- 1 -

ECR, Paris, France – February 08, 2018

Christoph Glock, Yacine Rekik, Aris A. Syntetos

On the impact of inventory accuracy

improvements on sales

- 2 -

Background and objectives

Inventory inaccuracies: major issue in retailing and apparel industry.

Physical stock is (typically) less than what we think it is.

Most reasonable assumption in retailing. Generally, stores are negative in terms of

stock and distribution centres are positive.

Thus, reconciling inventories may only lead to an increase in sales.

(We will see later that positive stock is also possible, still leading though to reduced

sales!)

The problem has been established; we are not here to argue for its

existence.

- 3 -

Background and objectives

But rather:

Assess the implications of the problem, or rather the implications of fixing the problem (phase 1);

Assess alternative ways of fixing the problem itself (phase 2).

Phase 1: What is the impact on (increased) sales if inventory accuracy is increased

by x%?

How does inventory accuracy develop over time after a stock take?

Is there an optimal number of stock takes?

What exactly constitutes this problem of inventory discrepancies?

Phase 2 (upon convincing everybody of the implications): what are the strategies

to be employed (algorithmic driven, new identification technologies, counting, etc.)

to fight the route causes of the problem?

- 4 -

Error free (r,Q) inventory policy

Safety Stock

L1L2

t

Reorder Point: r

Order Quantity Q

Q

Lead Time

- 5 -

Safety Stock

L1L2

t

Order Quantity Q

Q

Lead Time

Expected error free stock level

Impact of errors on the (r,Q) inventory policy

Reorder Point: r

This is the visible stock behavior: POS (Real Demand) + Replenishment

This is the invisible stock behavior: POS (Real Demand) + Replenishment + Skrink (Ghost Demand)

Actual stock level subject to errors

- 6 -

Safety Stock

L1L2

t

Order Quantity Q

Q

Lead Time

Wrong ordering

timing and quantity

Impact of errors on the (r,Q) inventory policy

Lost Sales are more

frequent

Reorder Point: r

And more importantly, they are not detectable

- 7 -

Safety Stock

L1L2

t

Order Quantity Q

Q

Lead Time

Wrong ordering

timing and quantity

Impact of errors on the (r,Q) inventory policy

Lost Sales are more

frequent

Reorder Point: r

And more importantly, they are not detectable

Errors act as “ghost” demand decreasing the stock level without generating a revenue:The Inventory is controlled based on some visible wrong information,whereas the sales are satisfied based on some correct but invisible information

- 8 -

Matched store experiment

Sales (e.g.) 12 weeks before count

Sales (e.g.) over 12 weeks

“Test”

“Control”

Sto

ck C

ou

nt

Sales 12 weeks after the inventory records have been

“trued up”

No

Sto

ck

Co

un

t

Sales 12 weeks after

- 9 -

Empirical analysis

Currently working with 8 Retailers across Europe:

NDAs have been signed and we are in various phases with regards to data transfer and analysis;

4 Grocery retailers (supermarkets), 2 Apparel retailers and 2 other;

Customised reports to be produced for all participating retailers.

Initial results:

2 Grocery retailers: ALPHA and BETA

- 10 -

Aim of the study

There is some published evidence suggesting that as much as 65% of inventory

records are wrong (Nicole DeHoratius, 2012). This is True for retailers ALPHA and BETA:

Our objective is not to prove the existence of Discrepancies, but to help answering the

questions:

Does more accurate inventory grow sales, if so by how much?

What investment is required to improve it?

