Planning Demand and Supply in a Supply Chain Forecasing and Aggregae Planning 1
Learning Objecives Overview of forecasing Forecas errors Aggregae planning in he supply chain Managing demand Managing capaciy 2
Phases of Supply Chain Decisions Sraegy or design: Planning: Operaion Forecas Forecas Acual demand Since acual demands differs from forecass so does he execuion from he plans. E.g. Supply Chain concenraion plans 40 sudens per year whereas he acual is??. 3
Characerisics of forecass Forecass are always wrong. Should include expeced value and measure of error. Long-erm forecass are less accurae han shor-erm forecass. Too long erm forecass are useless: Forecas horizon Aggregae forecass are more accurae han disaggregae forecass Variance of aggregae is smaller because exremes cancel ou» Two samples: (3,5) and (2,6). Averages of samples: 4 and 4.» Variance of sample averages=0» Variance of (3,5,2,6)=5/2 Several ways o aggregae Producs ino produc groups Demand by locaion Demand by ime period 4
Forecasing Mehods Qualiaive Exper opinion E.g. Why do you lisen o Wall Sree sock analyss? Time Series Saic Adapive Causal Forecas Simulaion for planning purposes 5
Componens of an observaion Observed demand (O) = Sysemaic componen (S) + Random componen (R) Level (curren deseasonalized demand) Trend (growh or decline in demand) Seasonaliy (predicable seasonal flucuaion) 6
Time Series Forecasing Quarer Demand D II, 1998 8000 III, 1998 13000 IV, 1998 23000 I, 1999 34000 II, 1999 10000 III, 1999 18000 IV, 1999 23000 I, 2000 38000 II, 2000 12000 III, 2000 13000 IV, 2000 32000 I, 2001 41000 Forecas demand for he nex four quarers. 7
99,1 99,2 99,3 99,4 00,1 Time Series Forecasing 50,000 40,000 30,000 20,000 10,000 0 8 97,2 97,3 97,4 98,1 98,2 98,3 98,4
Forecasing mehods Saic Adapive Moving average Simple exponenial smoohing Hol s model (wih rend) Winer s model (wih rend and seasonaliy) 9
Error measures MAD Mean Squared Error (MSE) Mean Absolue Percenage Error (MAPE) Bias Tracking Signal 10
Maser Producion Schedule Volume Firm Orders Forecass Frozen Zone Flexible Zone Time MPS is a schedule of fuure deliveries. A combinaion of forecass and firm orders. 11
Aggregae Planning If acual is differen han plan, why boher sweaing over deailed plans Aggregae planning: General plan Combined producs = aggregae produc» Shor and long sleeve shirs = shir Single produc Pooled capaciies = aggregaed capaciy» Dedicaed machine and general machine = machine Single capaciy Time periods = ime buckes» Consider all he demand and producion of a given monh ogeher Quie a few ime buckes When does he demand or producion ake place in a ime bucke? 12
Fundamenal radeoffs in Aggregae Planning Capaciy (regular ime, over ime, subconrac) Invenory Backlog / los sales: Cusomer paience? Basic Sraegies Chase (he demand) sraegy; fas food resaurans Time flexibiliy from workforce or capaciy; machining shops, army Level sraegy; swim wear 13
Maching he Demand Use invenory Demand Use delivery ime Demand Use capaciy Demand 14
Aggregae planning a Red Tomao Farm ools: Shovels Same characerisics? Spades Generic ool, Shovel Forks Aggregae by similar characerisics 15
Aggregae Planning a Red Tomao Tools Monh Demand Forecas January 1,600 February 3,000 March 3,200 April 3,800 May 2,200 June 2,200 16
Aggregae Planning Iem Maerials Invenory holding cos Marginal cos of a sockou Hiring and raining coss Layoff cos Labor hours required Regular ime cos Over ime cos Cos of subconracing Max overime hrs per employee Cos $10/uni $2/uni/monh $5/uni/monh $300/worker $500/worker 4hours/uni $4/hour $6/hour $30/uni 10hours/employee 17
