LECTURE 8: MANAGING DEMAND

Transcription

1 LECTURE 8: MANAGING DEMAND AND SUPPLY IN A SUPPLY CHAIN INSE 6300: Quality Assurance in Supply Chain Management 1

2 RESPONDING TO PREDICTABLE VARIABILITY 1. Managing Supply Process of managing production capacity and inventory in order to efficiently meet customer demands. Consideration of whom to procure from, subcontracting and backlogs while planning. 2. Managing Demand Process of managing customer demands through forecasting. Consideration of short-term t price discounts and trade promotions in managing inventories for meeting demands. 3. Aggregate Planning Process of determining ideal levels of capacity, production, subcontracting, inventory, stockouts, and even pricing ii over a specified time horizon. 2

3 1. MANAGING SUPPLY Management of supply is vital to improving synchronization in the supply chain in face of predictable variability. It can be achieved through the management of: Production Capacity Inventory 3

4 MANAGING CAPACITY Time flexibility from workforce Use of seasonal workforce Use of subcontracting Use of dual facilities specializeded and flexible Designing product flexibility into the production processes 4

5 MANAGING INVENTORY Using common components across multiple products Build inventory of high-demand or predictable demand products Proactive management of independent demand inventory 5

6 INVENTORY TYPES ABC classification (based on Pareto s principle) A Category Items Comprise 20% of SKU (stock keeping unit) & Contribute to 80% of $ spend. B Category Items Comprise 30% of SKU & Contribute to 15% of $ spend. C Category Items Comprise 50% of SKU & Contribute to 5% of $ spend. Dependent vs Independent (e.g. car vs its components) Raw material, WIP, Finished goods 6

7 INVENTORY TYPES Inventory Type Definition Cycle stock Components or products that t are received in bulk by a downstream partner, gradually used up, and then replenished again in bulk by the upstream partner Safety stock Anticipation inventory Hedge inventory Transportation inventory Smoothing inventories Extra inventory that companies hold to protect themselves against uncertainties in either demand or replenishment time Inventory that is held in anticipation of customer demand A form of inventory build up to buffer against some event that may not happen Inventory that is moving from one link in the supply chain to the another Inventories used to smooth out differences between upstream production levels and downstream demand. 7

8 PERIODIC VS CONTINUOUS REVIEW SYSTEMS Periodic review systems An inventory system used to manage independent demand inventory. The inventory level for an item is checked at regular intervals and restocked to some predetermined level. Continuous review systems An inventory system used to manage independent demand inventory. The inventory level for an item is constantly monitored, and when the reorder point is reached, an order is released. 8

9 PERIODIC REVIEW SYSTEMS The actual order quantity, Q, is the amount required to bi bring the inventory levell back up to R. where Q = R- I Q =orderquantity R = restocking level I = inventory level at the time of review 9

10 RESTOCKING LEVELS 10

11 CONTINUOUS REVIEW SYSTEMS In a continuous review system, the inventory level for an item is constantly monitored, and when the reorder point is reached, anorder is released. A continuous review system with constant demand rate d 11

12 THE ECONOMIC ORDER QUANTITY The economic order quantity represents the particular order quantity (Q) that t minimizes i i holding costs and ordering costs for an item. 12

13 REORDER POINTS AND SAFETY STOCK The eoq tells how much to order, but not when to order. The decision of how much safety stock to hold depends on variability of demand, variability of lead time, average length of lead time and desired service level. Impact of varying demand rates and lead times 13

14 QUANTITYQ DISCOUNTS In eoq, the assumption was that the price per unit was fixed. When quantity discounts are in effect, the analysis should be done as follows: Compute eoq and compare total holding, ordering and item costs at the eoq quantity with total costs at each price break above the eoq. Find the lowest price option and stop. 14

15 SINGLE PERIOD INVENTORY SYSTEMS A system used when demand occurs in only a single point of time. Excess inventory has to be discarded or sold at a loss. Example, fresh foods, newspapers etc. Target service level and stocking point are set at which the expected cost of a shortage equals the expected cost of having excess units. ( A service level indicates the amount of demand to be met under conditions of demand and supply uncertainty ) 15

16 MANAGING INVENTORY IN THE SUPPLY CHAIN Inventory positioning The cost and value of inventory increase as materials move down the supply chain The flexibility of inventory decreases as materials move down the supply chain Transportation, packaging and material handling considerations 16

17 JUST-IN-TIME (JIT) JIT is a philosophy of manufacturing based on planned elimination of all waste and on continuous improvement of productivity. The primary elements of JIT are to have the required inventory when needed; to improve quality to zero defects; to reduce lead times by reducing setup times, queue lengths and lot sizes; to incrementally revise the operations themselves; and to accomplish these activities at minimum cost. It applies to all forms of manufacturing job shop, process, and repetitive and to many service industries as well. 17

18 JIT PERSPECTIVE ON WASTE Overproduction Waiting Unnecessary transportation Inappropriate process Unnecessary inventory Unnecessary/excess motion Defects Underutilization of employees 18

