operational b i in supply chain planning

Operational business intelligence in supply chain planning

Solve the Insight Puzzle & See the Entire Picture !

Johan Blomme

Business Intelligence Manager, AMP

Operational business intelligence in supply chain planning

email

[email protected]

� Meeting the demand economy

� Trends in business intelligence

Agenda


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

� Meeting the demand economy

� 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


demand driven value chain

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 INFORMATIONMANAGEMENT INFORMATION BUSINESS OPERATIONSBUSINESS OPERATIONS


DATA DRIVENDATA DRIVEN PROCESS DRIVENPROCESS DRIVEN

TIME DELAYEDTIME DELAYED REAL TIMEREAL TIME

predictive

prospective, proactiveinformation delivery,actionable analytics

MANAGEMENT INFORMATIONMANAGEMENT INFORMATION BUSINESS OPERATIONSBUSINESS OPERATIONS


Buisnessvalue

What happened ?

query & reporting

OLAP

monitor

data mining

predictiveanalysis

What’s happening now ? What might happen ?

alertnotification(BAM)

restrospectiveinformationdelivery at

multiple levels

Business

reporting

•What has happened ?

•e.g. What is M.A.D. of forecasts for product X ?

•e.g. Why have out of stocks increased in week 20 ?

Responsive

•What’s happening now : Performance measurementand alert notification

•e.g. what is OOS % at the end of day 1 of sales


Responsive

analytics

•e.g. what is OOS % at the end of day 1 of salespromotion ?

Actionableanalytics

•What might happen : business process optimization

•e.g. SKU is going to be out of stock ; increasereplenishment frequency to prevent OOS

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

DATA DRIVENDATA DRIVEN PROCESS DRIVENPROCESS 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 delivers reporting

� the starting point is the business process in the center (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 DELAYEDTIME DELAYED REAL TIMEREAL TIME

� analysis happens after fact, using aggregated and detailed data (query driven)

� 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


1Fragmented and inefficient due to poor flow of information

Product Flow

Information Flow

demand Patterns (bullwhip effect !)

� 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.


– 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 demandis 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.


flow of information

Store

Ordering

processes

flow of goods

customerdirect

supplier VMI, automatic replenishment

monitor stock-levels

through real-time data

gathered at P.O.S.

log

isti

cs a

s a

ma

rke

tin

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oo

l

chemical

industry

machine

logistics as a marketing tool


log

isti

cs a

s a

ma

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tin

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logistics as a cost saving tool

machine

building

paper

industry

plant

constructions

electronics

automotive

logistics as a marketing 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


• 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

random sample of 2.553 from non-OOS combinations (36.415)

training sample (N = 5.106) - c=0.728



random sample of 2.553 from OOS-occurrences (5.106)


2.553 OOS-occurrences not in training sample

test sample (N = 5.106) - c=0.712

OOS

DATAPREDICTIVE

ANALYSISUNCERTAINTY OUTCOME


logistic regression

P.O.S.-features

sales history

sales velocity

Unit of analysis :

P.O.S. x MEDIA PRODUCT (WEEKLY MAGAZINE) AT PARTICULAR OSD IN 10-WEEK PERIOD

PRODUCT & P.O.S. CHARACTERISTICS

. expansive

. positive

. constant

. declining

CATP.O.S. development : evolution of P.O.S. turnover (2006 vs. preceeding years) ;

. < 500

. 500-1000

. > 1000

CATno. of titles in newsstand

1-25CATproduct id (25 media products)

Unit of analysis :

P.O.S. x MEDIA PRODUCT (WEEKLY MAGAZINE) AT PARTICULAR OSD IN 10-WEEK PERIOD

PRODUCT & P.O.S. CHARACTERISTICS

. expansive

. positive

. constant

. declining

CATP.O.S. development : evolution of P.O.S. turnover (2006 vs. preceeding years) ;

. < 500

. 500-1000

. > 1000

CATno. of titles in newsstand

1-25CATproduct id (25 media products)


SALES VELOCITY

SALES HISTORY

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

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

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

mean % unsolds in 10-week period before OSD

INTinventory history during 10 weeks preceeding media issue

# OOS incidences occurring in 10-week period before OSD

INThistory of OOS during 10 weeks preceeding media issue

. declining

. strongly declining

SALES VELOCITY

SALES HISTORY

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

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

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


INTinventory history during 10 weeks preceeding media issue


INThistory of OOS during 10 weeks preceeding media issue

. declining


. expansive

. positive

. < 500

. 500-1000 ®

. > 1000

media product 0,563***

0,588***

0,611***

0,754***

…

…

1,385***

1,447***

1,603***

1,749***

P.O.S. development

no. of titles

product id (25 products)

