Quantifying lost sales from shelflost sales from shelf … · Let’s analyse the OOS causes… • OOS in fashion & apparel retail stores derives from somestores derives from some

Quantifying lost sales from shelf out of stockQuantifying lost sales from shelf out-of-stock in fashion & apparel retail stores

L.Scarabotti, C.Battista, D. Falsini, M.M.Schiraldi

Department of Enterprise Engineering, «Tor Vergata» University of Rome, Italy

XVI Summer School “Francesco Turco” – 2011Padova, September 14-16, 2011

Quantifying lost sales from shelf Out-Of-Stock in fashion & apparel retail stores

• What’s Out-Of-Stock ? (OOS)• The OOS problem and consumers answer

ContextAnalysis pAnalysis

• Proposed approach and model hypotheses• A procedure for quantifying OOS in apparel retail stores

Model Proposal

• Validation on a industrial case: data analysisR lt i d f t h

Results and• Results, open issue and future researchConclusions

L.Scarabotti - "Tor Vergata” University of Rome Abano Terme (Padova, Italy) – 09/14-16/2011 2

Let’s analyse the OOS causes…

• OOS in fashion & apparel retail stores derives from somestores derives from some specific causes

• Only few expert can reallyOnly few expert can really understand the phenomenon…

• Women can ! Easily ☺


What’s OOS ?

VARIABLES RELATED TO

THE OOSREFERENCES

(A i di D d GSales velocity (Anupindi, Dada, Gupta, 1998)

Inventory level (Clark & Lee, 2000; Downs, M tt S l 2001)Inventory level Metters, Semple, 2001)

Promotional product

(Gruen, Corsten, Bharadwaj, 2002)

Store size (Gruen, Corsten, Bharadwaj, 2002)

Seasonality (Metters, 1998)

Stock t li ti

(Cetinkaya & Lee, 2000; N h i & S ith 1994)


centralization Nahmias & Smith, 1994)

The OOS problem and consumers answer

The top five solutions for a consumer forced to face an OOS situation:

Common causes and problemsin fashion & apparel industry: forced to face an OOS situation:

• purchase the item in another store

• delay the purchase (from the

in fashion & apparel industry:

• assortment

• products displaysame shop)

• replace the item with another one of the same brand

• stock list accuracy

• sales forecasts

order process• replace the item with another item

belonging to a different brand

• not purchase the item at all

• order process

• availability at the supplier premises

However in B2C it is not easy to recognize

• not purchase the item at all• delivery process

However in B2C it is not easy to recognizethe consumer behaviour as a consequence of a OOS


How can we quantify lost sales only processing sales data ?





Model Proposal




The proposed approach

• It is easy to identify a lost sale for OOS if it occurred between two successive replenishmentssuccessive replenishments

ze 25INVENTORY BUILD-UP DIAGRAM FOR ITEM XXX

Stoc

k si

z

10

15

20

25

LOT SIZE

Time0

5

STOCKOUT(i i i )(in quantities)

STOCKOUT(in days)

ESTIMATED LOT COVERAGE

Considering just the OOS occurred between two successive reorders, the depletion of a product before the next replenishment cannot be


p p pregarded as a strategic or a marketing choice, but as a mere logistics inefficiency

The proposed approach

• A lost-sale arises when it is reasonable to expect a product sale while the product is OOS (e g it is probable that a certain customer couldthe product is OOS (e.g. it is probable that a certain customer could ask for the specific product now or in the future).

• Hypothesis (important!):Hypothesis (important!): • No lost-sales have been considered after last sale of the product

(underestimation of the phenomenon)

ock

size

10152025

INVENTORY BUILD-UP DIAGRAM FOR ITEM XXX

Fading-out

NO LOST SALES FROM STOCKOUT

0Time

Sto

510 product

Stoc

k si

ze

510152025


No sales afterthe last l i h t


NO LOST SALES FROM STOCKOUT0

S

Time

5 replenishment

The procedure

Analyze the sales data for each product and filter those product which show a coefficient of variation of the sales in each store over a certain threshold

Compute the inventory level for each product in each store

Determine the number of out of stock events andDetermine the number of out-of-stock events and the number of out-of-stock days per each item

