Operational B I In Supply Chain Planning

Solve the Insight Puzzle & See the Entire Picture !

Operational business intelligence in supply chain planning

Johan Blomme
Business Intelligence Manager, AMP

email
johan.blomme@ampnet.be

Agenda

Meeting the demand economy
Trends in business intelligence
Predicting out of stocks with real-time P.O.S.-data

The future value chain :
– capturing of demand signals to estimate true customer demand
– real-time visibility and information sharing with partners

demand driven value chain


Trends in business intelligence

BI has evolved from its primary purpose of ad hoc query and analysis on a static store of
historical information to analyzing transaction data in (near) real-time.

MANAGEMENT INFORMATION BUSINESS OPERATIONS

DATA DRIVEN PROCESS DRIVEN

TIME DELAYED REAL TIME


MANAGEMENT INFORMATION BUSINESS OPERATIONS

prospective, proactive
information delivery,
actionable analytics

alert predictive
notification analysis
Buisness value

data mining
(BAM)

restrospective
information
monitor
delivery at
multiple levels
OLAP

query &
reporting

What happened ? What’s happening now ? What might happen ?


•What has happened ?
•e.g. What is M.A.D. of forecasts for product X ?
Business
•e.g. Why have out of stocks increased in week 20 ?
reporting

•What’s happening now : Performance measurement
and alert notification
Responsive •e.g. what is OOS % at the end of day 1 of sales
analytics promotion ?

•What might happen : business process optimization
•e.g. SKU is going to be out of stock ; increase
Actionable replenishment frequency to prevent OOS
analytics


DATA DRIVEN PROCESS DRIVEN

The emphasis is not on the data itself, but on the business processes that generate
the data.

« Business intelligence is moving into the context of the business process, not just to
make users’ information experience more effective, but also to allow for business
process optimization » .

Software Macro-Trends : Reshaping Enterprise Software – Sep. 2005


a data store is fed by operational systems and then the starting point is the business process in the center
delivers reporting (the data and the reporting are determined by the
process)
the flow of information is two-way : from business
processes to analytics and from analytics to business
processes (closed-loop approach)
operational and analytical processes are converging


TIME DELAYED REAL TIME

analysis happens after fact, using aggregated and analysis of detailed data while event is occurring
detailed data (query driven)
events are interpreted in real-time :
– monitor
– interpret
– predict


Predicting out of stocks with real-time P.O.S.-data

Publisher Distributor Newsstand

Product Flow demand Patterns (bullwhip effect !)

Information Flow

Fragmented and inefficient due to poor flow of information
1


The publishing supply chain is partly inefficient due to a lack of visibility of day-to-day
demand and stock positions.

Return rates of 60 % and more are not uncommon in the publishing industry.

While excess inventory leads to waste, at the same time retailers are often faced with
the problem of out of stocks :
– it is estimated that out of stocks cause lost sales of about 3-4 % ;
– most OOS-problems are caused inside the store.

Finding a balance between inventory and service levels will continue to grow as the
numer of SKU’s continues to grow (niche marketing), in combination with seasonal
effects, frequent promotional activities, etc.

To minimize inventory and improve product availability, a better view of real demand
is necessary.


Managing the replenishment process can increase visibility in the supply chain.

Generate operational improvements from downstream retail (P.O.S.)-data to reduce
out of stocks and improve sales.

monitor stock-levels
through real-time data
gathered at P.O.S.

flow of information
Store
direct
Ordering customer
supplier VMI, automatic replenishment
processes

flow of goods


logistics as a marketing tool

logistics as a marketing tool
chemical
industry

machine
building

paper
industry

plant
constructions automotive

electronics

logistics as a cost saving tool
logistics as a cost saving tool


In order to develop replenishment models, we need evidence
about the relationship between performance variables (e.g. inventory levels, out of stock)
and contextual variables (e.g. store and product characteristics)

?
What is the power of P.O.S. real-time data
to predict out of stock ?


AMP-Distrishop : daily P.O.S.-data from major retailers


Visualisation of sales velocity for weekly titles (source : AMP-Distrishop)


Product velocity is the key :
– the faster moving the item, the bigger the impact on the business (e.g. negative
consumer reactions) ;
– the focus needs to be on the fastest moving items.

