Supply Chain Management SYST 4050 Slides Supply Chain Management Lecture 10 Chapter 1

Outline Today Tomorrow Next week SYST 4050 Slides Outline Today Finish Chapter 6 (Decision tree analysis) Start chapter 7 Tomorrow Homework 2 due before 5:00pm Next week Chapter 7 (Forecasting) Chapter 1

Example: Decision Tree Analysis SYST 4050 Slides Example: Decision Tree Analysis New product with uncertain demand ($85 profit/unit) Annual demand expected to go up by 20% with probability 0.6 Annual demand expected to go down by 20% with probability 0.4 Use discount factor k = 0.1 Chapter 1

SYST 4050 Slides Example Represent the tree, identifying all states as well as all transition probabilities Period 2 P = 120*85+(0.6*12240+0.4*8160)/1.1 = 19844 P = 12240 Period 1 D=144 0.6 Period 0 D=120 0.6 0.4 P = 8160 D=100 D=96 0.6 P = 100*85+ (0.6*19844+0.4*13229)/1.1 = 24135 0.4 D=80 0.4 P = 5440 D=64 P = 80*85+(0.6*8160+0.4*5440)/1.1 = 13229 Chapter 1

Calculate the NPV of each possible scenario separately SYST 4050 Slides Example Represent the tree, identifying all states as well as all transition probabilities Period 2 Period 1 D=144 0.6 Period 0 D=120 0.6 0.4 D=100 D=96 0.6 0.4 D=80 0.4 D=64 Calculate the NPV of each possible scenario separately Chapter 1

Calculate the NPV of each possible scenario separately SYST 4050 Slides Example Represent the tree, identifying all states as well as all transition probabilities D=144 D=96 D=64 D=120 D=80 D=100 0.6 0.4 Period 0 Period 2 Period 1 Calculate the NPV of each possible scenario separately Chapter 1

Decision Trees (Summary) SYST 4050 Slides Decision Trees (Summary) A decision tree is a graphic device used to evaluate decisions under uncertainty Identify the duration of each period and the number of time periods T to be evaluated Identify the factors associated with the uncertainty Identify the representation of uncertainty Identify the periodic discount rate k Represent the tree, identifying all states and transition probabilities Starting at period T, work back to period 0 identify the expected cash flows at each step (Alternatively, calculate the NPV of each possible scenario separately) Decisions: Sign a long-term warehousing contract or get space in the spot market? How much capacity should the facility have? What fraction of this capacity should be flexible? Multi-period decisions when uncertainty resolves after each period Sources of uncertainty: Prices Demand Inflation rate Exchange rates Chapter 1

SYST 4050 Slides Decision Trees Using decision trees to evaluate network design decisions Should the firm sign a long-term contract for warehousing space or get space from the spot market as needed What should the firm’s mix of long-term and spot market be in the portfolio of transportation capacity How much capacity should various facilities have? What fraction of this capacity should be flexible? Chapter 1

Example: Decision Tree Analysis SYST 4050 Slides Example: Decision Tree Analysis Three options for Trips Logistics Get all warehousing space from the spot market as needed Sign a three-year lease for a fixed amount of warehouse space and get additional requirements from the spot market Sign a flexible lease with a minimum change that allows variable usage of warehouse space up to a limit with additional requirement from the spot market Chapter 1

Example: Decision Tree Analysis SYST 4050 Slides Example: Decision Tree Analysis Trips Logistics input data Evaluate each option over a 3 year time horizon (1 period is 1 year) Demand D may go up or down each year by 20% with probability 0.5 Warehouse spot price p may go up or down by 10% with probability 0.5 Discount rate k = 0.1 Chapter 1

Example Represent the tree, identifying all states Period 2 Period 1 SYST 4050 Slides Example D=144 p=$1.45 p=$1.19 D=96 p=$0.97 D=64 Period 2 Represent the tree, identifying all states D=120 p=$1.32 p=$1. 08 D=80 p=$1.08 Period 1 0.25 0.25 0.25 0.25 0.25 Period 0 0.25 D=100 0.25 p=$1.20 0.25 Chapter 1

Example – Option 1 (Spot) SYST 4050 Slides Example – Option 1 (Spot) Period 2 Starting at period T, work back to period 0 identify the expected cash flows at each step C(D = 144,000, p = 1.45, 2) = 144,000 x 1.45 = $208,800 R(D = 144,000, p = 1.45, 2) = 144,000 x 1.22 = $175,680 P(D = 144,000, p = 1.45, 2) = R – C = 175,680 – 208,800 = –$33,120 D=144 p=$1.45 D=144 p=$1.19 Cost D=96 p=$1.45 D=144 Revenue p=$0.97 D=96 p=$1.19 Profit D=96 p=$0.97 D=64 p=$1.45 D=64 p=$1.19 D=64 p=$0.97 Chapter 1