19 JIT PERSPECTIVE ON INVENTORY 19

20 JIT - CONTROLLING INVENTORY LEVELS USING KANBANS Uses simple visual signals to control production How many kanban cards here? Why? 20

21 2. MANAGING DEMAND -FORECASTING A forecast is an estimate of the future level of some variable. Forecast types Demand forecasts Supply forecasts Price forecasts 21

22 LAWS OF FORECASTING Law 1: Forecasts are almost always wrong Law 2: Forecasts for the near term tend to be more accurate Law 3: Forecasts for groups of products of services tend to be more accurate Law 4: Forecasts are no substitute for calculated values 22

23 COMPONENTS OF A FORECAST Past Demand Lead time of product Planned advertising or marketing efforts State t of the economy Planned price discounts Actions that competitors have taken 23

24 FORECASTING METHOD Observed Demand (O) = systematic component (S) + random component (R ) The objective of forecasting is to filter out the random component (noise) and estimate the systematic component. The forecast error measures the difference between the forecast and the actual demand. d 24

25 RANDOM COMPONENTS IN DEMAND Dema nd for pr roduct or service Seasonal peaks Random variation Average demand over four years Trend component Actual demand line Year 1 Year 2 Year 3 Year 4 25

26 FORECASTING METHODS Qualitative Methods Primary subjective and rely on human judgment Time Series Methods Use historical demand to make forecasts Causal Methods Use estimates of correlated environmental factors of demand Simulation Methods Imitate the consumer choices that give rise to demand to arrive at a forecast arrive at a forecast 26

27 TIME-SERIES FORECASTING METHODS Static A static method assumes that the estimates of level, trend and seasonality within the systematic component do not vary as the new demand is observed. Adaptive Moving average Simple exponential smoothing Holt s model (with trend) ) Winter s model (with trend and seasonality) 27

28 MOVING AVERAGE MODELS Forecast Demands for previous n periods Period Demand F t + 1 = D t + D t D t n + n Periods 1 3-period moving average forecast for Period 9: Here, t+1 = 9, n = 3 F9=(D 8 +D7+D6)/3 =( ) / 3 =

29 WEIGHTED MOVING AVERAGES Forecast Demand F t+ 1 Wt Dt = + W t 1 Dt 1 + W ( W + W t t t 1 2 Dt Wt W ) t n+ 1 n+ 1 D t n+ 1 Weights Forecast for Period 9 = [(W8*D8) + (W7 *D7) + (W6 *D6)] / (W8 + W7 + W6) = [(0.5 12) + (0.3 14) + (0.2 8)] / ( ) = 11.8 Note : All weights should add up to 1. Recent demands are given higher weights than their precedents 29

30 EXAMPLE: MOVING AVERAGES Period Actual Demand Two-Period Moving Average Forecast Three-Period Weighted Moving Average Forecast Weights = 0.5, 0.3,

31 FORECAST GRAPH: MOVING AVERAGES Actual Demand 8 Two-Period Moving Average Forecast 6 Three-Period Weighted Moving Average

32 EXPONENTIAL SMOOTHING Formula F t+1 = F t + α (D t F t ) = α D t + (1 α) F t where F t = Forecast for the current period t D t = Actual demand for the current period t α = Weight between 0 and 1 Note : The forecast for period t+1 are obtained by taking the previous period actual demand (Dt) and forecast (Ft). An initial value of F0 is assumed. 32

33 EXPONENTIAL SMOOTHING FORECAST ΑLPHA = Period Actual Demand Exponential Smoothing Forecast F1 = F 2 = = = F 3 = = F t+1 = α D t + (1 α) F t 33

34 EXPONENTIAL SMOOTHING (WITH TRENDS) Add trend factor and adjust the forecast using exponential smoothing T t+1 = β (F t+1 F t ) + (1 β) T t T t = Trend factor for the current period t β = Weight between 0 and 1 F t+1 adjusted for trend is = F t+1 + T t+1 The trended forecast is obtained by This forecast Ft+1 is obtained using adding Tt+1 to forecast Ft+1 obtained the exponential smoothing formula 34 of from exponential smoothing. slide 32.

35 SIMPLE LINEAR REGRESSION Time series OR causal model Assumes a linear relationship: y = a + b(x) y x35x

36 SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION Predicted variable (demand) Predictor variable y = a + b(x) y x y x n n i n i i i = = 1 1 )( ( x x n y x b n n i i i i i i i = = = = = ) ( n x i i i = = 1 1 bx y a = 36

37 EXAMPLE: REGRESSION FORECASTS Period (X) Demand (Y) X 2 XY b = = a = = Column Sums 37

38 RESULTS: REGRESSION FORECAST Forecast (Y) = Period(X) Y Demand Regression X 38

39 COEFFICIENT OF CORRELATION AND REGRESSION MODEL Y r = 1 Y r = -1 Y i = a + b X i Y i = a + b X i X Y r =.89 Y r = 0 X Y i = a + b X i X Y i = a + b X ix r 2 = square of correlation coefficient (r), is the percent of the variation in y that is explained by the regression equation 4-39

