Demand Planning Leadership Exchange: Demand Sensing - Are You Ready?
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Transcript of Demand Planning Leadership Exchange: Demand Sensing - Are You Ready?
August 27th, 2013 plan4demand
DEMAND PLANNING LEADERSHIP EXCHANGE PRESENTS:
The web event will begin momentarily with your hosts:
&
Proven Supply Chain Partner
More than 500 successful SCP engagements
in the past decade.
We’re known for driving measurable results
in tools that are adopted across our client
organizations.
Our experts have a minimum of 10 years
supply chain experience.
Our team is deep in both technology and
supply chain planning expertise; have
managed multiple implementations; have a
functional specialty.
“Plan4Demand has consistently put
in extra effort to ensure our Griffin
plant consolidation and demand
planning projects were successful.”
-Scott Strickland, VP Information Systems
Black & Decker
A dynamic techno-functional
supply chain professional with 10
years of experience in food,
beverage, CPG and medical
device industries.
Extensive knowledge of Demand
Planning, Production Planning,
Purchasing and S&OP across
SAP, JDA and i2 technologies.
Supply Chain Management
consultant and statistician with
over 20 years of process
improvement experience with a
focus in demand planning,
business intelligence and
technology experience across
multiple platforms, including SAP
APO, JDA, and Oracle.
Joel Argo,
Manager
Gary Griffith ,
Senior Manager
Understand demand sensing key concepts & capabilities
Understand the integration between the mid to long term
forecast (i.e. the operational forecast) with the short term
forecast (using demand sensing)
Technology considerations and change management impacts on
organization; demand planning maturity curve assessment
Walk away with an improved, objective view of the fit of
demand sensing within their organization
4
1. Demand Sensing Overview
Review Current Demand Planning Challenges
Define Demand Sensing - Value of Demand Sensing
Applicability of Demand Sensing
2. How Demand Sensing works
Input Variables - Forecast Horizons - Integrating with Statistical Forecast
Integration with Major Demand Planning Systems
3. Demand Sensing Examples
Net Change in Sales (Over/Under) - Net Change in Shipments (Over/Under)
Promotional Planning
4. Data Elements & Modeling Techniques
5. Change Management
6. Key Take-A-Ways
7. Q&A
5
Traditional statistical forecasting methods have become efficient
Difficult to integrate real time data into a quantitative time series statistical model
Same time series model applied across short, mid, and long term plan
Difficult to plan product launches and promotions without adequate sales history
Time consuming to evaluate stat models across hundreds of SKUs
Low volume items remain difficult to forecast due to fluctuations in demand
Companies becoming efficient
Skillsets within functional silos cannot support the full
use of certain technologies
Data repositories are large
New technological developments not as robust as a
decade ago
Confidence in new technology is low (clouds, S&OP
software, Demand Sensing)
Demand Management Demand Sensing Business Benefit / Risk
Primary
Purpose
Long term strategy and sales
forecast, Better manufacturing
planning
Short term, tactical forecast, Better
replenishment planning to one, or a
few, key retail customers
Improved inventory
positioning; reduce
out-of-stocks
Most Granular
Data Used
Shipments from manufacturer’s
DCs to customer’s DC
POS and in store inventory data Minimize the “Bull Whip”
Effect
Completeness
of Data
Across all customers Across one, or a few, key retail
customers
Limited focus but with
higher/targeted results
Rolling Forecast
Time Horizon
Rolling monthly forecasts
over a year
Rolling daily forecasts
over the next 4-12 weeks
Better placement of inventory
with daily forecast updates
Key Forecast
by Time Period
Consensus demand plan for
+18 months horizon
Next week’s or month’s replenishment
plan to the DCs
Improved deployment
planning; reduce
transportation costs
Key Drawback Susceptible to Bullwhip Effects in
operations, causing increase in
the cost of time, money and
resources
Many retailers lack sufficient in store
inventory accuracy to make this
feasible but “Big Box” retailers are
ready
Data completeness and
accuracy, a risk;
Collaboration, a necessity
8
AMR Research
“Demand Sensing is the amount of time it takes to see true channel purchase or consumption data.”
