Transcript External Transformation Deck

Supply Chain Planning
Demand Planning Excellence –
Automotive Industry
May 2013
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Demand Planning Process Example
Planning Complexity for a Typical Mid-Market Company
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More than 10,000 items to review on a monthly and/or weekly rolling planning horizon
Volatile and complex planning environment
Difficulty to hire and retain skilled specialists in supply chain or operations management
Often only one resource to manage the total puzzle
IT systems not connected and not integrated
Functions are not connected, centralized or integrated
Few or limited resources to invest on advanced IT systems
ERP system lack of functionalities to support decision making process
Extensive usage of Excel spreadsheets
Lack of operational performance measures (KPIs)
Limited business intelligence capabilities
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Demand Planning Process Example
Automotive Industry
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Asia
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Industry : manufacturer and distributor of
automotive parts (OEM and aftermarket)
Number of active items (skus)
  8000
Required service level (fill rate) : >= 96 %
17 warehouses located in North America
(Canada and USA)
3 manufacturing plants located in Canada
Usage of domestic and offshore sources (Asia)
15 major national client accounts
Lead times require accurate 3-month forecast
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Demand Planning Process Example
Business Objectives
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Business Objectives : « Aftermarket » distribution channel
 Increase forecast accuracies (1, 2 and 3 periods ahead (months))
 Decrease inventory of finished goods in the network
 Maintain and increase customer service
 Reduce planning complexity
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Demand Planning Process Example
Modules Deployed
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Demand Planning Process Example
Required Planning Technology Enablers
Hyper cube technology (RAM based cube)
Usage and
enablement of
all of these
features
Configurable environments & processes
Easy integration to any other tools
Library of algorithms – 300 indicators
Collaborative work flows – Excel integration
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The key is to integrate all the information into the same planning workspace (screen)
Planner shall see, by default, only the important elements on which he should focus on
Planner should have drill down capability to see components that create or influence the
value’s behavior
Reaction time between the change of an input and the visualization of its impacts on a set of
outputs should tend to be “real time“
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Demand Planning Process Example
Planning Environment Prior to the Implementation of nSkep Demand Planning
Build and manage
forecasts
Excel files (non
standard)
Consolidate files
Product Line i
Use forecasts
Procurement
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Production
…
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Product Line N
2
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Finances –
Budget tracking
Usage of diverse non standard Excel spreadsheets to build forecasts
Manual and labor intensive planning process
Each business unit had the responsibility to supply its sales forecasts
A team at headquarter had the responsibility of consolidating and formatting forecasts from
all business units
Long response time (long planning cycle) and heavy usage of human resources
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Demand Planning Process Example
The Vision
Supply Chain
Suppliers +
Manufacturing plants
Control tower
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Collaborate
Control
Plan
Execute
Measure
One planning tool
• Integrated with the transactional
systems
• With BI capabilities
• Configurable and flexible
Clients
Distribution
and transportation
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Demand Planning Process Example
Implementation Phases
Phase 1
Phase 2
Centralization and
standardization
Optimization and Simplification
• Install and deploy an integrated
SCM planning technology
• Focus on consolidated forecasts
at the item levels
Manage by exception.