Test :
– weekly magazines (392) ;
– selection of 25 titles :
• fast moving items
• P.O.S.-coverage : distributed in at least 1.000 P.O.S.
• minimum circulation order : 10.000 copies
– measurement of sales velocity for each item in each store during a 10-week
period (april-june 2007) ;
– Distrishop-P.O.S. (413) : selection of 284 newsstands (413 -> 284 : due to
validity control of real-time data).


A relatively small number of media products constitutes the majority of newsstand sales


Total sample (combination P.O.S./#weeks/#media products) = 41.521

« balanced » samples (based on incidence of OOS, 12.3%):
– training
– test

POS/ #weeks / # media products 41.521
% OOS (12,3 %) 5.106

training sample (N = 5.106) - c=0.728
random sample of 2.553 from non-OOS combinations (36.415)
random sample of 2.553 from OOS-occurrences (5.106)

test sample (N = 5.106) - c=0.712
random sample of 2.553 from non-OOS combinations (36.415)
2.553 OOS-occurrences not in training sample


PREDICTIVE
DATA UNCERTAINTY OUTCOME
ANALYSIS

OOS

P.O.S.-features
sales history
sales velocity

logistic regression


Unit of analysis :
P.O.S. x MEDIA PRODUCT (WEEKLY MAGAZINE) AT PARTICULAR OSD IN 10-WEEK PERIOD
PRODUCT & P.O.S. CHARACTERISTICS

product id (25 media products) CAT 1-25

no. of titles in newsstand CAT . < 500
. 500-1000
. > 1000
P.O.S. development : evolution of P.O.S. turnover (2006 vs. preceeding CAT . expansive
years) ; . positive
. constant
. declining
. strongly declining
SALES HISTORY

history of OOS during 10 weeks preceeding media issue INT # OOS incidences occurring in 10-week
period before OSD

inventory history during 10 weeks preceeding media issue INT mean % unsolds in 10-week period
before OSD

SALES VELOCITY

sales variance : sales coefficient of variance (calculated by dividing the INT scale value from 1 to 8
standard deviation of sales in a 7 day-period by mean sales value)

sales throughput: mean sales in a 7 day-period INT scale value from 1 to 8

inventory range of coverage : relative measure of inventory level, INT scale value from 1 to 8
calculated as the absolute inventory divided by mean sales


Odds ratios for the risk of OOS : effect size of media products
product id (25 products) media product 0,563***
0,588***
0,611***
0,754***
…
…
1,385***
1,447***
1,603***
1,749***
no. of titles . < 500
. 500-1000 ®
. > 1000
P.O.S. development . expansive
. positive
. constant ®
. declining
. strongly declining
history of OOS # OOS incidences occurring in 10-week
period before OSD

inventory history mean % unsolds in 10-week period
before OSD

sales variance

sales throughput

inventory range of coverage

*** p<0.001


Confidence intervals (95 %) for odds ratios of media products


Odds ratios for the risk of OOS : effect size of media products,
no. of titles and P.O.S.-development
product id (25 media product 0,563*** 0,571***
products) 0,588*** 0,600***
0,611*** 0,678***
0,754*** 0,879***
… …
… …
1,385*** 1,301***
1,447*** 1,500***
1,603*** 1,588***
1,749*** 1,678***

no. of titles . < 500 1,003
. 500-1000 ® 1,000
. > 1000 1,115*
P.O.S. . expansive 0,895*
development . positive 0,966
. constant ® 1,000
. declining 1,062
. strongly declining 1,038
history of OOS # OOS incidences occurring in 10-week
period before OSD

inventory history mean % unsolds in 10-week period
before OSD

sales variance

sales throughput

inventory range
of coverage

* p<0.05 *** p<0.001


no. of titles, P.O.S.-development and sales history
product id (25 media product 0,563*** 0,571*** 0,622***
media products) 0,588*** 0,600*** 0,635***
0,611*** 0,678*** 0,712***
0,754*** 0,879*** 0,891***
… … …
… … …
1,385*** 1,301*** 1,400***
1,447*** 1,500*** 1,409***
1,603*** 1,588*** 1,550***
1,749*** 1,678*** 1,602***