SCMFocus.com
“Demand sensing is the use of a procedure to analyze the demand history in order to gain new insight as to how to develop a better forecast, and to make changes in the short term to the forecast ”
Ad Hoc Definition
“The process of utilizing the most current market information to generate a short term demand plan”
1980 1990 2000 2005 < 1970 2010
Traditional Forecasting Methods
Fourier, Holt Winters, Lewandowski, Crostons
ERP Systems Become Dominant
SAP, E3, AS400, Lawson, JDE
Demand Sensing Development
TeraData begins refining demand sensing
Early Computers
Computing automates statistical models- Large ERP companies emerge
Forecasting tools Refined
Module development begins
Cloud Computing
Large cloud servers are used primarily as backup tools
Sophistication
Tools become more sophisticated, cloud computing common
Current Companies:
Current Industries:
Chemical, Oil and Gas, Food and Beverage, CPG
Factors to be considered prior to DS implementation:
Lead Time (Cycle Time)
Order UOM vs. Forecast UOM
Maturity of Demand Planning Processes
Maturity of S&OP Processes
Level Demand Planners Skillset
System Compatibility
Goal of Demand Planning Group
Demand sensing initially adopted by CPG companies (quick production cycle time)
Demand sensing short term tool (4-16 weeks)
Not a replacement for statistical forecasting
Distributor may use lead time as short term
Potentially not applicable for items where lead time exceeds more than 16 weeks
P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P 10 P 11
Short Term Mid Term Long Term
• Demand Sensing Horizon
• Lead time or production cycle
time for product
• Generally 4-16 weeks
• Raw material planning zone
• Potential increase safety stock
of raw material
• Directly affected by Sensing
• Financial Planning Zone
• Statistical forecast efficient
todays news = old news
• Not affected by Sensing
Ability to forecast order
quantities in the short term
vs. sales forecast
Safety stock currently
handles delta
Sales forecast based off
revenue target
Order forecast based on
customer orders
Includes order minimums,
orders not shipped, typical
order size
Setup as data repository
* Example mandates a min order quantity of 700 units and assumes 1 order per month
- Increases annual volume by ~4K units
1. Demand planning organization enters demand plan into Excel or Access based on inputs from an informal S&OP process
2. Demand Planning has a basic forecasting system but no S&OP process
3. Demand Planning has a basic forecasting system with a formalized S&OP process but not fully leveraging system- Beginning to experiment with statistical modeling
4. Demand Planning is utilizing statistical modeling and executes a formalized S&OP process- Statistical modeling can be improved
5. Demand Planning is actively forecasting all items via statistical forecasting, but struggling to improve MAPE
6. Demand Planning is actively using demand sensing and causal modeling to improve forecast accuracy while using S&OP systems and tools to improve overall S&OP process
1
2
3
4
5
User Skillset
6
Sys
tem
Ca
pa
bil
itie
s
Operational Financial Goals
Reduce working capital costs
Improve customer service
Minimize production costs
Increase network capacity
Improve cash flows
Organizational Goals
Streamline Demand Planning Process
Improve KPI’s
Improve Financial Planning
$
$
Reducing Working Capital Reduction in raw materials,
safety stock and cycle stock
Estimated every $.01 saved in
production equals $10.00 + in
sales
Minimize Production Costs Produce product only needed for
sales and lead time variation
Saves man hours, machine hours,
trans costs
Increase Network Capacity Space = Money
Consolidate
React to ad hoc events
Less spending on capital
Improve Cash Flows By utilizing real time downstream
signals to predict customer order
patterns net terms could be
minimized while maximizing fill rate
leading to increased profit margins
and faster cash flows
Improve Customer Service Ability to predict order size
React to demand fluctuations
Ord
er
to C
ash
Traditional Demand Planning Process
Based on forecast accuracy, traditional demand planning processes may require manual adjustments to forecast in the short term to accommodate peaks and valleys based on current market knowledge
Demand Planning Process with Demand Sensing
Demand sensing potentially reduces the amount of SKUs a demand planner needs to review as forecast
accuracy is increased through analyzing current market conditions
By utilizing current sales patterns and trends, demand sensing will automatically incorporate market conditions
into the short term forecast
Demand Sensing tools are usually applied on an ongoing basis, therefore, the short term forecast could
change frequently based if the disconnect between actual and forecast justifies a change.
