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Susan Cholette DS855 Fall 2006

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Managerial levers to improve supply chain profitability ... Less overstock. Why would this benefit be especially attractive to clothing retailers? ... – PowerPoint PPT presentation

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Title: Susan Cholette DS855 Fall 2006


1
Susan Cholette DS855 Fall 2006
Expected demand
Risk of shortage
Service level
Quantity
z
z-scale
0
Determining Optimal Level of Product Availability

2
Outline
  • The importance of the level of product
    availability
  • Factors affecting the optimal level of product
    availability
  • Managerial levers to improve supply chain
    profitability
  • Supply chain contracts and their impact on
    profitability
  • Setting optimal levels of product availability in
    practice
  • Digression Introduction to Simulation
  • Supply Chain contracts (section 12.4) are ignored
    in this class

3
What Do We Mean by Optimal?
  • Product availability is measured by the cycle
    service level or the fill rate
  • Product availability affects supply chain
    responsiveness
  • From previous chapter, recall trade-off
  • High levels of product availability ? increased
    responsiveness and higher revenues
  • High levels of product availability ? increased
    inventory levels and higher costs
  • Product availability is related to profit
    objectives, and strategic and competitive issues
    (e.g., Nordstroms verses Ross)
  • We now ask what is the level of fill rate or
    cycle service level that will result in maximum
    supply chain profits?

4
Factors Affecting the Optimal Level of Product
Availability
  • What is the Cost of over-stocking? Co
  • What is the Cost of under-stocking? Cu
  • Possible scenarios
  • Does the product have a finite duration?
    (trend/spoilage/seasonal)
  • Yes- i.e. Seasonal items with a single order
    placed for the season
  • No- i.e. Continuously stocked, stable,
    non-spoilage items
  • How is demand during a stock-out treated?
  • What if all Demand during stock-out is
    backlogged?- i.e. there is a cost for an
    out-of-stock, but the order is not lost
  • Or….
  • What if all Demand during stock-out is
    irretrievably lost?

5
One Time Orders for Single-Season Products
  • A Season can be as long as a half-year for the
    sale of Skis from late Fall through Spring. Or it
    can be as short as 15 minutes (How many
    hamburgers to prepare in anticipation of
    customers arriving?)
  • Commonality is that we place the order before the
    season with no way of ordering extra product if
    demand is better than expected.
  • Once the season is over, the product has a
    limited (if any) salvage value
  • BUS786 students may recognize this as the
    newsboy problem - How many daily newspapers to
    stock at the kiosk?

6
One Time Orders for Single-Season Products
Calculations
  • Calculations you may remember from BUS786 (Also
    on sheet 1 Ch12_inv.xls)
  • Optimal SL Cu/(Cu Co)
  • Optimal Order Size Q m zs where z
    NORMSINV(SL)
  • So how do we get Expected Profit, or E(pr)?
  • E(pr) (p-s)mNORMDIST((Q-m)/s,0,1,1)
  • (p-s)sNORMDIST(((Q-m)/s,0,1,0)
  • - Q(c-s)NORMDIST(Q,m,s,1)
  • Q(p-c)(1-NORMDIST(Q,m,s,1))
  • Ugh! E(pr) will not be on any 855 quizzes or
    tests!

7
Setting Service Levels for Continuously Stocked
Items
  • More complex than for single-season
  • For items that are stock continuously and have
    repeated orders, here are some scenarios (demo
    sheet 2 on ch12_inv.xls)
  • If we can backlog all demand for out-of-stock
    items (and incur Cost Cu for discount, free
    express shipping, or other services to mollify
    customers)
  • Target SL 1-HQ/(DCu)
  • If we expect to lose all demand for out-of-stock
    items (What does Cu represent now?)
  • Target SL 1-HQ/(HQ DCu)
  • In this scenario, service levels will be higher
    than with 100 conversion of our backlog, if all
    other parameters are the same
  • What can we say about the target service level if
    we expect to lose some, but not all of the demand
    for the Out-of-Stock items?

