Materials for Lecture 19

# Materials for Lecture 19

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## Materials for Lecture 19

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##### Presentation Transcript

1. Materials for Lecture 19 Topics for this lecture Setting up an internet business Inventory management models Decision tree models Data, where is it? Read Chapter 14 Lecture 19 Inventory Management.XLSX Lecture 19 Decision Tree.XLSX
2. Inventory Management & Data Sources Inventory management is about When to re-order How much to order Factors to consider Cost of storage Cost of placing an order Cost of lost sales due to shortage Stochastic demand Delivery time from time order is placed Can you backlog demand
3. Inventory Management Simulate the inventory management problem as a stochastic problem Simulate N periods to test impacts of alternative inventory management schemes Period -- length of time for the problem – week, month Based on the time period for the demand data Also based on order/delivery time
4. Inventory Management Example of a weekly Inv. Management Problem Cost to place an order \$200 Cost of a unit purchased \$4 Cost of storage for 1 week \$3 Cost of each lost sale \$10 Price of product sold \$25 Weekly demand PDF ~ N(40,6) 2 week delivery time; could be stochastic Beginning inventory 100 Inventory management rule to test: Place order if inventory on hand <= 50 units Amount to order = 150 – inventory on hand KOV = average weekly profit, cost, inventory, revenue
5. Rules for Simulating Inventory Demandt is stochastic Beginning inventoryt = ending inventoryt-1 Supplyt = beginning inventoryt + quantity receivedt Salest = Minimum (demandt or supplyt) Ending Inventoryt = supplyt – salest Quantity receivedt = quantity orderedt-n if it takes “n” periods for the delivery Lost salest = 0.0 If(supplyt > demandt) else Lost salest = demandt – supplyt
6. Calculating Inventory Costs Purchase costst = cost per unit paid for product Order costst = fixed cost to place an order (shipping costs, office expense, delivery processing costs, Fed Ex rush delivery fee, etc.) Storage costst = cost per unit * beginning inventoryt Penalty costst = cost to the business for lost sales or lost salest * cost for perceived lost goodwill
7. Inventory Management Model The model would have 40 to 50 weeks so the startup conditions do not dictate the results for the inventory management rule being analyzed
8. Inventory Management Scenarios Reorder Point Should firm reorder when inventory < 50? Scenario 40, 50, 60, 70, 80, 90 for the reorder point Order up to amount Should firm reorder a larger amount Scenario 140, 150, 160, 170, 190 Would it be more profitable to pay more (or less) to get the order delivered faster (slower)? Pay \$300/order to get delivery in 1 week Pay \$100/order to get delivery in 3 weeks Each question is a PDF, use simulation to estimate the unknown PDF
9. Inventory Management.XLS Scenario reorder points of: 50, 60, 70, 80, 90
10. Decision Trees Many textbooks are available to discuss decision trees and their application to decision making Decision trees are a simple way to organize decisions and outcomes Decision trees do not use simulation Decision trees could be used to construct simulation models Outline for this section of the lecture Demonstrate a simplified decision tree Demonstrate its use for decision making Demonstrate how a decision tree can be used to formulate a simulation model
11. Decision Tree Terminology Box represents a decision Circle denotes an outcome Decision tree organizes decisions and outcomes Stay in Houston 45% chance Die Run Stay in College Station 0% chance Die Hurricane coming to Galveston Stay and Die
12. Decision Tree for Business A startup business faces decision to expand or exit Franchise Business, rich beyond belief Continue Development Business Fails, dog dies, spouse leaves with an employee, you become a street person Business breaks even for years, no big profits earned Sell business
13. Decision Tree for Business High Demand A startup business faces decision to expansion has four options to consider Moderate Demand Franchise Business Low Demand Continue Development Fails to take off Franchise Business Fails Business breaks even for years, no big profits earned Sell business at a loss
14. Decision Tree to Simulation P(High) = .25 Assign probabilities to each decision point and PDFs of net returns for each choice P(Mod) = .30 Franchise Business Invest \$1million P(Success) = 0.75 P(Low) = .40 Continue Development P(Near Fail) =0.05 Start Franchise Business Fails P(F) = .25 Business breaks even for years, no big profits earned P(breakeven) = 0.25 Sell business at a loss of 50% of the initial investment