no. of titles . < 500 1,003 0,998
. 500-1000 ® 1,000 1,000
. > 1000 1,115* 1,015
P.O.S. . expansive 0,895* 0,843*
development . positive 0,966 0,920
. constant ® 1,000 1,000
. declining 1,062 1,034
. strongly declining 1,038 1,012
history of OOS # OOS incidences occurring in 10-week 1,127*
period before OSD

inventory history mean % unsolds in 10-week period 0,988
before OSD

sales variance

sales throughput

inventory range
of coverage

* p<0.05 *** p<0.001


no. of titles, P.O.S.-development, sales history and sales velocity

product (25 media product 0,563*** 0,571*** 0,622*** 0,890*
media products) 0,588*** 0,600*** 0,635*** 0,901
0,611*** 0,678*** 0,712*** 0,867*
0,754*** 0,879*** 0,891** 0,850**
… … … …
… … … …
1,385*** 1,301*** 1,400*** 1,119*
1,447*** 1,500*** 1,409*** 1,246**
1,603*** 1,588*** 1,550*** 1,189**
1,749*** 1,678*** 1,602*** 1,164*

no. of titles . < 500 1,003 0,998 1,005
. 500-1000 ® 1,000 1,000 1,000
. > 1000 1,115* 1,015 1,010
P.O.S. . expansive 0,895* 0,843* 0,866*
development . positive 0,966 0,920 0,985
. constant ® 1,000 1,000 1,000
. declining 1,062 1,034 1,053
. strongly declining 1,038 1,012 1,076
history of OOS # OOS incidences occurring in 10-week 1,127* 1,109
period before OSD

inventory history mean % unsolds in 10-week period 0,988 0,983
before OSD

sales variance 1,229**

sales throughput 0,890*

inventory range 0,846*
of coverage

* p<0.05 ** p< 0.01 *** p<0.001


Model fitted : c= 0.712
The c-statistic represents the proportion of pairs with different observed outcomes (no OOS / OOS) for which the model
correctly predicts a higher probability for observations with the event outcome (OOS) than the probability for nonevent
observations. For the present model, the value of the c-statistic means that 71,2 % of all possible pairs – one with no
OOS and one with OOS – the model correctly assigned a higher probability to the cases in which OOS occurred.

The c-statistic provides a basis for comparing different models fitted to the same data : for a model without sales
velocity-variables the c-statistic is 0,627.

While the incidence of OOS is strongly influenced by media product characteristics,
the introduction of sales velocity
– reduces the effect of media product ;
– independent of all other features, out of stock-occurrences vary significantly by
sales velocity :
• e.g. sales variance : the odds ratio of 1,229 may seem relatively small ; however if the
effect size of sales variance is transformed to a probability, it means that with a one unit
increase in sales variance the 00S-probability increases with 2,4 % ; at the highest level
of sales variance, the probability of out of stock increases with 16,8%.


Conclusion

Product sales velocity has an influence on OOS, implicating that real time
visibility of sales at item level to monitor changes in sales velocity makes it
possible to improve in store operations.

Real-time P.O.S.-data is therefore a driver for actionable analytics and
business process optimalization :
– to report and to alert on out of stocks as they happen ;
– guide the replenishment process, based on true customer demand
(when should which qty be ordered) ;
– which results in greater in store availability and visibility of products ;
– to enhance the customer experience of shopping.


Recommendations for future analysis

data accuracy

operationalization of products characteristics (e.g. promotional events)

further examination of store characteristics, e.g. SKU-density

development of forecast models based on history data and real-time data :

• setup of rules-driven stock management decisions : detection of regular cycles (normal
performance varies by hour of the day, day of the week, …) and exceptions on regular
cycles
• setup of individual (P.O.S.-) profiles : an increase in the velocity of sales may trigger an
alert for a P.O.S., but not for a different newsstand


Operational B I In Supply Chain Planning

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