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Planning Demand and Supply in a Supply Chain. Forecasting and Aggregate Planning Chapters 8 and 9. Learning Objectives. Overview of forecasting Forecast errors Aggregate planning in the supply chain Managing demand Managing capacity. Phases of Supply Chain Decisions.

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planning demand and supply in a supply chain

Planning Demand and Supply in a Supply Chain

Forecasting and Aggregate Planning

Chapters 8 and 9

learning objectives
Learning Objectives
  • Overview of forecasting
  • Forecast errors
  • Aggregate planning in the supply chain
  • Managing demand
  • Managing capacity
phases of supply chain decisions
Phases of Supply Chain Decisions
  • Strategy or design: Forecast
  • Planning: Forecast
  • Operation/ExecutionActual demand
  • Since actual demands differ from the forecasts, …

so does the execution from the plans.

    • E.g. Supply Chain degree plans for 40 students per year whereas the actual is ??
characteristics of forecasts
Characteristics of forecasts
  • Forecasts are always wrong. Include expected value and measure of error.
  • Long-term forecasts are less accurate than short-term forecasts.

Too long term forecasts are useless: Forecast horizon

    • Forecasting to determine
      • Raw material purchases for the next week; Ericsson
      • Annual electricity generation capacity in TX for the next 30 years; Texas Utilities
      • Boat traffic intensity in the upper Mississippi until year 2100; Army Corps of Engineers
  • Aggregate forecasts are more accurate than disaggregate forecasts
    • Variance of aggregate is smaller because extremes cancel out
      • Two samples: {3,5} and {2,6}.

Averages: 4 and 4.

Totals : 8 and 8.

      • Variance of sample averages/totals=0
      • Variance of {3,5,2,6}=5/2
    • Several ways to aggregate
      • Products into product groups; Telecom switch boxes
      • Demand by location; Texas region
      • Demand by time; April demand
forecasting methods
Forecasting Methods
  • Qualitative
    • Expert opinion
      • E.g. Why do you listen to Wall Street stock analysts?
    • What if we all listen to the same analyst? S/He becomes right!
  • Time Series
    • Static
    • Adaptive
  • Causal: Linear regression
  • Forecast Simulation for planning purposes
master production schedule mps
Master Production Schedule (MPS)
  • MPS is a schedule of future deliveries. A combination of forecasts and firm orders.
aggregate planning ag gregate past part of ad gregare totaled
Aggregate Planning (Ag-gregate: Past part. of Ad-gregare: Totaled)
  • If the actual is different than the plan, why bother sweating overdetailed plans
  • Aggregate planning: General plan for our frequency decomposition
    • Combined products = aggregate product
      • Short and long sleeve shirts = shirt
        • Single product
      • AC and Heating unit pipes = pipes at Lennox Iowa plant
    • Pooled capacities = aggregated capacity
      • Dedicated machine and general machine = machine
        • Single capacity
          • E.g. SOM has 100 instructors
    • Time periods = time buckets
      • Consider all the demand and production of a given month together
        • When does the demand or production take place in a time bucket?
        • Increase the number of time buckets; decrease the bucket length.
fundamental tradeoffs in aggregate planning
Fundamental tradeoffs in Aggregate Planning

Capacity: Regular time, Over time, Subcontract?

Inventory: Backlog / lost sales, combination: Customer patience?

Basic Strategies

  • Chase (the demand) strategy;produce at the instantaneous demand rate
    • fast food restaurants
  • Level strategy;produce at the rate of long run average demand
    • swim wear
  • Time flexibility;high levels of workforce or capacity
    • machining shops, army
  • Deliver late strategy
    • spare parts for your Jaguar
matching the demand
- Which is which?
    • Level
    • Deliver late
    • Chase
    • Time flexibility
Matching the Demand

Adjust thecapacity

to match the demand

Demand

Use capacity

Demand

Demand

Use inventory

Use delivery time

Demand

capacity demand matching inventory capacity tradeoff
Capacity Demand Matching Inventory/Capacity tradeoff
  • Level strategy: Leveling capacity forces inventory to build up in anticipation of seasonal variation in demand
  • Chase strategy: Carrying low levels of inventory requires capacity to vary with seasonal variation in demand or enough capacity to cover peak demand during season
case study aggregate planning at red tomato
Case Study: Aggregate planning at Red Tomato
  • Farm tools:
  • Shovels
  • Spades
  • Forks

Aggregate by similar characteristics

Same characteristics?

Generic tool, call it Shovel

aggregate planning
Aggregate Planning

What is the cost of production per tool? That is materials plus labor.

Overtime production is more expensive than subcontracting.