1. Aggregae Planning (Decision Variables) W = Workforce size for monh, = 1,..., 6 H = Number of employees hired a he beginning of monh, = 1,..., 6 L = Number of employees laid off a he beginning of monh, = 1,..., 6 P = Producion in monh, = 1,..., 6 I = Invenory a he end of monh, = 1,..., 6 S = Number of unis socked ou a he end of monh, = 1,..., 6 C = Number of unis subconraced for monh, = 1,..., 6 O = Number of overime hours worked in monh, = 1,..., 6 18
2. Objecive Funcion: 6 6 6 6 6 6 6 6 Min 4 8 20 W + 300H + 500L + 6O + 2I + 5S + 10P + 30C = 1 = 1 = 1 = 1 = 1 = 1 = 1 = 1 3. Consrains Workforce size for each monh is based on hiring and layoffs W W = W W 1 1 + H H + L L, or = 0 for = 1,...,6, where W 0 = 80. Producion (uni) for each monh canno exceed capaciy (hour) P 40W 8 20(1/ 4) + O 4 W P + O 0, 4 or for = 1,...,6. 19
20 3. Consrains Invenory balance for each monh 500. 0 1,000, 1,...,6, 0,, 6 0 0 1 1 1 1 = = = = + + + + + = + + I S I S I S D C P I S I S D C P I and where for Over ime for each monh 1,...,6. 0 10 or 10 = for O W W O
Applicaion Solve he formulaion, see Table 8.3 Toal cos=$422.275k, oal profi=$640k Apply he firs monh of he plan Delay applying he remaining par of he plan unil he nex monh Rerun he model wih new daa nex monh This is called rolling horizon execuion 21
Aggregae Planning a Red Tomao Tools This soluion was for he following demand numbers: Monh Demand Forecas January 1,600 February 3,000 March 3,200 April 3,800 May 2,200 June 2,200 Toal 16,000 Wha if demand flucuaes more? 22
Increased Demand Flucuaion Monh Demand Forecas January 1,000 February 3,000 March 3,800 April 4,800 May 2,000 June 1,400 Toal 16,000 Toal coss=$432.858k. 23
Chaper 9: Maching Demand and Supply Supply = Demand Supply < Demand => Los revenue opporuniy Supply > Demand => Invenory Manage Supply Producions Managemen Manage Demand Markeing 24
Managing Predicable Variabiliy wih Supply Manage capaciy a b» Time flexibiliy from workforce (OT and oherwise)» Seasonal workforce» Subconracing» Couner cyclical producs: complemenary producs Negaively correlaed produc demands Snow blowers and Lawn Mowers» Flexible processes: Dedicaed vs. flexible d c Similar capabiliies b a a,b, c,d d One super faciliy c 25
Managing Predicable Variabiliy wih Invenory» Componen commonaliy Remember fas food resauran menus» Build seasonal invenory of predicable producs in preseason Nohing can be learn by procrasinaing» Keep invenory of predicable producs in he downsream supply chain 26
Managing Predicable Variabiliy wih Pricing Manage demand wih pricing Original pricing:» Cos = $422,275, Revenue = $640,000, Profi=$217,725 Demand increases from discouning Marke growh Sealing marke share from compeior Forward buying: sealing marke share from he fuure Discoun of $1 increases period demand by 10% and moves 20% of nex wo monhs demand forward 27
Off-Peak (January) Discoun from $40 o $39 Monh Demand Forecas January 3,000=1600(1.1)+0.2(3000+3200) February 2,400=3000(0.8) March 2,560=3200(0.8) April 3,800 May 2,200 June 2,200 Cos = $421,915, Revenue = $643,400, Profi = $221,485 28
Peak (April) Discoun from $40 o $39 Monh Demand Forecas January 1,600 February 3,000 March 3,200 April 5,060=3800(1.1)+0.2(2200+2200) May 1,760=2200(0.8) June 1,760=2200(0.8) Cos = $438,857, Revenue = $650,140, Profi = $211,283 Discouning during peak increases he revenue 29 bu decreases he profi!