Week 1 2 3 4 5 Average

Negative Discrepancy 26.46% 28.39% 27.35% 28.33% 35.42% 29.19%

Positive Discrepancy 11.34% 12.99% 12.67% 13.33% 11.44% 12.36%

Zero Discrepancy 62.20% 58.62% 59.97% 58.34% 53.14% 58.45%

Week 12

Negative Discrepancy 29.93%

Positive Discrepancy 24.44%

Zero Discrepancy 45.63%

Retailer ALPHA Retailer BETA

- 11 -

Experiment at Retailer ALPHA

12

weeks

Stock Audit

Sales in the Test and Control stores are compared

and the impact of the stock audit on sales of the

test store is investigated

12

weeks

CONTROL

Store

TEST

Store

- 12 -

SKUs Clustering: ABC classification based on Turnover

Turnover: 69.87%

Turnover: 25.06%

Turnover: 5.07%0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

Turnover Contribution

Fast Movers

21.26% of

SKUs

Middle Movers

40.91% of

SKUs

Slow Movers

37.81% of

SKUs

- 13 -

SKUs Clustering: ABC classification based on Turnover

Discrepancy: 57.93%

Discrepancy: 28.23%

Discrepancy; 13.83%

Turnover: 69.87%

Turnover: 25.06%

Turnover: 5.07%0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

Discrepancy Contribution Turnover Contribution

Fast Movers (17.78% of

SKUs) are generating

69.87% of turnover. They

also represent 57.93% of

Discrepancies

Fast Movers

21.26% of

SKUs

Middle Movers

40.91% of

SKUs

Slow Movers

37.81% of

SKUs

A more accurate

inventory system will

highly benefit the Fast

Movers!

- 14 -

SKUs Clustering: ABC classification based on Discrepancy

Less than 2% of

SKUs are generating

70% of total

discrepancy

But 45.6% of High

Discrepancy Class

also belong to the

Fast Mover Class

The Fast/High SKUs (Fast

Mover class for Sales and

High Discrepancy class for

Discrepancy) needs a

careful analysis of

inaccuracy sources and

operations improvements

Discrepancy

Discrepancy Class % of SKUs Contribution Mean € Min € Max €

High Discrepancy 1.83% 69.92% 94.30 -14485.75 14163.01

Middle Discrepancy 17.29% 25.08% -1.07 -365.21 357.12

Low Discrepancy 80.88% 5.00% -0.41 -19.87 19.87

- 15 -

SKUs Clustering based on the Discrepancy sign

Discrepancy is not

always negative. It is

not all about

Shrinkage

Positive discrepancy is

not negligible and

generally is caused by

Information Systems

manipulations and

errors

Positive or Negative

discrepancy: the

impact on sales is

the same

Discrepancy Sign

SKUs %

Discrepancy Mean €

Negative 29.93% -80.79Positive 24.44% 103.88

Zero 45.63% 0

Discrepancy Sign Class

SKUs %

Discrepancy Mean €

Discrepancy Min €

Discrepancy Max €

1 4.03% 555.59 66.9 14163.012 4.46% 39.97 23.07 66.34

3 4.91% 14.98 9.59 23.034 6.22% 6.09 3.48 9.575 51.88% 0.16 -1.28 3.47

6 8.49% -3.25 -5.52 -1.37 6.09% -8.31 -11.96 -5.538 5.21% -17.39 -25.06 -11.979 4.66% -39.38 -61.27 -25.07

10 4.05% -510.22 -14485.75 -61.44

- 16 -

Comparison Test vs. Control

The Turnover in the Test store is 5.22% higher than in the Control Store

6.11% 5.90%

-8.21%

-10.00%

-8.00%

-6.00%

-4.00%

-2.00%

0.00%

2.00%

4.00%

6.00%

8.00%

Fast Mover Middle Mover Slow Mover

Turnover comparison Test vs Control Store

Fast and Middle Mover

SKUs which account for

more than 86% of

discrepancies seem to

benefit from the stock

audit taking place in the

Test Store

- 17 -

Sales increase in the Test store, after the Audit, compared to the Control Store

4.91%

3.14%

2.52%

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

High Discrepancy Middle Discrepancy Low Discrepancy

Both positive and

negative discrepancy

classes benefit from

stock audit

3.61%

8.01%

2.56%

0.43%-0.22%

1.91% 1.71%

4.80%

10.42%

-0.54%

-2.00%

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

SignClass 1

SignClass 2

SignClass 3

SignClass 4

SignClass 5

SignClass 6

SignClass 7

SignClass 8

SignClass 9

SignClass 10

All Discrepancy classes

(High, Middle and Low)

benefit from the stock

audit with a higher

benefit for the “High

Discrepancy” Class

- 18 -

Experiment at Retailer BETA

4

weeks

1

week1

week

1

week1

week

1

week

1

week1

week

1

week1

week

1

week

Stock Audit

Computer and Counted Physical stock during the

audit are contrasted with what they should be

(last counted physical stock + stock input – stock

output)