What is the saving achieved by producing a tool in house rather than subcontracting?

1 aggregate planning decision variables
1. Aggregate Planning (Decision Variables)

Wt= Number of employees in month t, t = 1, ..., 6

Ht = Number of employees hired at the beginning of month t, t = 1, ..., 6

Lt = Number of employees laid off at the beginning of month t, t = 1, ..., 6

Pt= Production in units of shovels in month t, t = 1, ..., 6

It = Inventory at the end of month t, t = 1, ..., 6

St= Number of units backordered at the end of month t, t = 1, ..., 6

Ct= Number of units subcontracted for month t, t = 1, ..., 6

Ot= Number of overtime hours worked in month t, t = 1, ..., 6

Did we aggregate production capacity?

2 objective function
2. Objective Function:

3. Constraints

  • Workforce size for each month is based on hiring and layoffs
  • Production (in hours) for each month cannot exceed capacity (in hours)
3 constraints
3. Constraints
  • Inventory balance for each month

Period

t+1

Period

t-1

Period

t

3 constraints1
3. Constraints
  • Overtime for each month
execution
Execution
  • Solve the formulation, see Table 8.3
    • Total cost=$422.275K, total revenue=$640K
  • Apply the first month of the plan
  • Delay applying the remaining part of the plan until the next month
  • Rerun the model with new data next month
  • This is called rolling horizon execution
aggregate planning at red tomato tools1
Aggregate Planning at Red Tomato Tools

This solution was for the following demand numbers:

What if demand fluctuates more?

increased demand fluctuation
Increased Demand Fluctuation

Total costs=$432.858K.

16000 units of total production as before why extra cost?

With respect to $422.275K of before.

matching demand and supply
Matching Demand and Supply
  • Supply = Demand
  • Supply < Demand => Lost revenue opportunity
  • Supply > Demand => Inventory
  • Manage Supply – Productions Management
  • Manage Demand – Marketing
managing predictable variability with supply
Managing Predictable Variability with Supply

Manage capacity

  • Time flexibility from workforce (OT and otherwise)
  • Seasonal workforce, agriculture workers
  • Subcontracting
  • Counter cyclical products: complementary products
    • Similar products with negatively correlated demands
      • Snow blowers and Lawn Mowers
      • AC pumps and Heater pumps
  • Flexible capacities/processes: Dedicated vs. flexible

a

d

d

a

a,b,

c,d

c

c

b

b

Similar capabilities

One super facility

managing predictable variability with inventory
Managing Predictable Variability with Inventory
  • Component commonality
    • Remember fast food restaurant menus
    • Component commonality increase the benefit of postponement.
      • More on this later
  • Build seasonal inventory of predictable products in preseason
    • Nothing can be learnt by procrastinating
  • Keep inventory of predictable products in the downstream supply chain
managing predictable variability with pricing revisit red tomato tools
Managing Predictable Variability with PricingRevisit Red Tomato Tools
  • Manage demand with pricing
    • Original pricing:
      • Cost = $422,275, Revenue = $640,000, Profit=$217,725
  • Demand increases from discounting
    • Market growth
    • Stealing market share from competitors
    • Forward buying
      • stealing your own market share from the future

Discount of $1 in a period increases that period’s demand by 10% (market and market share growth) and moves 20% of next two months demand forward

Can you gather this information –price sensitivity of the demand- easily? Does your company have this information?

off peak january discount from 40 to 39
Off-Peak (January) Discount from $40 to $39

Cost = $421,915, Revenue = $643,400, Profit = $221,485

peak april discount from 40 to 39
Peak (April) Discount from $40 to $39
  • Cost = $438,857, Revenue = $650,140, Profit = $211,283
  • Discounting during peak increases the revenue
  • but decreases the profit!
demand management
Demand Management
  • Pricing and Aggregate Planning must be done jointly
  • Factors affecting discount timing and their new values
    • Consumption: 100% increase in consumption instead of 10% increase
    • Forward buy, still 20% of the next two months
    • Product Margin: Impact of higher margin. What if discount from $31 to $30 instead of from $40 to $39.)
january discount 100 increase in consumption sale price 40 39
January Discount: 100% increase in consumption, sale price = $40 ($39)

Off peak discount: Cost = $456,750, Revenue = $699,560

Profit=$242,810

peak april discount 100 increase in consumption sale price 40 39
Peak (April) Discount: 100% increase in consumption, sale price = $40 ($39)
  • Peak discount: Cost = $536,200, Revenue = $783,520
  • Profit=$247,320
performance under different scenarios
Performance Under Different Scenarios

Use rows in bold to explain Xmas discounts.