Automation and optimization
• Manage by exception
• Create planning clusters
• Create alerts and alarms
• Reconcile financial trends with
the operational forecasts
• Forecasts at the item-hub
(warehouse) levels
• Forecasts at the item-spokes
(warehouse) levels
Centralized planning
functions, integration of
information flows and of
functions
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Internal collaboration
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Demand Planning Process Example
Centralized Demand Planning Function
Centralized Function
Step 1 Improvements
 Statistical forecasts –
automated process
 Segmentation of the
process by product line
and by ABC classes
 Measure and track
forecast accuracies
 Consolidation of data
flows and planning
processes on a
centralized platform
 Stabilize data flows
Reduce number of
participants
 Better visibility
S&OP Process
Sales Forecasts
Statistical forecasts
built and managed by
the demand planner
Sales forecasts built
by the CMs (Category
Managers)
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Consensus
forecasting process
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5
# Number of resources involved in the process
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Demand Planning Process Example
Centralized Demand Planning Function
Centralized Function
Step 1 Challenges
S&OP Process
Sales Forecasts
Statistical forecasts
built and managed
by the demand
planner
Sales forecasts built
by the CMs
(Category
Managers)
Consensus
forecasting process
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 A large quantity of
information to review
Difficulty to trust the
system (change
management challenge)
 Wrong interpretation
of the outputs
 Improper or weak
understanding of
statistical models and
how they manipulate
information
 Long time required to
review all forecasts
 Difficulty to properly
interpret risks
 Bias caused by human
factors
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Demand Planning Process Example
Manage by Exception  Automation and Optimization
Segmentation of the Complexity and Processes
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Strong Positive Trend
Risks - Variability
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Strong Negative Trend
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Sort by importance
Stable Trend
Alerts
Reviews achieved by the planner
3 130 items (40 %) to review
Seasonal
Discrete
Non Seasonal
Automated reviews by the system
4 717 items (60 %) reviewed by
the system
New
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Demand Planning Process Example
Manage by Exception  Automation and Optimization
Segmentation of the Complexity and Processes – Second Level of Segmentation
Reviews achieved by the planner
1845 items (23.50 %)
Automated reviews by the system
6002 items (76.50 %)
Sales Forecasts
Non Seasonal
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Seasonal
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Demand Planning Process Example
Manage by Exception  Automation and Optimization
Manage by Exception - Automation and Optimization
Sales Forecasts
Statistical forecasts built and
managed by the demand
planner
Sales forecasts built by the
CMs (Category Managers)
Consensus forecasting
process
Seasonal
Discrete
Non Seasonal
New
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Step 2 Improvements
 Reduce by 76% the
number of items
reviewed on a monthly
basis
 Increase the users’
trust with the planning
system
Help focus on the most
important elements
 Reduce complexity
 Quickly identify
problems and suspicious
behaviors
 Reduce planning cycle
time
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Demand Planning Process Example
Manage by Exception  Automation and Optimization
Manage by Exception - Automation and Optimization
Sales Forecasts
Statistical forecasts built and
managed by the demand
planner
Sales forecasts built by the
CMs (Category Managers)
Consensus forecasting
process
Step 2 Challenges
 Difficulty to reconcile
operational forecasts
with financial trends
 Gaps between the two
forecast flows
Seasonal
Discrete
Non Seasonal
New
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Demand Planning Process Example
Internal Collaboration
Sales + Supply Chain + Finances (Complete Internal Collaboration)
Sales Forecasts
Sales and Supply Chain
Statistical forecasts built
and managed by the
demand planner
Sales forecasts built by
the CMs (category
managers)
Product
Line
Item
Item – HUB
(Warehouses)
Finances
Consensus forecasting
process
Consensus and alignment
of financial trends with
operational plans
consensus
Flex Budget
Production & Procurement
Supply Chain
Item – Spokes
(Warehouses)
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Demand Planning Process Example
Internal Collaboration
Sales + Supply Chain + Finances (Complete Internal Collaboration)
S&OP Process
Sales Forecasts
Statistical forecasts
built and managed by
the demand planner
Sales forecasts built by
the CMs (category
managers)
Consensus forecasting
process
Consensus and
alignment of the
financial trend with the
operational plan
Step 3 Improvements
 Alignment of
operational forecasts and
financial trends
 Better forecast at the
Item-Hub levels and at
the Item-DC levels
 Coherent
communication of the
demand plan to all actors
within the company
(one version of the truth)
Product Line
Items
Combinaisons « Item – HUB (warehouses) »
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Demand Planning Process Example
Tangible Benefits
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Business Objectives : « Aftermarket » distribution channel
 Increase forecast accuracies (1, 2 and 3 periods ahead (months))
• Increase forecast accuracies by more than 5 points
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Decrease finished good inventory in the overall network
• For only ten items, inventory value reduced by more than $50,000
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Maintain and increase customer service
Reduce planning complexity
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Centralized demand planning function
Standardized processes
Reduced number of actors
Reduced planning cycle time
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Demand Planning Process Example
Key Takeaways
Divide planning complexity and processes
based on a set of business rules, alerts and
classification parameters
Automate creation and classification of
planning clusters
Automate and optimize decision making
process whenever possible. Identify which
cluster can be treated.
Prioritize and identify planning clusters that
should be reviewed by the planner or by the
group
Use the right management alerts and key
performance indicators for each decision
making process
Within each planning cluster, prioritize items
to review based on other sorting criteria
Adapt decision making methodologies for each
cluster and each process
Allow decision maker to create and maintain
his own planning clusters
Identify decisions and parameters that have
been overridden by the user
Measure quality of decision (likelihood
corridor) and send alerts if decision is not
acceptable based on tolerance corridor
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Thank You!
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