8
Managerial Levers to Improve Supply Chain
Profitability
  • How do we keep product availability high, yet
    decrease inventory costs?
  • Obvious actions
  • Increase salvage value of each unit
  • Decrease the margin lost from a shortage (backup
    sourcing)
  • Improved forecasting
  • Quick response (placing a second order)
  • Postponement
  • Tailored sourcing

9
Improved Forecasts
  • Improved forecasts result in reduced uncertainty
  • Less uncertainty (lower s) results in either
  • Lower levels of safety inventory (and costs) for
    the same level of product availability
  • or
  • Higher product availability for the same level of
    safety inventory
  • or
  • Both lower levels of safety inventory and higher
    levels of product availability

10
Impact of Improving Forecasts (Example)
  • Bloomingdales buys holiday china and sells it
    from October thru December. In January all china
    will be sold in an end-of-season-sale at a huge
    discount
  • Demand Normally distributed with a mean of R
    350 and standard deviation of ? 150
  • Wholesale cost 100
  • Retail price 250
  • End-of-season sale value 80
  • What is Cost of Over-stocking and Cost of
    Under-stocking?
  • Cu p-c 250-100 150
  • Co c s 20
  • What is the Optimal Service Level?
  • SL Cu/(CuCo) 88
  • How many units should be ordered as ?R changes
    and how is expected profit affected?

11
Impact of Improving Forecasts
  • Also calculated on sheet 1 of Ch12_inv.xls

12
Quick Response
  • Set of actions taken by managers to reduce lead
    time
  • Reduced lead time results in improved forecasts
  • Typical example of quick response is multiple
    orders in one season for retail items (such as
    fashion clothing)
  • Buyers can usually make very accurate forecasts
    after seeing sales in the first week or two in a
    season
  • Multiple orders are only possible if the lead
    time is reduced otherwise there wouldnt be
    enough time to get the later orders before the
    season ends
  • Benefits
  • Lower order quantities ? less inventory, same
    product availability
  • Less overstock
  • Why would this benefit be especially attractive
    to clothing retailers?
  • Higher profits

13
Quick Response Multiple Orders Per Season
  • Sheet 3 of single order calculations done on
    sheet 3 of ch12-inv.xls goes over Saks shawl
    example on p. 356
  • Caveat- includes complications of holding costs,
    and is beyond scope of this class (even book
    example not performed correctly!)
  • In order get quantitative predictive effects of
    multiple orders per season, would have to use
    simulation.

14
Postponement
  • Delay of product differentiation until closer to
    the time of the sale of the product
  • All activities prior to product differentiation
    require aggregate forecasts more accurate than
    individual product forecasts
  • Individual product forecasts are needed close to
    the time of sale demand is known with better
    accuracy (lower uncertainty)
  • Results in a better match of supply and demand
  • Valuable in e-commerce time lag between when an
    order is placed and when customer receives the
    order (this delay is expected by the customer and
    can be used for postponement)
  • Higher profits, better match of supply and demand

15
Value of Postponement Example Benetton
  • Demand for sweaters of each of 4 colors
  • Mean demand 1,000 SD 500
  • For each garment
  • Sale price 50
  • Salvage value 10
  • Production cost using Option 1 (long lead time)
    20
  • Production cost using Option 2 (uncolored thread)
    22
  • Allows postponement (dye later) but is more
    costly
  • What is the value of postponement?
  • Expected profit increases from 94,576 to
    98,092
  • Sheet 5 of ch12-inv shows this example in detail

16
Value of Postponement with Dominant Product
  • Color with dominant demand Mean 3,100, SD
    800
  • Other three colors Mean 300, SD 200
  • Expected profit without postponement 102,205
  • Expected profit with postponement 99,872
  • In this situation it is better to not postpone,
    as higher production costs from postponement
    option dominate the profits
  • What might be a solution to improve profits?