The product, with less (forward buying/market growth) ratio, is discounted more.

What gift should you buy on the special days (peak demand) when retailers

supposedly give discounts?

E.g.Think of flowers on valentine’s day. How about diamonds?

For flowers, what is (forward buying/market growth) due to discounting?

How about for diamonds?

Needempirical data. What is available?

empirical data who spends how much on valentine s day
Empirical Data: Who spends / How much on Valentine’s day
  • The average consumer spends $122.98 on 2008 Valentine’s Day, similar to $119.67 of 2007. Total US spending on Valentine’s Day is $17.02 B by 18+.
  • Spending
    • by gender
      • Men again dishes out the most in 2008, spending an average of $163.37 on gifts and cards, compared to an average of $84.72 spent by women.
    • by age
      • Adults: 25-34 spend $160.37.
      • Young adults: 18-24 spend $145.59.
      • Upper Middle age: 45-54 spend $117.91.
      • Lower Middle age: 35-44 spend $116.35.
      • Elderly: 55-64 spend $110.97.
  • Gifts
      • 56.8% of all consumers give a greeting card.
      • 48.2% plan a special night out.
      • 48.0% buy candy.
      • 35.9% buy flowers.
      • 12.3% give a gift card.
      • 11.8% buy clothing.
      • ??.?% buy diamonds
          • Source: National Retail Federation www.nrf.com

Where is forward buy or market growth

due to discounting?

aside continuous compounding
Aside: Continuous Compounding
  • If my $1investment earns an interest of r per year, what is my interest+investment at the end of the year?

Answer: (1+r)

  • If I earn an interest of r/2 per six months, what is my interest+ investment at the end of the year?

Answer: (1+r/2)2

  • If I earn an interest of (r/m) per (12/m) months, what is my interest+investment?

Answer: (1+r/m)m

  • Think of continuous compounding as the special case of discrete-time compounding when m approaches infinity.
  • What if I earn an interest of (r/infinity) per (12/infinity) months?

See the appendix of scaggregate.pdf for more on continuous compounding.

deterministic capacity expansion issues
Deterministic Capacity Expansion Issues
  • Single vs. Multiple Facilities
    • Dallas and Atlanta plants of Lockheed Martin
  • Single vs. Multiple Resources
    • Machines and workforce; or aggregated capacity
  • Single vs. Multiple Product Demands
    • Have you aggregated your demand when studying the capacity?
  • Expansion only or with Contraction
    • Is there a second-hand machine market?
  • Discrete vs. Continuous Expansion Times
    • Can you expand SOM building capacity during the spring term?
  • Discrete vs. Continuous Capacity Increments
    • Can you buy capacity in units of 2.313832?
  • Resource costs, economies of scale
  • Penalty for demand-capacity mismatch
    • Recallable capacity: Electricity block outs vs Electricity buy outs
      • Happens in Wisconsin Electricity market
      • What if American Airlines recalls my ticket
  • Single vs. Multiple decision makers
a simple model
A Simple Model

No stock outs. x is the size of the capacity increments.

δ is the increase rate of the demand.

infinite horizon total discounted cost
Infinite Horizon Total Discounted Cost
  • f(x) is expansion cost of capacity increment of size x
  • C(x) is the long run (infinite horizon) total discounted

expansion cost

solution of the simple model
Solution of the Simple Model

Solution can be: Each time expand capacity by an amount

that is equal to 30-week demand.

shortages inventory holding subcontracting
Shortages, Inventory Holding, Subcontracting
  • Use of Inventory and subcontracting to delay capacity expansions
stochastic capacity planning the case of flexible capacity
1

A

2

B

3

Plants

Stochastic Capacity Planning: The case of flexible capacity
  • Plant 1 and 2 are tooled to produce product A
  • Plant 3 is tooled to produce product B
  • A and B are substitute products
    • with random demands DA + DB = Constant

y1A=1, y2A=1, y3A=0

y1B=0, y2B=0, y3B=1

Products

capacity allocation
Capacity allocation
  • Say capacities are r1=r2= r3=100
  • Suppose that DA + DB = 300 and DA >100 and DB >100

With plant flexibility y1A=1, y2A=1, y3A=0, y1B=0, y2B=0, y3B=1.

If the scenarios are equally likely, expected shortage is 50.

capacity allocation with more flexibility
Capacity allocation with more flexibility
  • Say capacities are r1=r2= r3=100
  • Suppose that DA + DB = 300 and DA >100 and DB >100

With plant flexibility y1A=1, y2A=1, y3A=0, y1B=0, y2B=1, y3B=1.