Example – Option 1 (Spot) SYST 4050 Slides Example – Option 1 (Spot) Period 2 Starting at period T, work back to period 0 identify the expected cash flows at each step D=144 p=$1.45 D=144 p=$1.19 D=96 p=$1.45 D=144 p=$0.97 D=96 p=$1.19 D=96 p=$0.97 D=64 p=$1.45 D=64 p=$1.19 D=64 p=$0.97 Chapter 1

Example – Option 1 (Spot) SYST 4050 Slides Example – Option 1 (Spot) Starting at period T, work back to period 0 identify the expected cash flows at each step EP(D = 120, p = 1.22, 1) = 0.25xP(D = 144, p = 1.45, 2) + 0.25xP(D = 144, p = 1.19, 2) + 0.25xP(D = 96 p = 1.45, 2) + 0.25xP(D = 96, p = 1.19, 2) = –$12,000 PVEP(D = 120, p = 1.22, 1) = EP(D = 120, p = 1.22, 1)/(1+k) = –12,000/1.1 = –$10,909 D=144 p=$1.45 p=$1.19 D=96 D=120 p=$1.32 0.25 Period 1 Period 2 Chapter 1

Example – Option 1 (Spot) SYST 4050 Slides Example – Option 1 (Spot) Starting at period T, work back to period 0 identify the expected cash flows at each step P(D = 120, p = 1.32, 1) = R(D = 120, p = 1.22, 1) – C(D = 120, p = 1.32, 1) + PVEP(D = 120, p = 1.22, 1) = $146,400 - $158,400 + (–$10,909) = –$22,909 D=144 p=$1.45 p=$1.19 D=96 D=120 p=$1.32 0.25 Period 1 Period 2 Chapter 1

Example – Option 1 (Spot) SYST 4050 Slides Example – Option 1 (Spot) Starting at period T, work back to period 0 identify the expected cash flows at each step D=144 p=$1.45 p=$1.19 D=96 p=$0.97 D=64 D=120 p=$1.32 p=$1. 08 D=80 0.25 Period 1 Period 2 Chapter 1

Example – Option 1 (Spot) SYST 4050 Slides Example – Option 1 (Spot) Starting at period T, work back to period 0 identify the expected cash flows at each step D=144 p=$1.45 p=$1.19 D=96 p=$0.97 D=64 D=120 p=$1.32 p=$1. 08 D=80 D=100 p=$1.20 0.25 Period 0 Period 1 Period 2 NPV(Spot) = $5,471 NPV(Spot) = $5,471 Chapter 1

Example: Decision Tree Analysis SYST 4050 Slides Example: Decision Tree Analysis Three options for Target.com Get all warehousing space from the spot market as needed Sign a three-year lease for a fixed amount of warehouse space and get additional requirements from the spot market Get 100,000 sq ft. of warehouse space at $1 per square foot Additional space purchased from spot market Sign a flexible lease with a minimum change that allows variable usage of warehouse space up to a limit with additional requirement from the spot market Chapter 1

Example – Option 2 (Fixed lease) SYST 4050 Slides Example – Option 2 (Fixed lease) Starting at period T, work back to period 0 identify the expected cash flows at each step Period 2 D=144 p=$1.45 Period 1 0.25 D=144 0.25 D=120 p=$1.19 0.25 0.25 p=$1.32 D=96 0.25 p=$1.45 D=144 Period 0 0.25 D=120 p=$0.97 p=$1. 08 D=100 D=96 0.25 p=$1.20 p=$1.19 D=80 D=96 p=$1.32 p=$0.97 0.25 D=64 D=80 p=$1.45 p=$1.32 D=64 p=$1.19 D=64 p=$0.97 Chapter 1

Example – Option 2 (Fixed lease) SYST 4050 Slides Example – Option 2 (Fixed lease) Starting at period T, work back to period 0 identify the expected cash flows at each step P(D =, p =, 2) = R(D =, p =, 2) – C(D =, p =, 2) P(D =, p =, 2) = Dx1.22 – (100,000x1.00 + Sxp) 8 Chapter 1

Example – Option 2 (Fixed lease) SYST 4050 Slides Example – Option 2 (Fixed lease) Starting at period T, work back to period 0 identify the expected cash flows at each step P(D =, p =, 1) = R(D =, p =, 1) – C(D =, p =, 1) + PVEP(D =, p =, 1) P(D =, p =, 1) = Dx1.22 – (100,000x1.00 + Sxp) + EP(D =, p =, 1)/(1+k) Chapter 1

Example – Option 2 (Fixed lease) SYST 4050 Slides Example – Option 2 (Fixed lease) Starting at period T, work back to period 0 identify the expected cash flows at each step P(D =, p =, 0) = R(D =, p =, 0) – C(D =, p =, 0) + PVEP(D =, p =, 0) P(D =, p =, 0) = 100,000x1.22 – 100,000x1.00 + 16,364/1.1 NPV(Fixed lease) = $38,364 NPV(Fixed lease) = $38,364 Chapter 1

