Nestle Waters Forecasting Improv Ment Slides

5/23/2018 Nestle Waters Forecasting Improv Ment Slides

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CONTINUOUS OPTIMIZATION OF DEMAND PLANNING IN

NWNAPresented by Ron Levkovitz, Sid Reddy, Jon Santos

NWNA, Ogentech Ltd.

June 2012


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Many demand planning solutions do not stand to theiroriginal premise (improving accuracy)

The typical phenomenon

Good accuracy on well behaved items that arealready well predicted by planners

Low accuracy on promoted/badlybehaved/cannibalized/new items that are

complicated to forecast

Result

The introduction of statistical forecasting does not

improve forecast accuracy when compared to themanual forecasting

The statistical forecasting fails to gain plannerstrustplanners revert to manual planning

Easy toforecast

Impossibleto forecast

Easy toforecast


StatsForecast

?


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Nestl Waters North America

Overview and deployment strategy


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$1,507

1976 80 82 87 89 92 93 99 00 01 02 03 04 05 06 07 08 09

$1 $50 $50 $440 $571 $613 $687 $1,673 $2,103 $2,409

Sales Revenue$MM

$2,901 $3,388 $3,846$2,632 $4,260

Nestl Waters North America has beenin the Bottled Water Business for Over 30 Years

$4,240

$4,08

Glacier

Pure

Cameron

Springs

Capco
http://webnative.ryanpartnership.com/webnative/getimage?-web+/RAID3/wn_client_sites/perrier_hires/Nestle_Pure_Life/Labels_and_Logos/logos/pr1585_npl_flavors_logo.epshttp://webnative.ryanpartnership.com/webnative/getimage?-web+/RAID3/wn_client_sites/perrier_hires/Nestle_Pure_Life/Labels_and_Logos/logos/NPL_PureLife_logo.eps


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Our Brand Portfolio Is a Competitive Advantage

Super Premium

Premium

Popular

Value

Regional BrandsSpring & Sparkling (Heritage, Local Values)

National BrandNestl Pure Life

(Family, Trust, Volume Generator)


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NWNAs Supply Chain Is Best In Class

Against All Beverage Manufacturers

Product ion

31%

Shutt le

Transportat ion

1%

Warehousing

7%

Outbound

Transportat ion

23%

Repo WT Returns

Deliveries

Packaging

Materials

35%

Water,

Flavors...

3%

Low Cost Manufacturing (BIC)

Environmental Leadership

Efficient Logistics

Flawless Customer Service

Fully Integrated with Sales


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Dallas

CabazonOntario

Hawkins

Red Boiling

Madison

Zephyrhills

DenverIndianapolis

Allentown East & West

Mecosta

Hollis

Hope

Guelph

Livermore

Phoenix

Houston

Lorton

Chicago

Hilliard

Sacramento

Poland Spring

Kingfield

LA

28 Factories in US & Canada

19 Retail PET Factories

9 Direct Factories

100% Non-Union

NWNA Factory Structure

Pasadena


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Customer Material Location Combination

Level # of Nodes # of STEADYNodes

Sales Organization 2 2

Business Segment 12 11

MG1 371 322

CL4 3,674 2,214

Material 6,019 3,192

Location 10,550 5,109

Planning Level(MG1/CL4). Jointmarketing/supply chainODP meeting

Desired forecastinglevel (material ->location)


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DPA Material Location 2009


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Decision we expect the plannerto take when using SAP/APO

Relevant past length

Daily, Weekly or monthly

Algorithm strategy

Algorithm parameters

Type of outlier correction

Type of error

Hierarchy level

Things we expect the plannerto know

Judge statistical forecast

Judge marketing projections

Consolidate between supplychain and marketing demandsand resolution (e.g. consolidatenationwide clients andlocations)

Understand promotions

Cannibalization effects

Etc.

It is not reasonable to expect the planner to apply judgmentsfor all items at all levels

It is not reasonable to expect the planner to optimize thebaseline forecast via statistical means


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NWNA original problem statement

Arrive at the Right Level for Statistical Forecasting

Figure out the most accurate way for Dis-aggregation

Select the Prudent Model (that suits our data) which might-

Suggest the best Level to use APO automatic algorithmic selection

Identify a potential for a Composite Forecast Algorithm.

Propose a better- fit Algorithm

Indicate a need for ABC categorization

We look to achieve a DPA (Product / Location / Week) increase without impactingthe current CDP Process. We need an empirical input for our direction forward.


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Forecasting solutions approaches in demandplanning


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Many demand planning solutions do not stand to theiroriginal premise (improving accuracy)

The typical phenomenon

Good accuracy on well behaved items that arealready well predicted by planners

Low accuracy on promoted/badlybehaved/cannibalized/new items that are

complicated to forecast

Result

The introduction of statistical forecasting does not

improve forecast accuracy when compared to themanual forecasting

The statistical forecasting fails to gain plannerstrustplanners revert to manual planning

Easy toforecast


Easy toforecast


StatsForecast?