Computer Discrepancy: what was found vs what isshown in the computer

before the audit

Physical Discrepancy: what was found vs what should be in the stock given last week audit andstock movements

- 19 -

SKUs Clustering: ABC classification based on Turnover

Fast Movers (14% of

SKUs) are generating

69.97% of turnover. They

also contribute to 48% of

Computer Discrepancies

and 46% of Physical

Discrepancies

46.14%

24.80%

29.05%

69.97%

25.02%

5.00%

48.65%

29.23%

22.12%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

Fast Mover Middle Mover Slow Mover

Turnover vs Discrepancy Contribution

Computer Discrepancy Contribution Turnover Contribution

Physical Discrepancy Contribution

54% of Fast Mover class

belongs to “High Physical

Discrepancy” class.

33% of Fast Mover class

belongs to “High

Computer Discrepancy”

class

- 20 -

Correlation stock movement-discrepancy

A Strong correlation between

stock movements (Input and

Output) and the Discrepancies

(Computer and Physical)

y = 0.4037x + 6.0082R² = 0.7622

0

5

10

15

20

25

30

35

40

( 10) 0 10 20 30 40 50 60

Average Physical Discrepancy as a function of the Stock Output

y = 0.4332x + 3.647R² = 0.8274

0

10

20

30

40

50

60

( 10) 0 10 20 30 40 50 60 70 80 90

Average Physical Discrepancy as a function of Stock Input

- 21 -

Exploration of shortage situations

Over the 5 weeks of the experiment, 40% of SKUs experienced at least once a zero

stock count during the weekly stock audit;

These 40% of SKUs experienced possibly at least once a shortage situation over the 5

weeks;

To evaluate whether a shortage occurred or not, we compare the sales for these SKUs

when the stock count is zero and when the latter is positive;

When the counted stock is zero during the stock audit, the EPOS are decreased by

2.86% in average. In term of sales £ turnover, this represents a loss of 3,69%;

12% of these SKUS belong to the Fast Mover class;

These 40% of SKUs which experienced at least once a shortage are subject to a

probability of 81% of Physical Discrepancy and a probability of 76% of Computer

Discrepancy.

- 22 -

Exploration of “Frozen SKUs” cases

Some SKUs faced a situation during some weeks where the computer stock record

was positive whereas the counted physical record showed a stock level equal to

zero. With a positive computer stock level, the replenishment process does not

trigger an order from suppliers leading to a physical stock level equal to zero in the

forthcoming week and consequently leading to lost sales.

Each week, approximatively 10% of SKUs faced this situation and it could be

verified that the EPOS for these SKUs is approximately equal to zero in the

forthcoming week.

6% of SKUs faced this situation each week and a big majority of them end up as

"frozen SKUs" without any EPOS signal during the 5 weeks experiment.

- 23 -

Exploration of the implication of hand adjustment of the stock

y = -0.5716x - 2.4757R² = 0.5254

y = -0.5368x - 0.9836R² = 0.6936

-12

-10

-8

-6

-4

-2

0

2

4

6

8

10

-12 -10 -8 -6 -4 -2 0 2 4 6 8

Discrepancy on Computer and Physical as a function of Hand Adjustment of the Stock

Average VarianceStore PI vs Count

Average VarianceExpected PIvs Count

Linear (Average VarianceStore PI vs Count)

Linear (Average VarianceExpected PIvs Count)

There is a strong

correlation between the

hand-made adjustment

quantity and the later-on

discovered

discrepancies

- 24 -

Next steps

Extending and finalizing the analysis for the rest of stores for Retailers Alpha

and Beta;

Completing the analysis for the rest of retailers;

Producing customized reports for each of the participating retailers;

Synthesizing the results in the form of a white paper to be hopefully useful for

the retailing industry (and sectors within it).