Flexibility can decrease shortages. In this case, from 50 to 0.

a formulation with known demands d j d j
A Formulation with Known Demands: Dj=dj
  • i denotes plants
  • j denotes products, not necessarily substitutes
  • cij tooling cost to configure plant i to produce j
  • mj contribution to margin of producing/selling a unit of j
  • ri capacity at plant i
  • Dj=dj product j demand
  • yij=1 if plant i can produce product j, 0 o.w.
  • xij=units of j produced at plant i

Solutions depend on scenarios:

- If DA=200 and DB=100, then y1A=y2A=y3B=1.

- If DA=100 and DB=200, then y1A=y2B=y3B=1.

unknown demands d j d j k with probability p k
Unknown Demands: Dj=djk with probability pk
  • Dj=djkproduct j demand

under scenario k

  • xijk= units of j produced

at plant i if scenario k

happens

  • yij=1 if plant i can produce

product j, 0 o.w.

  • Does yijdiffer under

different scenarios?

Should my variable depend on scenarios? (Yes / No)

Anticipatory variable and Nonanticapatory variable

reality check how do car manufacturers assign products to plants
Reality Check: How do car manufacturers assign products to plants?
  • With the last formulation, we treated the problem of assigning products to plants.
  • This type of assignment called for tooling/preparation of each plant appropriately so that it can produce the car type it is assigned to.
  • These tooling (nonanticipatory) decisions are made at most once a year and manufacturers work with the current assignments to meet the demand.
  • When market conditions change, the product-to-plant assignment is revisited.
    • Almost all car manufacturers in North America are retooling their previously truck manufacturing plants to manufacture compact cars as consumer demand basically disappeared for trucks with high gas prices.
    • Also note that the profit margin made from a truck sale is 2-5 times more than the margin made from a car sale. No wonder why manufacturers prefer to sell trucks!
  • In the following pages, you will find the product to plant assignment of major car manufacturers in the North America. These assignments were updated in the summer of 2008 just about the time when manufacturers started talking about retooling plants to produce compact cars.
all of toyota plants in the north america
All of Toyota Plants in the North America

Toyota. Cambridge

Corolla, Matrix, Lexus, Rav4

Toyota-Subaru. LaFayette

Camry

Nummi: Toyota-GM. Freemont.

Corolla, Tacoma, Pontiac Vibe

Toyota. Princeton

Tundra, Suquoia, Sienna

Toyota. Georgetown

Avalon, Camry, Solara

Toyota. Long Beach

Hino

Toyota. Blue Springs

Highlander

Toyota. Tijuana, Mexico

Tacoma

Toyota. San Antonio

Tundra

all of honda plants in the north america
All of Honda Plants in the North America

Honda. Alliston, Ca.

Civic, Acura, Odyssey,

Pilot, Ridgeline

Honda. Decatur

TBO in 2008

Honda. Marysville

Accord, Acura

Honda. Lincoln

Odyssey, Pilot

Honda. El Salto, Me

Accord

all of nissan plants in the north america
All of Nissan Plants in the North America

Nissan. Smyrna

Frontier, Xterra, Altima,

Maxima, Pathfinder

Nissan. Canton

Quest, Armada, Titan, Infiniti, Altima

all of hyundai kia plants in the north america
All of Hyundai-Kia Plants in the North America

Kia. LaGrange

TBO in 2009

Hyundai. Montgomery

Sonata, Santa Fe

all of mercedes and bmw plants in the north america
All of Mercedes and BMW Plants in the North America

BMW. Spartanburg

Z4, X5, X6

M roadster, coupes

Mercedes. Tuscaloosa

M, R classes

all of ford plants in the north america
All of Ford Plants in the North America

Ford. Oakville, Ca.

Edge, Lincoln MKX

Ford. Dearborn

F-150, Lincoln Mark LT

Ford. Saint Paul

Ranger, Mazda B series

Ford. Saint Thomas, Ca.

Crown Victoria, Grand Marquis

Ford. Flat Rock

Mustang, Mazda 6

Ford. Wayne

Focus, Expedition, Lincoln Navigator

Ford. Chicago

Taurus, Mercury Sable

Ford. Avon Lake

E Series

Ford. Kansas City

Escape, Escape Hybrid,

Mazda Tribute,

Mercury Mariner, F-150

Ford. Louisville

F-250, F-550, Explorer,

Mercury Mountaineer

Ontario, Michigan, Illinois,

Indiana, Ohio in Focus

Ford. Hermosillo, Mex. Ford Fusion, Lincoln MKZ, Mercury Milan

Ford. Cuatitlan, Mex.