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Forecasting solutions approaches in demand planning

Automatic systems - produce full forecast using a completemodel (including all relevant information).

Semi automatic systemsproduce mostly baselines. Humaninterventions are required to complement the forecast withpromotions, cannibalizations, etc.

Toolbox systemprovide planners tools to produce statisticalforecasts if they wish to do so


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The challenge

Moving from the old fashioned planner based forecasting to anautomated process where the planner intervenes only in places theycan provide real benefits.


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How to improve results? The positive planning cycle summary

Define the needs and the proper objective functions to reflectthem

Produce fully automatic highest accuracy rich forecasts

define cleansing, event handling and forecasting strategiesUse segmentation tools to distinguish between excellent,good and bad forecasts

Insure acceptance of the better forecasts, limit the influenceof bad forecasts

Avoid deterioration by making the inspection loop an integral

part of the operational process and by re-optimizing theresults


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DP system (SAP APO) design and maintenanceOptimization involvement points -Planners

Interventionpoints

Creation

Methodology


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The optimization system: ForecastPlanner by Ogentech

Run Full Simulation and computeoptimal forecasting policy

Get Data

Forecast result

Reconciliation tools

Acceptance tools

BI and predictive analytics

Mode 1: Forecasting Service

Configuration andForecast Policies

DP optimization

Run simulation limited toHosting DP abilities

SAP-APO JDA Manugistics

Oracle Demantra.

.

Forecast Planner Generic Technology

Algorithmic pool forecasting core.

Time series analysis,

explanatory analysis, risk engines,

correlation analysis, clustering and

hierarchical analysis

Exception mgnt.

Reconciliation tools

Acceptance tools

Bi and predictive analytics

Exception mgnt.

Mode 2:

Mode 1:


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Use all available attributes to create cross

hierarchies trees and combinations.Model information

Desired periodicity & horizon

Levels of Interest and multiple time bases

KPIs

Available data

Attribute hierarchies (item catalog, locations,sales outlets, )

Operations (events, lifecycles, promotions,holidays, )

Collections/BOMs (cross-hierarchy groups)

Historical data (with all known attributes)

Trade partners dataSupporting data

POS data

Item attributes (master data)

Sales and marketing atributes

Time bucket attributes

Model creationanalysis of the data exploit available data


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Item attributes (master data)

Sales and marketing atributes

Time bucket attributes

Model creationjudging forecasts

Bias: coverage of sales - required for inventory

and capacity management

Distance: lower the error for (?) periods using

some sort of error function

Structure: look right, behave the same way that

past data behave ( different from distance

because measured in the future and not on the

past.

Stability: should not change much from period

to period unless something really changes

(otherwise disturbers enrichments)


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Find the best model for forecasting

Find the best model for cleansing

In NWNAshould I forecast

Combines knowledge from different levels

of the hierarchy tree

Determines the best forecasting strategies,

past length and the associated algorithm

parameters for different futures

Adapts methods to the appropriate time

base and reconcile results (weekly,monthly, quarterly)

Model creation: best model approach (and not best fit approach)


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Mapping ForecastPlanner to SAP APO

Define attributes for selections (e.g. Forecast Strategy and

segmentation)

For each selection: Define proper forecasting hierarchies

Define hierarchy disaggregation methods

Define time base disaggregation method

Define cleansing methods and cleansing scope (time base and

hierarchy)

Define forecasting profiles and master profiles

Algorithms

Algorithmic parameters

Outlier correction algorithmsCreate separate process chain


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Continuous result monitoring

Capture the entire process in the reporting

From statistical forecast to unconstrained final forecast

Add the forecasting hierarchy into the reports

Segmentation of products

Close inspection of performance and exceptions

Addition of results to the feedback loop


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Success of approach

New forecasting method installedduring June 2010

New forecasting helped improveforecast accuracy

Improvement in baseline forecastaccuracy 2010. Error drops fromaverage of

H1 2010: 45%

H2 2010: 35%0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

Baseline Error 2010

Baseline Error

Before After

Promotions have huge effect on summer sales


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Promotions have huge effect on summer salesBaseline and actual sales

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A large promotion of US43/0702/0WK(07 NPL Purif DC PET 20X0.5L) in thesummer of 2009

Forecast at Multiple Levels (MG1-CL4-M-L)


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Forecast at Multiple Levels (MG1-CL4-M-L)Overall very high stability of the results

27


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Effect of a weak summer

0

20,000,000

40,000,000

60,000,000

80,000,000

100,000,000

120,000,000

140,000,000

160,000,000

0

0.1

0.2

0.3

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0.6

200902

200903

200904

200905

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200910

200911

200912

201001

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201004

201005

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201010

201011

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201102

201103

201104

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201109

Turns rate

Avg. Inventory

Very good results from August 2010 ti ll February 2011

Significant stock build up from MarchMay

Build up to match the strong 2010 results due to a very hot

summerCold summer resulted in accumulation of inventory

From June onwards - performance is still better than 2009 and isalmost equal to 2010