Demand Managemen Pricing and Aggregae Planning mus be done joinly Facors affecing discoun iming Consumpion: Changing fracion of increase coming from forward buy (100% increase in consumpion insead of 10% increase) Forward buy, sill 20% of he nex wo monhs Produc Margin: Impac of higher margin. Wha if discoun from $31 o $30 insead of from $40 o $39.) 30
January Discoun: 100% increase in consumpion, sale price = $40 ($39) Monh Demand Forecas January 4,440=1600(2)+0.2(3000+3200) February 2,400=0.8(3000) March 2,560=0.8(3200) April 3,800 May 2,200 June 2,200 Off peak discoun: Cos = $456,750, Revenue = $699,560 Profi=$242,810 31
Peak (April) Discoun: 100% increase in consumpion, sale price = $40 ($39) Monh Demand Forecas January 1,600 February 3,000 March 3,200 April 8,480=3800(2)+(0.2)(2200+2200) May 1,760=(0.8)2200 June 1,760=(0.8)2200 Peak discoun: Cos = $536,200, Revenue = $783,520 Profi=$247,320 32
Performance Under Differen Scenarios Regular Price Promoion Price Promoion Period Percen increase in demand Percen forward buy Profi Average Invenory $40 $40 NA NA NA $217,725 895 $40 $39 January 20 % 20 % $221,485 523 $40 $39 April 20% 20% $211,283 938 $40 $39 January 100% 20% $242,810 208 $40 $39 April 100% 20% $247,320 1,492 $31 $31 NA NA NA $73,725 895 $31 $30 January 100% 20% $84,410 208 $31 $30 April 100% 20% $69,120 1,492 Use rows in bold o explain Xmas discouns. 33
Facors Affecing Promoion Timing Facor High forward buying High sealing share High growh of marke High margin Low margin High holding cos Low capaciy volume flexibiliy Favored iming Low demand period High demand period High demand period High demand period Low demand period Low demand period Low demand period 34
Capaciy Demand Maching Invenory/Capaciy radeoff Leveling capaciy forces invenory o build up in anicipaion of seasonal variaion in demand Level sraegy Carrying low levels of invenory requires capaciy o vary wih seasonal variaion in demand or enough capaciy o cover peak demand during season Chase sraegy 35
Deerminisic Capaciy Expansion Issues Single vs. Muliple Faciliies Dallas and Alana plans of Lockheed Marin Single vs. Muliple Resources Machines and workforce Single vs. Muliple Produc Demands Expansion only or wih Conracion Discree vs. Coninuous Expansion Times Discree vs. Coninuous Capaciy Incremens Can you buy capaciy in unis of 723.13832? Resource coss, economies of scale Penaly for demand-capaciy mismach Single vs. Muliple decision makers 36
A Simple Model Unis x x Capaciy Demand D()=µ+δ x/δ Time () No sock ous. x is capaciy incremens. 37
Infinie Horizon Toal Cos C( x) x = exp r( k ) f ( x) = f ( x) k= δ k= 0 (exp( rx / δ )) f ( x) = 1 exp( rx / 0 δ k ) f(x) is expansion cos of size x C(x) is he infinie horizon oal discouned expansion cos f ( x) 0.5 = x ; r = 5%; δ = 1; x * 30 Each ime expand capaciy by 30-week demand. 38
Shorages, Invenory Holding, Subconracing Unis Subconracing Capaciy Demand Surplus capaciy Invenory build up Invenory depleion Use of Invenory and subconracing o delay capaciy expansions 39 Time
Sochasic Capaciy Planning: The case of flexible capaciy 1 A y 1A =1, y 2A =1, y 3A =0 2 3 Plans B Producs y 1B =0, y 2B =0, y 3B =1 Plan 1 and 2 can produce produc A Plan 3 can produce produc B A and B are subsiue producs wih random demands D A + D B = Consan 40
Capaciy allocaion Say capaciies are r 1 =r 2 = r 3 =100 Suppose ha D A + D B = 300 and D A >100 and D B >100 Wih plan flexibiliy y 1A =1, y 2A =1, y 3A =0, y 1B =0, y 2B =0, y 3B =1. Scenario D A D B X 1A X 2A X 3A X 1B X 2B X 3B Shorage 1 200 100 100 100 100 0 2 150 150 100 50 100 50 B 3 100 200 100 0 100 100 B 41
Capaciy allocaion wih more flexibiliy Say capaciies are r 1 =r 2 = r 3 =100 Suppose ha D A + D B = 300 and D A >100 and D B >100 Wih plan flexibiliy y 1A =1, y 2A =1, y 3A =0, y 1B =0, y 2B =1, y 3B =1. Scenario D A D B X 1A X 2A X 3A X 1B X 2B X 3B Shorage 1 200 100 100 100 0 100 0 2 150 150 100 50 50 100 0 3 100 200 100 0 100 100 0 42
Maerial Requiremens Planning Maser Producion Schedule (MPS) Bill of Maerials (BOM) MRP explosion Advanages Disciplined daabase Componen commonaliy Shorcomings Rigid lead imes No capaciy consideraion 43
Opimized Producion Technology Focus on boleneck resources o simplify planning Produc mix defines he boleneck(s)? Provide pleny of non-boleneck resources. Shifing bolenecks 44
Jus in Time producion Focus on iming Advocaes pull sysem, use Kanban Design improvemens encouraged Lower invenories / se up ime / cycle ime Qualiy improvemens Supplier relaions, fewer closer suppliers, Toyoa ciy JIT philosophically differen han OPT or MRP, i is no only a planning ool bu a coninuous improvemen scheme 45
Summary of Learning Objecives Forecasing Aggregae planning Supply and demand managemen during aggregae planning wih predicable demand variaion Supply managemen levers Demand managemen levers MRP, OPT, JIT Deerminisic Capaciy Planning 46