1) The document discusses the results of a survey on inventory management practices in retail. Most respondents order goods based on the latest weeks' sales and do not use detailed forecasting methods. 2) The survey found that retailers have high out-of-stock levels and customers often do not purchase items or switch to competitors when out-of-stocks occur. Poor ordering and replenishment processes were a top cause of out-of-stocks. 3) Time series analysis methods that incorporate factors like seasonality, price changes, and promotions can help retailers more accurately forecast demand and optimize inventory levels. This leads to lower costs and higher profits.

1. Smart Inventory Management
in Retail
A new look at familiar demand phenomena from
forecasting perspective
Alexey Ivasyuk, De Novo© 2012
www.de-novo.biz

2. Some Results Of Our Survey
• How do you run the process of ordering goods to stores?
– This is done by a store personnel… 60%
– Ordering process done by the centralized replenishment team 30%...
– Automated ordering procedure, with manual but centralized ordering
process for seasonal, new and promotional goods <10%
– Why does it matter? 1%
• Do you have a clear ordering and delivery calendar for each
supplier in your IT-system?
– The calendar is stated in agreements and is known to people who order
goods … 60%
– We maintain shipment terms in the system and refer to the terms for
information purpose when ordering <30%
– We have a clear ordering and delivery calendar and use it in the
automated ordering procedure <10%.
2

3. Some Results Of Our Survey
• How many days/weeks of stock do you usually order for the
goods delivered daily, weekly and long-term supply
– It depends… 60%
– We order 2-3 delivery cycles plus redundant… 30%
– We have an automated ordering procedure which uses historical
sales, promotional and delivery factors multiplied by the delivery
cycle plus… <10%
• How many stock weeks do you have for your TOP-items?
– 2-3 weeks of stock….60%
• How many stock weeks/months do you have in total?
– 2-3 months of stock… 60%
– Oh, you’d better not to know this… 1%
3

4. Do You Have The Best Stock Levels You
Could Have?
17.7
Average out-of-stock levels (%)
8.3 8.6
7.9
All EU North America Russia, CIS*
Source: ECR * Including our observations
90% of our respondents answered «No»4

5. Customer Reaction to Stockouts (%)
Source: ECR
Doesn't buy
9
anything
Buys a different size 16
Total losses:
Revisits the shop
later
17 47%!!!
Buys the same item
21
in a competitor shop
Buys a different
37
brand
Most of the retailers order redundant stock just to avoid the
5
losses

6. Top Root Causes of Stockouts (%)
All Countries
30%
59%
11%
Others Shelf replenishment Ordering and replenishment issues
Source: ECR Europe
6

7. An Open Secret:
Retail Store Ordering Formula
• Order volume = [Demand forecast] * [Influencing factors] *
[Delivery Cycle] + [Safety Stock] + [Presentation Stock] –
[Current stock]
• Safety Stock = Forecasting Error (MAD/RMSE) multiplied by
the delivery cycle period
The best forecast we know is about 10-15% of error, but:
The more precise the forecast, the more prone it is to error
30% of a forecast error means a good forecast
50% of a forecast error is still acceptable in FMCG because it requires
you to keep just 12 days of stock with weekly delivery cycle
Make a guess what is the forecasting error for the survey
respondents? 7

8. How the survey respondents predict the
demand?
• Orders are done based on the latest weeks
• Seasonal orders are done based on a previous season
– no consideration of assortment changes
60% • No detailed review at the past promotions while
ordering for the new promotions
• Out-of-stocks are not estimated
• Centralized ordering using moving average forecast
• Seasonal, new items, and promo orders are done
30% manually based on analytics and excel sheets
• Out-of-stocks are estimated based on the last sales
• Several forecasting methods are in use
• Seasonal patterns are calculated and verified
?% • Promotional performance is estimated and used for
future promotions
• Out-of-stocks are estimated based on forecast
8

9. Choosing a Forecasting Approach
What do mathematicians say
Time Frame Forecast Horizon Best Approach
Short Up to 19 months Time Series Analysis - methods are
based on the premise that you can predict
the future performance by analyzing the
past behavior
Medium 6-36 months Casual Analysis – uses the forecast for
several independent factors to predict a
dependant measure
Long 19 months – 5 years Expert Opinion - As the time horizon for
the forecast moves further out into the
future, expert opinion becomes the most
reliable predictor
Longer-range forecasts should generate data at higher levels to
offset the increasing likelihood of error 9

10. What Time Series Analysis is
Actual Sales
Holt-Winters Method
(takes the seasonality into account)
10

11. What Time Series Analysis is
Sales
Double Exponential Smoothing
11

12. What Time Series Analysis is
Sales
Single Exponential Smoothing
12

13. Demand Influences
• Christmas
Sales
•Promotion
• Unknown • Unknown
•Advertisement
•Price decrease
• Unknown
•Competitor
•Cross-elasticity raises price
Seasonality
Influencing
Events
factors
Sales Time
• Out-of-stock
Clean sales 13
Baseline forecast Exceptions

14. Forecasting and Category Management
Forecast at an item-group level
Consolidated forecast at SKU-level
A common case with assortment
• Group-level forecast shows upward trend
• Consolidated SKU-level forecast shows downward trend
What could it mean?
Got to review the SKU range! Demand forecasting is a baseline
for the category management and assortment planning 14

15. Promotional Activity Example
Promo-sales
Seasonal-sales
Baseline forecast
Promo period
Question:
• How to estimate the promotional impact?
• How to split a promotional sales uplift and seasonal demand?
• How to do all of this, if promotional activity took place for 500 SKUs
in 100 stores?
• How to re-use the experience in the future?
Tracking the forecast vs. actual sales will allow to do it regularly
15
in a proper manner

16. Price Elasticity of Demand
Normal case Cross-elasticity
• Price elasticities are almost always negative except for a few types
of goods such luxury goods
• Unclean sales history is not always telling this
16

17. Price Elasticity of Demand
SKU Sales
SKU Forecast
Competitor We We raised
raised raised prices
prices a price for for another SKUs
some SKUs in the same range
Optimal price
• Analyzing the forecast vs. actual
What if the increase?
sales is a basis for understanding
Demand
$4.99 price elasticity
855 pcs.
• The elasticity can be considered at
$5.59
550 pcs. item-group level as well as at SKU
level
17
Price

18. Common Misconceptions
«Complicated forecasts cannot be verified»
«Need to hire highly qualified analysts in order to do forecast»
«Users will never understand it – they will just have to accept it as is»
«Forecasts should be directly generated at the lowest level of
execution»
«Time Series Forecast is not suitable for such industries as
fashion, boutiques and jewellery»
«Our sales are so heavily dependent on unpredictable factors that
automated forecast will never help us»
18

19. Thank You!
Alexey Ivasyuk
+38 (044) 200-93-39
alexey.ivasyuk@de-novo-biz
There are huge opportunities to minimize costs and maximize profits if we
know what tomorrow will bring - but we don't!
Therefore we forecast!