Compute the sales average in the period in which the OOS is recorded and, hence, the estimated lost sales






Model Proposal




Data analysis (1/3)

• The analyzed fashion company manages 200+ stores selling an assortment of 50’000+ product of women apparel and accessories in Italy with a total revenue50 000+ product of women apparel and accessories in Italy, with a total revenue of more than 60M€/Y

• The sales in 137 stores were analyzed from 01/09/2009 to 31/12/2009 and 877 different products were selected among those which registered out-of-stocks


Data analysis (3/3)

• Two key factors need to be analyzed and compared per each product: mean (μ) and st dev (σ) of sales rate The proposed procedure should onlymean (μ) and st dev (σ) of sales rate. The proposed procedure should only be applied with products with low coefficient of variation cv = σ/|μ|.

25ize INVENTORY BUILD-UP DIAGRAM FOR ITEM XXX

10

15

20

Stoc

k si

c = σ/|μ|= 1,27

0

5

19/11/09

21/11/09

23/11/09

25/11/09

27/11/09

29/11/09

1/12

/09

3/12

/09

5/12

/09

7/12

/09

9/12

/09

11/12/09

13/12/09

15/12/09

17/12/09

19/12/09

21/12/09

23/12/09

25/12/09

27/12/09

29/12/09

31/12/09

cv σ/|μ| 1,27

Time

0,8

1

1,2

Stoc

k si

ze


0

0,2

0,4

0,6

S

cv = σ/|μ|= 6,96


Time

0

1/9/09

8/9/09

15/9/

09

22/9/

09

29/9/

09

6/10/0

9

13/10

/09

20/10

/09

27/10

/09

3/11/0

9

10/11

/09

17/11

/09

24/11

/09

1/12/0

9

8/12/0

9

15/12

/09

22/12

/09

29/12

/09

Data analysis (2/3)

SALES COEFFICIENT OF VARIATION (Cv)

5000

6000

7000PRODUCT PRICES DISTRIBUTION

6000

7000

8000

1000

2000

3000

4000

5000

2000

3000

4000

5000

6000

CORRELATION BETWEEN Cv AND THE PRODUCT DAILY REVENUE

C v

0

1000

0 0,5 1 1,5 2 2,5 3 3,5 4 4,5 5 5,5 6 6,5 7 7,5 80

1000

10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180Price

80

90

100

Product

CORRELATION BETWEEN Cv AND THE PRODUCT DAILY REVENUE

40

50

60

70Product with low

coefficient of variation and

10

20

30

40high revenue HIGH FASHIONINDUSTRY


00 1 2 3 4 5 6 7 8 9 10 11

Cv

MASS CONSUMERAPPAREL

Results from the validation on a industrial case

ITEMS IN STOCK OUT AND LOST SALES

Number of days of the analyzed period 120Number of days of the analyzed period 120

Total analyzed stores 137

Total analyzed items 51′554Total analyzed items 51 554

Total analyzed items with OOS 877

Total sales of analyzed OOS items 7′227Total sales of analyzed OOS items 7 227

Lost sales of analyzed OOS items 2′075

Potential revenue growth rate (% of analyzed items revenues) 28,7%

202530

202530

g ( y ) ,

05

101520

05

101520


Conclusions, open issues and future research

The test of the implemented methodology on a real fashion company case confirmed that lost sales from OOS CAN be guessed using sales dataconfirmed that lost sales from OOS CAN be guessed using sales data, despite an understimation problem related to the end of seasons

Future research may concentrate on:

• Estimating lost sales with fading-out products, through the comparison of the sales trends among different stores in similar contexts

• Deepening the insight into the correlation between cv and the product daily revenue – important conclusion may arise comparing different companies/product linesp p

• Refining the procedure in order to include high Cv products – most of these products do significantly contribute to company’s revenues !


THANKS FOR YOUR ATTENTION

Laura Scarabotti, Eng, PhD Candidate

Operations Management Research GroupDepartment of Enterprise Engineering

“Tor Vergata” University of RomeVia del Politecnico 1 – 00133 Rome (Italy)

[email protected]

Documents

Quantifying lost sales from shelflost sales from shelf … · Let’s analyse the OOS causes… • OOS in fashion & apparel retail stores derives from somestores derives from some