandDemand

452,89452,8966 N/AN/A

463,10463,1011 2.25%2.25%

476,55476,5577 2.91%2.91%

481,39481,3955 1.02%1.02%

2 yr SMA2 yr SMA1.965%1.965% 490,854490,854

Missing InformationMissing Information What promotions are being runWhat promotions are being run

how often / what products / wherehow often / what products / where

Delivery serviceDelivery service how often / where / method / what is consumer how often / where / method / what is consumer

complaintcomplaint

Inventory AccuracyInventory Accuracy ““Cycle Counting” – how oftenCycle Counting” – how often

What other products are machines used to What other products are machines used to produceproduce What effect does this have on bow rake productionWhat effect does this have on bow rake production

Is this a job shop / batch shop / assembly line Is this a job shop / batch shop / assembly line

What is our budgetWhat is our budget

How we plan to get the infoHow we plan to get the info

Plant VisitPlant Visit Production layoutProduction layout

Interview Ron AdamsInterview Ron Adams Promotion infoPromotion info Non-adjusted sales projectionsNon-adjusted sales projections Regional sales breakdownRegional sales breakdown

Interview Phil StantonInterview Phil Stanton Delivery service useDelivery service use Machine usageMachine usage Inventory accuracyInventory accuracy

Our RecommendationsOur Recommendations Change forecasting methodChange forecasting method

Demand basedDemand based SeasonalitySeasonality

Pareto items need to be batch shoppedPareto items need to be batch shopped Class A SKU’sClass A SKU’s

Coordinate marketing and production more Coordinate marketing and production more efficientlyefficiently

Have a finished goods warehouseHave a finished goods warehouse Two-bin systemTwo-bin system Ensure order fulfillment in a timely manner Ensure order fulfillment in a timely manner

Continuing ActionsContinuing Actions

Forward Integration – Acquire own delivery serviceForward Integration – Acquire own delivery service Ensure on-time deliveryEnsure on-time delivery

Ensure Six-Sigma QualityEnsure Six-Sigma Quality Define – identify customers and their prioritiesDefine – identify customers and their priorities Measure – the process and how it is performed Measure – the process and how it is performed Analyze – the most likely causes of defectsAnalyze – the most likely causes of defects Improve – the means to remove defect causesImprove – the means to remove defect causes Control – how to maintain the improvementsControl – how to maintain the improvements

Set up Poka-Yokes to ensure product qualitySet up Poka-Yokes to ensure product quality

Continuing ActionsContinuing Actions

Continuing ActionsContinuing Actions

Assembly line balancingAssembly line balancing Reduce slack time to improve efficiencyReduce slack time to improve efficiency

BenchmarkingBenchmarking Competitive – core functions with direct industry Competitive – core functions with direct industry

competitorcompetitor Functional – non-core operational functionsFunctional – non-core operational functions Internal – comparison made within the organizationInternal – comparison made within the organization

Questions?Questions?