40 DEALING WITH SEASONALITY Quarter Period Demand Winter Spring Summer Fall Winter Spring Summer Fall

41 DEALING WITH SEASONALITY Forecasted Demand = x Period Actual Regression Forecast Period Demand Forecast Error Winter Spring Summer Fall Winter Spring Summer Fall

42 REGRESSION FORECAST GRAPH Demand 400 Forecast Regression picks up trend, but not seasonality effect! 42

43 CALCULATING SEASONAL INDEX: WINTER QUARTER (Actual / Forecast) for Winter Quarters Winter 02: (80 / 90) = 0.89 Winter 03: (400 / 524.3) = 0.76 Average of these two =

44 SEASONALLY ADJUSTED FORECAST MODEL For Winter Quarter [ Period ] Or more generally: [ Period ] Seasonal Index 44

45 SEASONALLY ADJUSTED FORECASTS Forecasted Demand = x Period Demand Seasonally Perio Actual Regression /Foreca Seasonal Adjusted Forecast d Demand Forecast st Index Forecast Error Winter Spring Summer Fall Winter Spring Summer Fall

46 FORECAST MODEL WITH SEASONAL ADJUSTMENT Demand 400 forecast

47 NON LINEAR REGRESSION MODELS Non-linearity component Example: y = a + b ln(x) 47

48 MULTIPLE REGRESSION MODELS Multiple regression models contain more than one independent variable y y = a + b1 * x + b2 * z Dependent Variable Independent Variables z 48 x

49 GUIDELINES FOR SELECTING FORECASTING MODEL No pattern or direction in forecast error (Et) = Actual demand (Dt) Forecast (Ft) Smallest Forecast Error Mean square error (MSE) Mean absolute deviation (MAD) 49

50 FORECAST ERROR EQUATIONS Mean Square Error (MSE) 2 ( Et ) MSE = n Mean Absolute Deviation (MAD) MAD = n E t Mean Absolute Percent Error (MAPE) MAPE = 100 n i= 1 n Et D t 50 Error (Et) = Actual demand (Dt) Forecast (Ft)

51 FORECASTING IMPLICATIONS IN SUPPLY CHAIN 51

52 FORECASTING IMPLICATIONS IN SUPPLY CHAIN Collaborative planning, forecasting, and replenishment (CPFR) is a set of business processes, backed up by information technology, in which members agree to mutual business objectives and measures, develop joint sales and operational plans, and collaborate electronically to generate and update sales forecasts and replenishment plans. Voluntary Interindustry Commerce Standards Association (VICS) CPFR Model 52

53 3. AGGREGATE PLANNING Process of determining ideal levels of capacity, production, subcontracting, ti inventory, stockouts, and even pricing over a specified time horizon. Sales and operation planning Cash flow analysis Major approaches Top-down planning Bottom-up planning 53

54 TOP-DOWN PLANNING A single, aggregated sales forecast drives the planning process. The mix of products or services must be essentially the same from one period to the next, or should have similar resource requirements Steps Develop the aggregate sales forecast and planning values Translate the sales forecast into resource requirements Generate alternative production plans 54

55 BOTTOM UP PLANNING Bottom up planning is used when the products or services have different resource requirements and the mix is unstable from one period to the next. The resource requirements for each product or service are evaluated individually and then added up across all products or services to get a picture of overall requirements. 55

56 LEVEL, CHASE AND MIXED PRODUCTION PLANS Under a levell production plan, production is hld held constant, and inventory is used to absorb differences between production and sales forecast. In a chase production plan, production is changed each time period to match the sales forecast in each time period. A mixed production plan falls between these two extremes. It will vary both production and inventory levels in an effort to develop the most effective plan. 56

57 CASH FLOW ANALYSIS Net cash flow is defined as the net flow of dollars into or out of a business over some time period. Net cash flow =cashinflows cash outflows 57

58 ROLLING PLANNING HORIZONS Sales and operation plans must be updated on a rolling planning horizon, requiring them to update the sales and operation plan on a monthly or quarterly basis. 58

59 OPTIMIZATION MODELING S& OP Optimization objective (Maximize profits or Minimize costs) subject to constraints (workforce, hiring and layoff, capacity constraints, inventory balance constraints, overtime limits) Costs (regular time labor, overtime, cost of hiring and layoffs, cost of inventory and stockout, cost of materials and subcontracting) For example, 59

60 REFERENCES Cecil C Bozarth and Robert B Handfield, Introduction to Operations and Supply Chain Management, Chap 9, 12,13,15, Pearson Prentice Hall, 2006 Jay Heizer and Barry Render, Operations Management, 7e, Prentice Hall, Inc., 2004 Sunil Chopra and Peter Meindl, Supply Chain Management: strategy, planning and operations, 3e, Prentice Hall,