17
Tailored Postponement Example Benetton
  • Strategy
  • Produce Q1 units for each color using Option 1
    and QA units (aggregate) using Option 2
  • Tailored postponement allows a firm to increase
    profits by postponing differentiation only for
    products with the most uncertain demand products
    with more predictable demand are produced at
    lower cost without postponement
  • Usually too complex to model without resorting to
    simulation

18
Tailored Sourcing
  • A firm uses a combination of two supply sources
  • One is lower cost but is unable to deal with
    uncertainty well
  • The other is more flexible, and can therefore
    deal with uncertainty, but is higher cost
  • Analogous to the different production options for
    Red_Tomato tools from Aggregate Planning
  • The two sources must focus on different
    capabilities
  • Depends on being able to have one source that
    faces very low uncertainty and can therefore
    reduce costs
  • Increase profits, better match supply and demand

19
Vendor-Managed Inventories (VMI)
  • Manufacturer or supplier is responsible for all
    decisions regarding inventory at the retailer
  • Control of replenishment decisions moves to the
    manufacturer
  • Requires that the retailer share demand
    information with the manufacturer
  • Having final customer demand data also helps
    manufacturer plan production more effectively
  • Side-benefit manufacturer may have better
    information on new products than retailer
  • Real world Example MGM uses VMI in K-Mart and
    Wal-mart for their Videotapes DVDs SKUs
  • Potential drawback when retailers sell products
    that are substitutes in customers minds and
    these products are all managed by VMI (i.e. lose
    savings from aggregation of SS of combined
    products)

20
Simulation
  • Several techniques in this and other chapters
    have suggested simulation
  • Used to predict results when no closed form
    solution is possible
  • Used to validate results even when the problem
    seems simple enough to be solved with a couple
    equations!
  • With advent of cheap computing power, simulation
    is playing a greater role in diverse fields
  • Available to limited degree in excel (tendency to
    crash program)
  • Simulation techniques explored in great detail in
    a dedicated class DS851
  • Goal Run multiple instances of a scenario,
    then determine the average outcome and payoff by
    taking the average of all the scenarios

21
Setting Optimal Levels of Product Availability in
Practice
  • Use an analytical framework to increase profits
  • Verify with simulation
  • Beware of preset levels of availability
  • Use of approximate costs is acceptable
  • profit-maximizing solutions are very robust
  • Estimate a range for the cost of stocking out
  • Ensure that levels of product availability fit
    with the firms competitive strategy

22
Simulation Example
  • A game costs 5 to play and has 2 outcomes
  • Win 10 (original 5 5 more) with 55 chance
  • Lose the 5 playing fee with 45 chance
  • The expected value of playing this game once is
  • E(game) .555 .45(-5) 0.50
  • If I have 20 to start and I play the game 30
    times, how much should I expect to take home?
  • Is it 20 30E(game) 35
  • Why or why not?

23
Simulation Example
  • In general, F(E(x)) not same as E(f(x))
  • Our model ignores the possibility of going broke-
    cant play the game if run out of money!
  • Look at gambling_simulation.xls on website
  • Take average of 102 instances of a 30-round game
  • Sometimes average return is higher than 35
  • More often than not, it is lower than 35
  • Is there a right answer for what the simulated
    return is?
  • Why do we simulate so many times?

24
Example Pricing and Sourcing Strategy
  • Lands End needs to determine how many cashmere
    sweaters to buy and how deeply to discount
    leftovers
  • Sweaters sell for 150 each during winter
  • Demand is uncertain,
  • Assumed to be distributed normally, with m 3000
    and s 1000
  • Towards the end of Season, the sweaters are
    discounted
  • Discounted demand is uncertain, but is dependent
    on price
  • Normal distribution md 1000 5p , sd
    (1000 5p) /3
  • at p 80, we would average sales of 600
    sweaters, standard dev 200
  • Whatever is not sold at a discount cannot be kept
    for next year and is donated for a 20 salvage
    value per sweater

25
Inventory Simulation
  • Have a complex situation not solved by a
    closed-form equation Sheet 4 of Ch12_inv.xls or
    Appendix 12F
  • Two decisions to be modeled
  • Simulation set to be at 500 instances
  • The more instances, the more confident we are of
    the solution
  • Is this saying that we are observing demand from
    the winters of 2004 to 2504?
  • Summary statistics in grey tabulate the results
  • Another chart shows the results from 9 different
    strategies
  • How many instances were calculated, at a minimum
    to come up with this table?
  • Can we make any definitive claims about the
    superiority of one set of decisions to another ?

26
Summary of Learning Objectives
  • What are the factors affecting the optimal level
    of product availability?
  • What are the managerial levers that can be used
    to improve supply chain profitability through
    optimal service levels?
  • What tool is available for modeling complex
    inventory strategies and also to stress-test even
    simple ones?
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