Example: Decision Tree Analysis SYST 4050 Slides Example: Decision Tree Analysis Three options for Target.com Get all warehousing space from the spot market as needed Sign a three-year lease for a fixed amount of warehouse space and get additional requirements from the spot market Sign a flexible lease with a minimum change that allows variable usage of warehouse space up to a limit with additional requirement from the spot market $10,000 upfront payment Use anywhere between 60,000 and 100,000 sq ft. at $1 per sq ft. Additional space purchased from spot market Chapter 1

Example – Option 3 (Flexible lease) SYST 4050 Slides Example – Option 3 (Flexible lease) Flexible lease rules Up-front payment of $10,000 Flexibility of using between 60,000 and 100,000 sq.ft. at $1.00 per sq.ft. per year Additional space requirements from spot market Chapter 1

Example – Option 3 (Flexible lease) SYST 4050 Slides Example – Option 3 (Flexible lease) Starting at period T, work back to period 0 identify the expected cash flows at each step Period 2 D=144 p=$1.45 Period 1 0.25 D=144 0.25 D=120 p=$1.19 0.25 0.25 p=$1.32 D=96 0.25 p=$1.45 D=144 Period 0 0.25 D=120 p=$0.97 p=$1. 08 D=100 D=96 0.25 p=$1.20 p=$1.19 D=80 D=96 p=$1.32 p=$0.97 0.25 D=64 D=80 p=$1.45 p=$1.32 D=64 p=$1.19 D=64 p=$0.97 Chapter 1

Example – Option 3 (Flexible lease) SYST 4050 Slides Example – Option 3 (Flexible lease) Starting at period T, work back to period 0 identify the expected cash flows at each step P(D =, p =, 2) = R(D =, p =, 2) – C(D =, p =, 2) P(D =, p =, 2) = Dx1.22 – (Wx1.00 + Sxp) Chapter 1

Example – Option 3 (Flexible lease) SYST 4050 Slides Example – Option 3 (Flexible lease) Starting at period T, work back to period 0 identify the expected cash flows at each step P(D =, p =, 1) = R(D =, p =, 1) – C(D =, p =, 1) + PVEP(D =, p =, 1) P(D =, p =, 1) = Dx1.22 – (Wx1.00 + Sxp) + EP(D =, p =, 1)/(1+k) 20,000 20,000 Chapter 1

Example – Option 3 (Flexible lease) SYST 4050 Slides Example – Option 3 (Flexible lease) Starting at period T, work back to period 0 identify the expected cash flows at each step P(D =, p =, 0) = R(D =, p =, 0) – C(D =, p =, 0) + PVEP(D =, p =, 0) P(D =, p =, 0) = 100,000x1.22 – 100,000x1.00 + 38,198/1.1 NPV(Flexible lease) = $46,725 NPV(Flexible lease) = 56,725 – 10,000 = $46,725 Chapter 1

From Design to Planning SYST 4050 Slides From Design to Planning Network design C4  Designing Distribution Networks C5  Network Design in the Supply Chain C6  Network Design in an Uncertain Environment Planning in a supply chain C7  Demand Forecasting in a Supply Chain C8  Aggregate Planning in a Supply Chain C9  Planning Supply and Demand Chapter 1

What factors influence customer demand? SYST 4050 Slides Demand Forecasting How does BMW know how many Mini Coopers it will sell in North America? How many Prius cars should Toyota build to meet demand in the U.S. this year? Worldwide? When is it time to tweak production, upward or downward, to reflect a change in the market? What factors influence customer demand? Chapter 1

Factors that Affect Forecasts SYST 4050 Slides Factors that Affect Forecasts Past demand Time of year/month/week Planned advertising or marketing efforts Planned price discounts State of the economy Market conditions Actions competitors have taken Chapter 1

Example: Demand Forecast for Milk SYST 4050 Slides Example: Demand Forecast for Milk A supermarket has experienced the following weekly demand (in gallons) over the last ten weeks 109, 116, 108, 103, 97, 118, 120, 127, 114, and 122 What is a reasonable demand forecast for milk for the upcoming week? When could using average demand as a forecast lead to an inaccurate forecast? If demand turned out to be 125 what can you say about the demand forecast? Chapter 1

1) Characteristics of Forecasts SYST 4050 Slides 1) Characteristics of Forecasts Forecasts are always wrong! Forecasts should include an expected value and a measure of error (or demand uncertainty) Forecast 1: sales are expected to range between 100 and 1,900 units Forecast 2: sales are expected to range between 900 and 1,100 units Chapter 1

