This document summarizes a presentation on management by exception (MBE) and predictive analytics. It discusses how MBE can automate the identification of exceptions based on key performance indicators, guard rails, and business rules. Predictive analytics can then leverage multiple data sources to forecast trends and apply MBE workflows. Examples of MBEs are provided for trade promotion management, retail execution, warehouse management, and ERP to improve productivity, reduce out-of-stocks, and maintain margins. MBE enhances analytics by enabling automated exception handling and freeing up user time for higher-value work.

1. Management by Exception,
Predictive Analytics
& AFS G2
2016 AFS User Conference

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Ajay Chidrawar,
SVP Global Customer Success & GM Analytics BU
Ajay has 15+ years of experience successfully building and deploying
planning, execution and analytics software in a number of industries and
customers globally including CPG, Retail, Pharma, HiTech, Automotive
and Industrial.
Michael Scott, VP Product Management & Development –
AFS G2
Michael has over 25 years of experience in CPG, manufacturing, process
improvement and software development. He studied engineering and
management at the Georgia Institute of Technology.
Speaker(s)

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Agenda
• Current environment
• Challenges
• Management by Exception (MBE)
• MBE in Action
• Predictive Analytics

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The Current Environment
• Business challenges
• Rapidly evolving & expanding business – products, channels, partners
• Companies under pressure – margins, market share, revenue
• Evolving competitive landscape
• User challenges
• Do more with less – people, resources, infrastructure
• Higher expectations on rapid response
• Technology/Data challenges
• Volume and speed of data
• Difficult to sift through all the data

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How Do Companies Go About
This Today
• Standard Reports
• Looking into the rear view mirror
• Time consuming to find issues (hunt and peck)
• Too late by the time the problems are identified
• Data refresh frequency monthly or weekly
• Post facto analysis, unable to fix today’s problems
• Information leveraged by limited few users with analytical skills

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Where Do Most Companies Stand
in The Analytics Journey
Manual

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Management by Exception
Root Cause
AnalysisRoot Cause
Analysis
Review Root
Cause Analysis
Root Cause
AnalysisRoot Cause
Analysis
Recommended
Actions
KPIs
Guard Rails
Business Rules
Frequency
Levels of sophistication, increasing value
Exceptions by
email, dashboard
TPM
Retail
TPM FS RE-DSD WMS ERP
Root Cause
AnalysisRoot Cause
Analysis
Automatic
Resolutions

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TPM Retail – Trade Marketing
Promotion Compliance by Chain
Analysis:
Compliance point 3 was
the issue – products not
merchandised properly
Recommendation:
- Target visit for
Merchandiser to ensure
displays are properly
stocked in Chain X
- Rep to visit and
recheck Promotion
Compliance OR
Escalation to NAM for
Chain X to decide what
to pay/not to pay for
the Promotion.
KPIs: Promo Compliance
Guard Rails: <30%
Business Rules:
If Promo Compliance < 30%, Alert
Trade Promo Mgr
Frequency: Daily
Exceptions
by email,
dashboard
TPM FS RE WMS ERP
TPM
Retail

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Retail Execution – Sales Mgr/Dir
Distribution Gain/Loss by Brand
Analysis:
Analyse segment
Activity for Competitor
Brands eg. Shelf Share
Gains & Losses
View OOS by Chain
View SKU replenishment
volumes
Recommendation:
- Target Task to Increase
Shelf Share or
Secondary Placements
- Target Monitoring to
address OOS within
Chain
- Create a Visit for Order
or Escalate to Supply
Chain
KPIs: Distribution Loss
Guard Rails: >5%
Business Rules:
If DistributionLoss >5%, Alert Mgr
Frequency: Daily
Exceptions
by email,
dashboard
TPM
Retail
TPM FS WMS ERPRE-DSD

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WMS – Labor Performance
Case Picks Per Hour
Analysis:
Review the items being
loaded in the warehouse
to determine why case
pick
Recommendation:
Move incoming items
close to associated
items to cut loading
times
KPIs: Case Pick/Hr
Guard Rails: <80
Business Rules:
If CasePick/Hr <80, Alert WhseMgr
Frequency: Data update
Exceptions
by email,
dashboard
TPM
Retail
TPM FS RE-DSD WMS ERP

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ERP – Sales Margin %
for High Margin Products
Analysis:
Review the orders for
the product that has the
exception  One banner
was priced lower than
approved on the product
Recommendation:
Notify the rep(s) and
inform them of lowest
price approved for
that product in order
to maintain margin %
KPIs: Margin % by Prod
Guard Rails: <50%
Business Rules:
If CurMth Margin%<50%, Alert Mgr
Frequency: Data update
Exceptions
by email,
dashboard
TPM
Retail
TPM FS RE WMS ERP

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Predictive Analytics
• Leverage data from many sources –
Core Applications, POS, real time
data collected by users, partner data,
competitive data
• Forecast Trends based on available
data
• Apply MBE workflows to the KPI
Trends
Predictive
Analytics
WMS
ERP
TPM
DSD
RE
POS
Partner
Data
Comp
Data

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Predictive Analytics
Examples• TPM
• Predict if a promotion will achieve the required lift using partial sales numbers (up to date)
• Predict if a promotion will exceed expectations (could cause other issues OOS, etc.)
• RE/DSD
• Predict if reps will maintain call coverage based on assigned tasks based on previous task
performance
• Predict optimal stock levels for perishables to prevent spoilage and OOS (longer term
perishables)
• WMS
• Predict if warehouse will achieve today’s shipments based on shipment/warehouse type (if
linear, many are not)
• Predict space utilization issues by looking at the incoming vs outgoing items
• ERP
• Predict OOS situation for fast moving products
• Predict if sales trend will move outside of target growth projections