F-150, 250, 350, 450, 550,Ikon

all of chrysler plants in the north america
All of Chrysler Plants in the North America

Chrysler. Sterling Heights

Dodge Avenger, Chrysler Sebring

Chrysler. Brampton, Ca

Chrysler 300,

Dodge Challenger,

Dodge Charger

Chrysler. Warren

Dodge Ram, Dakota, Mitsubishi Raider

Chrysler. Detroit-Jefferson North

Jeep Grand Cherokee and Commander

Chrysler. Detroit-Conner Ave.

Dodge Viper, SRT 10 Roadster

Chrysler. Windsor, Ca

Dodge Grand Caravan, Chrysler Town

Chrysler. Newark

Dodge Durango, Chrysler Aspen

Will close in 2009

Chrysler. Belvidere

Dodge Caliber, Jeep Compass, Jeep Patriot

Chrysler. Toledo

Jeep Liberty, Dodge Nitro

Chrysler. Fenton-North

Dodge Ram

Chrysler. Fenton-South

Grand Voyager, Grand Caravan, Cargo Van

Ontario, Michigan, Illinois,

Indiana, Ohio in Focus

Chrysler. Saltillo, Mex.

Dodge Ram

Chrysler. Toluca, Mex.

Chrysler PT Cruise, Dodge Journey

all of gm plants in the north america
All of GM Plants in the North America

GM. Oshawa, Ca

Chevy Impala, Buick Allure, Chevy Silverado, GMC Sierra.

Trucks will stop in 2009.

GM. Orion

Pontiac G6,

Chevrolet Malibu

GM. Lansing-Grand River

Cadillac E-SRX

GM. Lansing-Delta Township

Buick Enclave, Saturn Outlook, GMC Acadia

GM. Flint

GMC Sierra, Chevy Silverado, Chevy - GMC medium trucks.

GM. Pontiac

Chevy Silverado, GMC Sierra

GM. Detroit

Buick Lucerne,

Cadillac DTS

GM. Janisville

Chevy Tahoe, Suburban, GMC Yukon

Will stop in 2010

GM. Wilmington

Saturn L series, Pontiac Solstice

GM. Fort Wayne

Chevy Silverado, GMC Sierra

GM. Fairfax

Chevy Malibu, Malibu Maxx, Saturn Aura

GM. Bowling Green

Cadillac XLR,

Chevy Corvette

GM. Moraine

Chevy Trailblazer, GMC Envoy, Oldsmobile Bravada, Isuzu Ascender, Saab 9-7X

Will stop in 2010

GM. Wentzville

Chevy Express, GMC Savana

GM. Lordstown

Chevy Cobalt, Pontiac Pursuit, G4, G5

GM. Spring Hill

Saturn Ion and Vue

Currently down

GM. Doraville

Chevy Uplander,

Pontiac Montana

GM. Arlington

Chevy Tahoe, Suburban,

GMC Yukon,

Cadillac Escalade

Ontario, Michigan, Illinois,

Indiana, Ohio in Focus

GM. Shreveport

Chevy Colorado, GMC Canyon, Isuzu brands, Hummer H3

GM. Ramos Arizpe, Mex.

Pontiac Aztek, Chevy Cavalier, Chevrolet Checy, Pontiac Sunfire, Buick Rendezvous

GM. Silao, Mex.

Chevrolet Suburban, Chevrolet Avalanche, GMC Yukon, Cadillac Escalade

GM. Toluca, Mex.

Chevrolet Kodiak Truck

Stopping in 2008

summary of learning objectives
Summary of Learning Objectives
  • Forecasting
  • Aggregate planning
  • Supply and demand management during aggregate planning with predictable demand variation
    • Supply management levers
    • Demand management levers
  • Capacity Planning
material requirements planning
Material Requirements Planning
  • Master Production Schedule (MPS)
  • Bill of Materials (BOM)
  • MRP explosion
  • Advantages
    • Disciplined database
    • Component commonality
  • Shortcomings
    • Rigid lead times
    • No capacity consideration
optimized production technology
Optimized Production Technology
  • Focus on bottleneck resources to simplify planning
  • Product mix defines the bottleneck(s) ?
  • Provide plenty of non-bottleneck resources.
  • Shifting bottlenecks
just in time production
Just in Time production
  • Focus on timing
  • Advocates pull system, use Kanban
  • Design improvements encouraged
  • Lower inventories / set up time / cycle time
  • Quality improvements
  • Supplier relations, fewer closer suppliers, Toyota city
  • JIT philosophically different than OPT or MRP, it is not only a planning tool but a continuous improvement scheme
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