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Susan Cholette DS855 Fall 2006


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

Susan Cholette DS855 Fall 2006
Expected demand
Risk of shortage
Service level
Determining Optimal Level of Product Availability

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

What Do We Mean by Optimal?
  • Product availability is measured by the cycle
    service level or the fill rate
  • Product availability affects supply chain
  • 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?

Factors Affecting the Optimal Level of Product
  • 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?
  • 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?

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?

One Time Orders for Single-Season Products
  • 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
  • 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

Setting Service Levels for Continuously Stocked
  • 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
  • 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?

Managerial Levers to Improve Supply Chain
  • 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
  • Improved forecasting
  • Quick response (placing a second order)
  • Postponement
  • Tailored sourcing

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

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
  • 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
  • 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?

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

Quick Response
  • Set of actions taken by managers to reduce lead
  • 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
  • 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

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

  • 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

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)
  • Production cost using Option 2 (uncolored thread)
  • Allows postponement (dye later) but is more
  • What is the value of postponement?
  • Expected profit increases from 94,576 to
  • Sheet 5 of ch12-inv shows this example in detail

Value of Postponement with Dominant Product
  • Color with dominant demand Mean 3,100, SD
  • 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?

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

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
  • 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

Vendor-Managed Inventories (VMI)
  • Manufacturer or supplier is responsible for all
    decisions regarding inventory at the retailer
  • Control of replenishment decisions moves to the
  • 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

  • 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
  • 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

Setting Optimal Levels of Product Availability in
  • 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

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?

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?

Example Pricing and Sourcing Strategy
  • Lands End needs to determine how many cashmere
    sweaters to buy and how deeply to discount
  • 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 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

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
  • 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 ?

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?