15. Decision Model with Risk
16. Decision Model with Risk
17. Decision Model with Risk
18. Decision Model with Risk
19. Decision Trees Summary Useful concept Maybe most useful in structuring our thinking about the options and the probabilities for each possible outcome When combined with a risk model it can add value to the simulation model results Is applicable to branch type decisions
20. Data, Where Do You Find It? Price projections for ag. commodities USDA Domestic Baseline at http://www.ers.usda.gov/publications/oce071/ FAPRI- Missouri Domestic Baseline at http://www.fapri.missouri.edu/ FAPRI- Iowa State International Baseline at http://www.fapri.iastate.edu/ Projection of Annual Inflation rates AFPC Baseline Working Paper http://www.afpc.tamu.edu/pubs/ Definition of a Baseline Continue the current policies for 10 years Assume average weather Simulate demand and supply forces for 10 years using econometric models Baselines are great source for point forecasts to use as your means
21. Data, Where Do You Find It? National, State and County level Data USDA-ERS at http://www.ers.usda.gov/Browse/FarmEconomy/ Provides data for costs of production, commodity outlook USDA-NASS http://www.nass.usda.gov/ Data for crops and plants, livestock and animals Planted and harvested acres Yield and production State and national season average prices for ag commodities This is where you get the historical data behind the FAPRI Baseline forecasts, used to estimate the parameters for stochastic variables
22. Data, Where Do You Find It? Data at the farm level http://www.ers.usda.gov/Browse/FarmPracticesManagement/ NASS surveys farmers and ERS tabulates the surveys, interactive web forms allow you to query the survey data base to calculate averages for questions asked in the surveys Survey farmers yourself That is what AFPC does and it works, its just costly This is where we get historical production data and local prices so we can make the local wedges
23. Data, Where Do You Find It? More about farmers production data You must use actual yield data at the farm level to reflect the producer’s actual risk Farmer yield data has more risk than county or state data because the latter is averaged over all farms in the region If you must use county yield data, at least increase relative risk to account for difference Use an EMP distribution with an Expansion factor Ỹt = Ŷt *(1+(EMP(Si , F(Si)) * Et) where Et is a fraction such as 1.3 to increase the relative risk 30%, and Si is a fraction of mean or trend
24. Data, Where Do You Find It? Some data series you have to purchase because it is cost effective or proprietary Examples of data that can be purchased: Weekly prices for grain in foreign countries Monthly ethanol prices Monthly ocean freight rates for shipping grain Daily futures and options prices Daily prices for stocks and mutual funds Where do you find data that is for sale? Look for trade publications that report weekly or monthly prices or quantities They usually offer to sell the historical data
25. Data, Where Do You Find It? Data for unobserved variables Yields for a farm you want to buy may not be available, and it they are your yield will likely be higher Yields for a crop that has never been grown Only have experiment station test plot yields Demand for a product that does not exist But there is one like it Customer acceptance for a new product Life cycle of the introduction adoption of a new seed Interview experts and simulate different aspects of the business based on their knowledge Rely on best guess parameters for simple distributions such as Uniform or GRKS
26. Summary of AGEC 622 Linear programming – what ought to be Probabilistic forecasting – capabilities of forecasting with multiple regression, exponential smoothing, seasonal analysis, and time series analysis Monte Carlo simulation – what could be …. Frame your problem in a systems framework Model design and development Parameter estimation for stochastic variables and deterministic component of a forecast Validate simulated variables Univariate and MV distributions Apply these tools for business decision making using stochastic efficiency
27. What can you take to the job? Improved Excel skills Applied econometrics Ability to organize & build a business model Make any business model a risk analysis tool Rank risky alternatives Deterministic and probabilistic forecasting Simetar Available as long as you are a fulltime student After you graduate, buy it at www.simetar.com If you do not have Simetar, you can use @Risk =NORM() same as =RISKNORMAL() =UNIFORM() same as =RISKUNIFORM()