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MBE Enhances Value
From Analytics
• MBE allows for automated exception
handling
• Prepackaged MBEs – benefit from day 1
• Configure new MBEs quickly as business
changes
• Free up users’ time for real value add
activities
• Sense and respond at the speed of data
• Leverage volume of data to find the right
exceptions
• Institutionalize the techniques of your best
users

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Thank you
Q & A
Next Session: AFS G2 Customer Spotlight in the Consumer
Goods Industry (5:00pm)

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Management by Exception
Review Root Cause
Analysis
KPI:
Guard Rails:
Business Rules:
Frequency:
Levels of sophistication, increasing value
Exceptions
by email
TPM
Retail
TPM FS RE WMS ERP
Recommended
Actions
Automatic
Resolution / Action

17. ©2016 AFSTechnologies, Inc., Proprietary and Confidential
Management by Exception
Root Cause Analysis
KPI:
Guard Rails:
Business Rules:
Frequency:
Levels of sophistication, increasing value
Exceptions
by email
TPM
Retail
TPM FS RE WMS ERP
Recommended
Actions
Automatic
Resolution / Action

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Management by Exception
DATA
from
TPM,
ERP,
WMS,
RE,
DSD
Root Cause
AnalysisRoot Cause
Analysis
Root Cause
Analysis
Root Cause
AnalysisRoot Cause
Analysis
Recommended
Actions
Metrics/KPIs
Guard Rails
Business Rules
Frequency
Automatic
resolutions
Levels of sophistication and increasing value
Exceptions
by email /
dashboards

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Scenario ID WMS35
Exception Trigger/Rule If case picks per hour fall below 80, alert whse mgr
Alert Type
Email
Dashboard Alert
Analysis Review the items being loaded in the warehouse to determine why case pick
Conclusion
Items that are common to the same orders are positioned at different ends of the warehouse causing multiple
trips and slowing the case rates
Response Action Resolved to move incoming items close to associated items to cut loading times
Business Value
What business value does
having this MBE add?
Small items like case picks per hour can add up to big savings over time. If the discrepancies are identified early,
they can be quickly and easily correcting and bring the case rate back up.
Value Impact - Increased labor productivity and better scheduling / wms velocity capabilities
Dataset Real Time Dashboard - Today
Solution Tab Labor Performance
Mode Grid
Filter None
Hierarchy Employee
Period Today (Default)
Column(s) (New Formula) Case Picks (hr) = {Case_Picked}/({Case_Picked_Min}/60) (From Labour_Perf_CasePicks Dashboard)
Numeric Condition Case Picks (hr)< 80
Execution Each time data is imported
WMS – Labor Performance
Case Picks Per Hour

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Report Name ERP26
Exception Trigger/Rule If the Margin % for the current calendar month falls below 50% for the product selected, then trigger
Alert Type
Dashboard Alert
Email
Analysis
Review the orders for the product that has the exception to see the margin % for this month to see what is
making
Conclusion One banner was given a lower than approved price on this product.
Response Action Notify the rep(s) and inform them of the lowest price approved for that product in order to maintain margin %
Business Value
What business value does
having this MBE add?
Keeps the margins at a planned level and identifies any variances in time for them to be quickly corrected.
Value Impact - Gross Margin
Dataset Default (only 1)
Solution Tab Sales
Mode Grid
Filter Product=Select High Margin Products
Hierarchy Product
Period ?
Column(s) (NEW Forumla) Margin % TY = (Margin Dollars TY)/(Sales Dollars TY)
Numeric Condition Margin % TY > ? Or Margin % TY < ?
Execution Each time data is imported
ERP – Sales
Margin % for High Margin Products

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Scenario ID RE1
Report Name Distribution Gain/Maintain/Loss by Brand
Exception Trigger/Rule Loss of Distribution is => 5%
Alert Type
Email
Dashboard Alert
Analysis
Analyse segment Activity for Competitor Brands eg. Shelf Share Gains & Losses
View OOS by Chain
View SKU replenishment volumes
Conclusion 0
Response Action
Target Task to Increase Shelf Share or Secondary Placements [Web Console]
Target Monitoring to address OOS within Chain [Web Console]
Create a Visit for Order or Escalate to Supply Chain
Business Value
What business value does
having this MBE add?
Value Impact - Increase share of shelf, secondary placements and reduced out of stocks - resulting in sales increase
and/or decreased sales losses. Protect brandshare and revenues with Agile, Rapid response to in-store conditions.
Dataset Main
Solution Tab Tasks
Mode Grid
Filter
Hierarchy Brand
Period Week
Column(s) Distribution % Change
Numeric Condition Distribution % Change > -5
RE – Sales Mgr/Dir
Distribution Gain/Loss by Brand

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Report Name Promotion Compliance by Chain
Exception Trigger/Rule Compliance is below 30%
Alert Type Email
Analysis **Discuss with TPM about how this is calculated & where
Conclusion Compliance point 3 was the issue – products not merchandised properly
Response Action
1. Target visit for Merchandiser to ensure displays are properly stocked in Chain X
2. Rep to visit and recheck Promotion Compliance OR Escalation to NAM for Chain X to decide what to pay/not to
pay for the Promotion.
Business Value
What business value does
having this MBE add?
a) Good promotion execution supported which ensures sales uplift. B) Clear picture of promo compliance in
relation to costs, with a clear picture with which to negotiate with retailers.
Value Impact - Higher return from Trade Spend Investment
Dataset Either
Solution Tab Promotion
Mode Grid
Filter
Hierarchy Chain/Banner
Period YTD
Column(s) Promotion Compliance Overall %
Numeric Condition Promotion Compliance Overall % < 30
Execution
TPM Retail – Trade Marketing
Promotion Compliance by Chain