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













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













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













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

http://www.google.com/url?sa=i&rct=j&q=Neural Network Nodes&source=images&cd=&cad=rja&docid=NhaOytSjpEHJPM&tbnid=oZ0L7iwvPEG7HM:&ved=0CAUQjRw&url=http://www.texample.net/tikz/examples/neural-network/&ei=yLIXUoGuCJGu4APgzIDQCg&bvm=bv.51156542,d.dmg&psig=AFQjCNGouZSTbLxuW25pNbzhVEGLd9frcw&ust=1377371186175944

http://www.google.com/url?sa=i&rct=j&q=Clustering&source=images&cd=&cad=rja&docid=ZettcgjDSWBIOM&tbnid=0SBcdpjt4tr2YM:&ved=0CAUQjRw&url=http://espin086.wordpress.com/2011/02/27/clustering-to-improve-merchandise-allocation-testing-and-forecasting-an-application-of-the-k-medians-algorithm/&ei=gLMXUvKVHfXI4AOgjoHgBg&bvm=bv.51156542,d.dmg&psig=AFQjCNGqA9FmdzWj3veHnb3RLbVhg9u2Kg&ust=1377371362687196













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:













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

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