Demand sensing
overrides short
term forecast
Production adherence
Increases accuracy on what is scheduled vs. actually produced
Production attainment
Less variability in production plans due to accurate planning should allow production to focus on efforts
Safety Stock
The ability to predict customer orders directly reduces the amount of inventory needed for demand variability while maintaining service level
Main input for any stat safety stock model is Demand variability and service level
Inventory Adherence
Working capital is a large cost center
Ability to accurately predict investment leads to an attainable and executable financial plan and goals
Forecast Accuracy
Lag dependent
Demand sensing used for short term forecast (6-8 weeks) or in some cases lead time
1. Demand Sensing Overview
Review Current Demand Planning Challenges
Define Demand Sensing - Value of Demand Sensing
Applicability of Demand Sensing
2. How Demand Sensing works
Input Variables - Forecast Horizons - Integrating with Statistical Forecast
Integration with Major Demand Planning Systems
3. Demand Sensing Examples
Net Change in Sales (Over/Under) - Net Change in Shipments (Over/Under)
Promotional Planning
4. Data Elements & Modeling Techniques
5. Change Management
6. Key Take-A-Ways
7. Q&A
19
Dow
nstr
eam
Da
ta
Shipments • Daily shipments
• Helps recalculate forecast on
projected order multiples
Orders • Daily orders to DC
• Includes orders that do not ship
• Order multiples
VMI Customers
• Auto replenishment history
• Similar to orders
Point of Sale Data • Daily sales data
• Detects variation in forecast from actual sales
Demand sensing vendors claim independent variables such as economic and weather conditions can be incorporated into demand sensing programs
Net changes in weather or markets would impact output
Data repository would need to be setup for variables
Historical
Sales
Shipments
Inventory
Demand
Planning Statistical Modeling
Demand
Sensing Heuristics and Modeling
Orders
Sho
rt T
erm
Fore
cast
Data Repository
System
Output
Legend
Demand
Planning
System
(APO,JDA)
Demand
Sensing
Tool
Data
Repository
Periodic Forecast
Update Data repositories need to be created for the
variables driving demand sensing forecast Send transactional data to demand planning
system and demand sensing tool
Demand sensing tools usually bolt-on to
demand planning system, but can be
integrated depending on the system Recent transactional data ran through
heuristics or mathematical models to adjust
short term forecast
User defines timing, variables, and methods
Sensing tool sends and updates forecast in
planning system
Process repeats weekly, daily, monthly
Some forecast disaggregation or other
specific settings may need to be tweaked
depending on current processes
Supply
Planning
System
1. Demand Sensing Overview
Review Current Demand Planning Challenges
Define Demand Sensing - Value of Demand Sensing
Applicability of Demand Sensing
2. How Demand Sensing works
Input Variables - Forecast Horizons - Integrating with Statistical Forecast
Integration with Major Demand Planning Systems
3. Demand Sensing Examples
Net Change in Sales (Over/Under) - Net Change in Shipments (Over/Under)
Promotional Planning
4. Data Elements & Modeling Techniques
5. Change Management
6. Key Take-A-Ways
7. Q&A
24
Demand sensing uses heuristics and
learning algorithms to adjust short term
forecast based on recent sales
Adjustments are incorporated into the
statistical forecast
Sales forecast reflects sales plan without
min order quantities
Ship history reflects min order quantity of
4,000 and incremental order quantity of
4,000 units
Sensing lowers forecast due immediate
performance
Noticed promotional volumes were not as
high as forecasted adjusted approximately
2200 units
1. Demand Sensing Overview
Review Current Demand Planning Challenges
Define Demand Sensing - Value of Demand Sensing
Applicability of Demand Sensing
2. How Demand Sensing works
Input Variables - Forecast Horizons - Integrating with Statistical Forecast
Integration with Major Demand Planning Systems
3. Demand Sensing Examples