2) Characteristics of Forecasts SYST 4050 Slides 2) Characteristics of Forecasts Long-term forecasts are less accurate than short-term forecasts Less easy to consider other variables Hard to include the effects of weather in a forecast Forecast horizon is important, long-term forecast have larger standard deviation of error relative to the mean Chapter 1

3) Characteristics of Forecasts SYST 4050 Slides 3) Characteristics of Forecasts Aggregate forecasts are more accurate than disaggregate forecasts Chapter 1

3) Characteristics of Forecasts SYST 4050 Slides 3) Characteristics of Forecasts Aggregate forecasts are more accurate than disaggregate forecasts They tend to have a smaller standard deviation of error relative to the mean Monthly sales SKU Monthly sales product line Chapter 1

4) Characteristics of Forecasts SYST 4050 Slides 4) Characteristics of Forecasts Information gets distorted when moving away from the customer Bullwhip effect Bullwhip effect: Because forecast errors are a given, companies often carry an inventory buffer called "safety stock". Moving up the supply chain from end-consumer to raw materials supplier, each supply chain participant has greater observed variation in demand and thus greater need for safety stock. In periods of rising demand, down-stream participants will increase their orders. In periods of falling demand, orders will fall or stop in order to reduce inventory. The effect is that variations are amplified as one moves upstream in the supply chain (further from the customer). Chapter 1

Characteristics of Forecasts SYST 4050 Slides Characteristics of Forecasts Forecasts are always wrong! Long-term forecasts are less accurate than short-term forecasts Aggregate forecasts are more accurate than disaggregate forecasts Information gets distorted when moving away from the customer Chapter 1

Is demand forecasting more important for a push or pull system? SYST 4050 Slides Role of Forecasting Supplier Manufacturer Distributor Retailer Customer Push Push Push Pull Push Push Pull Push: in anticipation of customer demand Pull: in response of customer demand Forecasting is used for both push and pull processes Push processes: Managers must plan the level of activity, be it production, transportation, or any other planned activity. Pull processes: Managers must plan the level of available capacity and inventory Always: Managers must forecast what customer demand will be Manufacturer: Production scheduling decisions, materials requirement planning, purchasing Distributor: Ordering, warehouse capacity Retailer: Shelf space Dell orders PC components in anticipation of customer orders (inventory) Dell performs assembly in response to customer orders (capacity) Intel needs forecasts to determine production and inventory levels. Intel’s suppliers need forecasts to determine production and inventory levels. Mature products with stable demand (milk, paper towels) are usually the easiest to forecast. Scheduling existing resources How many employees do we need and when? How much product should we make in anticipation of demand? Acquiring additional resources When are we going to run out of capacity? How many more people will we need? How large will our back-orders be? Determining what resources are needed What kind of machines will we require? Which services are growing in demand? declining? What kind of people should we be hiring? Push Pull Is demand forecasting more important for a push or pull system? Chapter 1

Types of Forecasts Qualitative Time series Causal Simulation SYST 4050 Slides Types of Forecasts Qualitative Primarily subjective, rely on judgment and opinion Time series Use historical demand only Causal Use the relationship between demand and some other factor to develop forecast Simulation Imitate consumer choices that give rise to demand Qualitative They are primarily subjective; rely on judgment and opinion, intuition, surveys, or comparative techniques. They produce information typically no quantitative, and subjective. Its non-scientific nature makes it difficult to standardize or validate for accuracy, Appropriate when there is little historical data or there is market intelligence. Time Series It uses historical demand only; appropriate when the basic demand pattern varies little between years, the basic premise is that the future demand pattern will be mostly a replication. It uses mathematician and statistical models as forecasting tools which can be static or adaptive to new demand patterns. Causal It uses the relationship between demand and some other factor (e.g. the state of the economy, interest rates) to develop forecast. For example, if is know how level service can influence sales, then by knowing the level of service provided, the level of sales can be projected. We can say that service causes sales. Another example is forecasting the impact of price promotion on demand. Usually it is very difficult to find good cause-and-effect relationships. Simulation This method imitates consumer choices that give rise to demand. It can combine time series and causal methods Chapter 1

Components of an Observation SYST 4050 Slides Components of an Observation Quarterly demand at Tahoe Salt Actual demand (D) Chapter 1

Components of an Observation SYST 4050 Slides Components of an Observation Quarterly demand at Tahoe Salt Level (L) and Trend (T) Chapter 1

Components of an Observation SYST 4050 Slides Components of an Observation Quarterly demand at Tahoe Salt Seasonality (S) Chapter 1

Components of an Observation SYST 4050 Slides Components of an Observation Observed demand = Systematic component + Random component L Level (current deseasonalized demand) T Trend (growth or decline in demand) S Seasonality (predictable seasonal fluctuation) Chapter 1

Time Series Forecasting SYST 4050 Slides Time Series Forecasting Forecast demand for the next four quarters. Chapter 1