. expansive

. positive

. < 500

. 500-1000 ®

. > 1000

media product 0,563***

0,588***

0,611***

0,754***

…

…

1,385***

1,447***

1,603***

1,749***

P.O.S. development

no. of titles


Odds ratios for the risk of OOS : effect size of media products




. positive

. constant ®

. declining


inventory range of coverage

sales throughput

sales variance

inventory history

history of OOS



. positive

. constant ®

. declining



sales throughput

sales variance

inventory history

history of OOS

*** p<0.001

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


0,563***

0,588***

0,611***

0,754***

…

…

1,385***

1,447***

1,603***

1,749***

. < 500

. 500-1000 ®

. > 1000

media product

1,003

1,000

1,115*

0,571***

0,600***

0,678***

0,879***

…

…

1,301***

1,500***

1,588***

1,678***

no. of titles


0,563***

0,588***

0,611***

0,754***

…

…

1,385***

1,447***

1,603***

1,749***

. < 500

. 500-1000 ®

. > 1000

media product

1,003

1,000

1,115*

0,571***

0,600***

0,678***

0,879***

…

…

1,301***

1,500***

1,588***

1,678***

no. of titles


Odds ratios for the risk of OOS : effect size of media products,

no. of titles and P.O.S.-development


mean % unsolds in 10-week period

before OSD


. expansive

. positive

. constant ®

. declining


0,895*

0,966

1,000

1,062

1,038


sales throughput

sales variance

inventory history

history of OOS

P.O.S. development

mean % unsolds in 10-week period

before OSD


. expansive

. positive

. constant ®

. declining


0,895*

0,966

1,000

1,062

1,038


sales throughput

sales variance

inventory history

history of OOS

P.O.S. development

* p<0.05 *** p<0.001

0,895*

1,003

1,000

1,115*

0,571***

0,600***

0,678***

0,879***

…

…

1,301***

1,500***

1,588***

1,678***

0,563***

0,588***

0,611***

0,754***

…

…

1,385***

1,447***

1,603***

1,749***

. expansive

. < 500

. 500-1000 ®

. > 1000

media product

0,843*

0,998

1,000

1,015

0,622***

0,635***

0,712***

0,891***

…

…

1,400***

1,409***

1,550***

1,602***

P.O.S.

no. of titles

product id (25 media products)

0,895*

1,003

1,000

1,115*

0,571***

0,600***

0,678***

0,879***

…

…

1,301***

1,500***

1,588***

1,678***

0,563***

0,588***

0,611***

0,754***

…

…

1,385***

1,447***

1,603***

1,749***

. expansive

. < 500

. 500-1000 ®

. > 1000

media product

0,843*

0,998

1,000

1,015

0,622***

0,635***

0,712***

0,891***

…

…

1,400***

1,409***

1,550***

1,602***

P.O.S.

no. of titles

product id (25 media products)


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


0,895*

0,966

1,000

1,062

1,038



. expansive

. positive

. constant ®

. declining


0,988

1,127*

0,843*

0,920

1,000

1,034

1,012


sales throughput

sales variance

inventory history

history of OOS

P.O.S.

development

0,895*

0,966

1,000

1,062

1,038



. expansive

. positive

. constant ®

. declining


0,988

1,127*

0,843*

0,920

1,000

1,034

1,012


sales throughput

sales variance

inventory history

history of OOS

P.O.S.

development

* p<0.05 *** p<0.001

0,843*

0,920

0,998

1,000

1,015

0,622***

0,635***

0,712***

0,891**

…

…

1,400***

1,409***

1,550***

1,602***

0,895*

0,966

1,003

1,000

1,115*

0,571***

0,600***

0,678***

0,879***

…

…

1,301***

1,500***

1,588***

1,678***

0,563***

0,588***

0,611***

0,754***

…

…

1,385***

1,447***

1,603***

1,749***

. expansive

. positive

. < 500

. 500-1000 ®

. > 1000

media product

0,866*

0,985

1,005

1,000

1,010

0,890*

0,901

0,867*

0,850**

…

…

1,119*

1,246**

1,189**

1,164*

P.O.S. development

no. of titles

product (25 media products)

0,843*

0,920

0,998

1,000

1,015

0,622***

0,635***

0,712***

0,891**

…

…

1,400***

1,409***

1,550***

1,602***

0,895*

0,966

1,003

1,000

1,115*

0,571***

0,600***

0,678***

0,879***

…

…

1,301***

1,500***

1,588***

1,678***

0,563***

0,588***

0,611***

0,754***

…

…

1,385***

1,447***

1,603***

1,749***

. expansive

. positive

. < 500

. 500-1000 ®

. > 1000

media product

0,866*

0,985

1,005

1,000

1,010

0,890*

0,901

0,867*

0,850**

…

…

1,119*

1,246**

1,189**

1,164*

P.O.S. development

no. of titles

product (25 media products)


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


0,988

1,127*

0,920

1,000

1,034

1,012

0,966

1,000

1,062

1,038



. positive

. constant ®

. declining


0,846*

0,890*

1,229**

0,983

1,109

0,985

1,000

1,053

1,076


sales throughput

sales variance

inventory history

history of OOS

development

0,988

1,127*

0,920

1,000

1,034

1,012

0,966

1,000

1,062

1,038



. positive

. constant ®

. declining


0,846*

0,890*

1,229**

0,983

1,109

0,985

1,000

1,053

1,076


sales throughput

sales variance

inventory history

history of OOS

development

* 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


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) ;


(when should which qty be ordered) ;

– which results in greater in store availability and visibility of products ;

– to enhance the customer experience of shopping.

� 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 :

Recommendations for future analysis


• 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