Net Change in Sales (Over/Under) - Net Change in Shipments (Over/Under)
Promotional Planning
4. Data Elements & Modeling Techniques
5. Change Management
6. Key Take-A-Ways
7. Q&A
28
Demand sensing is a daily process that uses near-term granular data to
improve the consensus forecast in the short term
Internal Sources:
Uses consensus forecast as an input along with current forecast accuracy and
forecast bias results
Daily sales orders (includes open orders) and shipments
New Product Introductions
Promotions / Price Changes
External Sources:
Daily store / item level POS
Consumer Sources (e.g. SAS can use structured or unstructured data)
Consumer behavior changes / trends
Social network sentiment
Economic trends such as downturns or market shifts
Significant weather impacts such as disasters (e.g. Hurricane Sandy)
Now
Next
Later
Tools like SAP SCM – APO
DP, JDA Demand are
widely used to estimate
statistical baseline
(i.e. key input into
demand plan) using
primarily time series and
intermittent demand
techniques
Demand sensing uses
advanced demand pattern
recognition techniques on
multiple demand signals,
promotions, new product
introductions and customer
feedback , for example
Source: Industry Week article by Charles Chase and Michael
Newkirk SAS; April 2012
SmartOps
Enterprise Demand
Sensing solution
estimates the
optimal mix of
demand inputs to
create an improved
short term forecast
31
The algorithm(s) being used by demand sensing vendors are proprietary but
tend to be advanced learning algorithms that are reactive and robust
Example: Predictive Modeling
Neural Networks (Data transformations occur at network nodes)
Clustering
32
1. Demand Sensing Overview
Review Current Demand Planning Challenges
Define Demand Sensing - Value of Demand Sensing
Applicability of Demand Sensing
2. How Demand Sensing works
Input Variables - Forecast Horizons - Integrating with Statistical Forecast
Integration with Major Demand Planning Systems
3. Demand Sensing Examples
Net Change in Sales (Over/Under) - Net Change in Shipments (Over/Under)
Promotional Planning
4. Data Elements & Modeling Techniques
5. Change Management
6. Key Take-A-Ways
7. Q&A
33
It is important that your organization is ready for
demand sensing as it is more sophisticated then the
advanced planning systems such as SAP, JDA and
Oracle Demantra that are often present their own set
of change management challenges
Think about piloting with a segment of the business
and one of your SuperUsers Proceed with caution
Assess where you are on the demand planning
maturity curve
34
Based on the short term forecast focus of demand sensing you should be
at least “Functional” and preferably “Skilled” in your current state
demand planning process
Some key areas of maturity are identified below:
1. Demand Sensing Overview
Review Current Demand Planning Challenges
Define Demand Sensing - Value of Demand Sensing
Applicability of Demand Sensing
2. How Demand Sensing works
Input Variables - Forecast Horizons - Integrating with Statistical Forecast
Integration with Major Demand Planning Systems
3. Demand Sensing Examples
Net Change in Sales (Over/Under) - Net Change in Shipments (Over/Under)
Promotional Planning
4. Data Elements & Modeling Techniques
5. Change Management
6. Key Take-A-Ways
7. Q&A
36
Demand Sensing is utilized in the short term and uses learning heuristics, stat modeling to make short term adjustments that incorporate current sales data, not historical
Demand sensing is currently being used primarily in CPG but can be applied to other industries
Several key considerations need to be considered before implementing demand sensing tools: Goal of the organization
Have current tools been maximized
System compatibility
Are short term changes possible operationally
The intent of demand sensing tools is not to cancel out stat forecasting
Demand sensing can help improve accuracy Predict order size
Use ad hoc variables (causal variables)
Good, accurate and timely data is key to implementation
For Additional Session Information, a PDF Copy,
or to Schedule a One-on-One…
Contact
Jaime Reints
866-P4D-INFO
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