Success of approach is also evident in Q4 2010


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Success of approach is also evident in Q4 2010Reduction is uniform and not due to the hot 2010 summer

Q42009 Q42010

Average case cost $5 $5

Sales (weekly average) 10,252,875 11,307,089

Inventory (weekly average) 19,010,466 18,098,250

In Months 0.44 0.38

early turns 27.18 31.49

Improvement 13.67%

Avg Inv using 2009 performance 20,965,146

Difference 2,866,896

Difference in $$$ $14,334,479

early Cost of Inventory Holding (industry figure) 15%

COI Per Month 0.0125

Savings, dollar per months $179,181

Savings, annual $2,150,172

DPA Material Location 2009


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ate a ocat o 009

DPA Material/Location 2010-11


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Working with planners

Th Ch ll


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The Challenge

1. Combine statistical forecasts and planners work

2. Make planners understand the automatic results

3. Help planners accept the statistical results and understand theirusefulness

4. Define clear separation between work to be done by thesystem and work to be done by planners

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Methodology


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Methodology

1. Create an optimized baseline for all levels

2. Use statistical proportional factors to define relations inside thehierarchy trees

3. Isolate areas where statistical forecast is better

4. Use statistical forecast proportional factors to drive planningdecisions to distribution decisions

1. from planning level (Material group @ trade partner) to distributionlevel (material @ location)

5. Measure success of statistical forecast and compare to finalforecast

6. Measure acceptance and confidence in statistics and sharewith planners

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Acceptance of statistics report


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Acceptance of statistics report

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Depth 5 - Material

Horizon 1

Gap Percentile All

Manager Id Mario

Values Baselineerr%

ConsensusError %Classification Month Baseline Baseline Abs Diff Forecast Forecast Abs Diff Actual

Consensus 2011-12 4,975,989 2,102,211 5,257,459 1,185,745 5,503,908 38% 22%

2012-01 5,459,062 2,561,699 5,469,104 1,605,679 6,009,137 43% 27%

2012-02 5,259,690 2,188,371 5,596,351 1,564,030 5,994,157 37% 26%

2012-03 6,699,328 2,665,815 6,726,029 1,700,798 7,774,651 34% 22%

2012-04 6,576,861 2,660,083 7,042,688 1,550,274 7,575,720 35% 20%

Consensus Total 28,970,930 12,178,179 30,091,631 7,606,526 32,857,573 37% 23%

Other 2011-12 1,687,316 3,145,847 2,853,712 3,586,383 2,550,769 123% 141%2012-01 2,067,467 4,632,853 3,585,201 2,959,051 4,182,610 111% 71%

2012-02 2,407,944 3,859,930 1,725,492 2,541,900 2,430,284 159% 105%

2012-03 2,296,818 4,325,411 3,461,703 4,328,084 3,514,351 123% 123%

2012-04 2,230,654 3,370,623 2,668,465 3,621,072 3,196,589 105% 113%

Other Total 10,690,199 19,334,664 14,294,573 17,036,490 15,874,603 122% 107%

Statistical 2011-12 6,497,845 1,858,156 7,103,055 2,761,754 6,880,187 27% 40%

2012-01 6,791,865 2,261,315 6,880,736 2,903,362 7,851,276 29% 37%

2012-02 5,749,458 1,541,138 6,245,709 2,103,615 6,611,012 23% 32%

2012-03 7,757,924 1,991,521 7,397,768 3,136,437 8,600,087 23% 36%

2012-04 7,281,780 2,295,005 8,323,506 2,424,529 8,911,947 26% 27%Statistical Total 34,078,872 9,947,135 35,950,774 13,329,697 38,854,509 26% 34%

Grand Total 73,740,001 41,459,978 80,336,978 37,972,713 87,586,685 47% 43%

Planning manager

Consensus is better during last 6 months

Stats is better during last 6 months

Forecasting level Both are bad

Growing levels of acceptance for stats

Impact of forecast improvement in client


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Executive Summary

A new forecasting methodology and procedure was deployed by during summer 2010

The procedure improved forecast accuracy at location level by an estimated 15%

The rate of inventory turns grew and money tied up in inventory was reduced from an average of

21M to around 18M.A nominal annual cost saving of $2-3 million was realized.

Successful forecasting was instrumental in quickly adjusting inventory levels to the coldersummer of 2011.

An upgrade, introduced in July 2011 (Ogentech 1.5), improves Canada accuracy results byaround 10%

Constant maintenance, monthly exceptions detection, and re-optimization of the paradigm

periodically insures maintaining these results.The maintenance adapts the statistical tools to the changing dynamics of trade partners, trendsand weather effectsthis prevents the familiar phenomenon of deterioration in statisticalforecasting that eliminates the benefits after few months.

The correctness of statistics allows planners to concentrate on changes and react better tomarket demands

The current service level by Ogentech is expected to continue to deliver cost savings of around3-4M per year


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Thank you

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Nestle Waters Forecasting Improv Ment Slides