Transforming Pharmaceutical Operational Performance with Supply Chain Traceability
Operational B I In Supply Chain Planning
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Transcript of 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
� Meeting the demand economy
� Trends in business intelligence
Agenda
� Trends in business intelligence
� Predicting out of stocks with real-time P.O.S.-data
� Meeting the demand economy
Copyright © IRI, 2005. Confidential and proprietary.
� 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
Copyright © IRI, 2005. Confidential and proprietary.
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 INFORMATIONMANAGEMENT INFORMATION BUSINESS OPERATIONSBUSINESS OPERATIONS
Copyright © IRI, 2005. Confidential and proprietary.
DATA DRIVENDATA DRIVEN PROCESS DRIVENPROCESS DRIVEN
TIME DELAYEDTIME DELAYED REAL TIMEREAL TIME
predictive
prospective, proactiveinformation delivery,actionable analytics
MANAGEMENT INFORMATIONMANAGEMENT INFORMATION BUSINESS OPERATIONSBUSINESS OPERATIONS
Copyright © IRI, 2005. Confidential and proprietary.
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
Copyright © IRI, 2005. Confidential and proprietary.
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
Copyright © IRI, 2005. Confidential and proprietary.
� 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
Copyright © IRI, 2005. Confidential and proprietary.
� 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
Copyright © IRI, 2005. Confidential and proprietary.
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
Copyright © IRI, 2005. Confidential and proprietary.
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.
Copyright © IRI, 2005. Confidential and proprietary.
– 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.
Copyright © IRI, 2005. Confidential and proprietary.
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
g t
oo
l
chemical
industry
machine
logistics as a marketing tool
Copyright © IRI, 2005. Confidential and proprietary.
log
isti
cs a
s a
ma
rke
tin
g t
oo
l
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)
Copyright © IRI, 2005. Confidential and proprietary.
?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
Copyright © IRI, 2005. Confidential and proprietary.
Visualisation of sales velocity for weekly titles (source : AMP-Distrishop)
Copyright © IRI, 2005. Confidential and proprietary.
Visualisation of sales velocity for weekly titles (source : AMP-Distrishop)
Copyright © IRI, 2005. Confidential and proprietary.
� 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
Copyright © IRI, 2005. Confidential and proprietary.
• 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
Copyright © IRI, 2005. Confidential and proprietary.
� 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
Copyright © IRI, 2005. Confidential and proprietary.
random sample of 2.553 from non-OOS combinations (36.415)
random sample of 2.553 from OOS-occurrences (5.106)
random sample of 2.553 from non-OOS combinations (36.415)
2.553 OOS-occurrences not in training sample
test sample (N = 5.106) - c=0.712
OOS
DATAPREDICTIVE
ANALYSISUNCERTAINTY OUTCOME
Copyright © IRI, 2005. Confidential and proprietary.
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)
Copyright © IRI, 2005. Confidential and proprietary.
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)
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
. 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
product id (25 products)
Odds ratios for the risk of OOS : effect size of media products
Copyright © IRI, 2005. Confidential and proprietary.
mean % unsolds in 10-week period before OSD
# OOS incidences occurring in 10-week period before OSD
. positive
. constant ®
. declining
. strongly declining
inventory range of coverage
sales throughput
sales variance
inventory history
history of OOS
mean % unsolds in 10-week period before OSD
# OOS incidences occurring in 10-week period before OSD
. positive
. constant ®
. declining
. strongly declining
inventory range of coverage
sales throughput
sales variance
inventory history
history of OOS
*** p<0.001
Confidence intervals (95 %) for odds ratios of media products
Copyright © IRI, 2005. Confidential and proprietary.
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
product id (25 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
product id (25 products)
Odds ratios for the risk of OOS : effect size of media products,
no. of titles and P.O.S.-development
Copyright © IRI, 2005. Confidential and proprietary.
mean % unsolds in 10-week period
before OSD
# OOS incidences occurring in 10-week period before OSD
. expansive
. positive
. constant ®
. declining
. strongly declining
0,895*
0,966
1,000
1,062
1,038
inventory range of coverage
sales throughput
sales variance
inventory history
history of OOS
P.O.S. development
mean % unsolds in 10-week period
before OSD
# OOS incidences occurring in 10-week period before OSD
. expansive
. positive
. constant ®
. declining
. strongly declining
0,895*
0,966
1,000
1,062
1,038
inventory range of coverage
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)
Odds ratios for the risk of OOS : effect size of media products,
no. of titles, P.O.S.-development and sales history
Copyright © IRI, 2005. Confidential and proprietary.
0,895*
0,966
1,000
1,062
1,038
mean % unsolds in 10-week period before OSD
# OOS incidences occurring in 10-week period before OSD
. expansive
. positive
. constant ®
. declining
. strongly declining
0,988
1,127*
0,843*
0,920
1,000
1,034
1,012
inventory range of coverage
sales throughput
sales variance
inventory history
history of OOS
P.O.S.
development
0,895*
0,966
1,000
1,062
1,038
mean % unsolds in 10-week period before OSD
# OOS incidences occurring in 10-week period before OSD
. expansive
. positive
. constant ®
. declining
. strongly declining
0,988
1,127*
0,843*
0,920
1,000
1,034
1,012
inventory range of coverage
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)
Odds ratios for the risk of OOS : effect size of media products,
no. of titles, P.O.S.-development, sales history and sales velocity
Copyright © IRI, 2005. Confidential and proprietary.
0,988
1,127*
0,920
1,000
1,034
1,012
0,966
1,000
1,062
1,038
mean % unsolds in 10-week period before OSD
# OOS incidences occurring in 10-week period before OSD
. positive
. constant ®
. declining
. strongly declining
0,846*
0,890*
1,229**
0,983
1,109
0,985
1,000
1,053
1,076
inventory range of coverage
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
mean % unsolds in 10-week period before OSD
# OOS incidences occurring in 10-week period before OSD
. positive
. constant ®
. declining
. strongly declining
0,846*
0,890*
1,229**
0,983
1,109
0,985
1,000
1,053
1,076
inventory range of coverage
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
Copyright © IRI, 2005. Confidential and proprietary.
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) ;
Copyright © IRI, 2005. Confidential and proprietary.
(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
Copyright © IRI, 2005. Confidential and proprietary.
• 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