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UPGRADING TO ASCP 12.2.5.XNov 11, 2015
Demantra Engine Tuning WebinarJanuary 19, 2016
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Housekeeping & AnnouncementsHello! Christina Bergman, Marketing Director, AVATADuring the WebinarPlease remain muted throughout presentationSend questions through chat windowAfter the WebinarCopy of entire presentation will be emailed to youWebinar archived on our SlideShare & YouTube AccountUpcoming WebinarsMar 15 - Trade Promotion Management Express SolutionMay 17 - Understanding Oracle SCM Cloud for High Tech & Disc MFGJul 19 Supply Chain Planning in the Cloud for JDE
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Introductions
Duane Hardacre, Managing Partner - AVATASanjay Agrawal, Director of Operations - AVATASharon Eliasi, Solution Specialist Analytical Factor
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Change release flags to specific enhancements associated with each release3
AgendaAbout AVATA and Analytical FactorWhy Invest in Tuning Demantras Statistical EngineCalculating ROIHow to best tune the Engine Best practice forecast metricsMethodology & Key areas of focus When to tune the engineWrap-up / Q&A
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Change release flags to specific enhancements associated with each release4
AVATA Analytical FactorStarted partnering in 2010Delivered many successful engagements together 2015 AVATA established equity stake to form an exclusive partnershipStrategic, Collaborative, Scalable
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with Deep VCP Expertise
Supply Chain PractitionersThe AVATA Difference
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Our consultants are unsurpassedin their depth of experience as supply chain practitioners.
Our PeopleOur Customer Focus
Our StrategyThere are pure strategy players, there are pure system integrators. And there is AVATA that brings both together. where strategy meets execution.We aim to achieve 100% customer satisfaction and referenceability! We are committed to the success of our clients at all costs!
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Analytical Factor - About
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Why Invest in Tuning your Statistical Engine
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Impact Overall SCM ProcessImproves credibility of the solution and optimizes the entire process Drives better process adoption, execution and closed loop feedback with trusted metricsReduces manual touches and bias in users/organizational inputsImproving forecast at the Finished Good SKU has positive ripple effect on the entire downstream supply chain Better Inventory Strategies (reduced capital investment)Increased visibility for Supply PlanningBetter supports your S&OP / IBP ProcessImproved visibility for CFO office
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Its About the Money and better supply chain execution Improve forecast resultsReduce forecast error Understand BIAS Waterfall Lag Times (understand impacts of lead-time)Forecasting has a direct correlation to inventory and service levelsOver forecast = Too much inventory Under Forecast = Insufficient stock, lost sales, missed shipments
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Calculate ROI on Forecast ImprovementMethod 1 (Conservative): Based on an industry studies: a one-percent decrease in forecast error translates into a 0.5% - 1% decrease in safety stock inventory.Method 2 (Based on AMR/Gartner Research correlations): a 6% forecast improvement can improve the perfect order by 10% and deliver as high as a 10-15% reduction in overall inventory.Method 3 (Multipronged Quantitative): Formulaic ROI which takes into consideration several impacts of forecast error on supply chain metrics/financials - inventory (over/under), target service level, gross margin, cost of stock outs. Yearly Benefit / Value B of improving forecast error:
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Calculate ROI on Forecast Improvement
Method 4 (Advanced Multipronged Quantitative)
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Forecasting Best Practice Snapshot
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Forecast Best Practice Simplified SnapshotClear Assumptions, right ownership and reduce organizational or other biasesEstablish a one number plan that is the final forecast driving the supply chain. There could be multiple constituents with their inputs into that numberForecast should be wrapped around a collaborative process (internally / externally) Focus on metrics (e.g. MAD, MAPE, WMAPE, LAG, BIAS) See Appendix A Leverage different LAGS (different business units, product categories)Customer / Demand segmentationData & Analytics Have the proper analytical skillset
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Forecast Best Practice Simplified SnapshotLet Statistical engine drive run-rate / stable high-runners and focus on the exceptions and outliersConsumer Good IndustryLeverage POS / Consumption data in addition to shipment / booking data (understand sell-in versus sell-through) Incorporate promotional data (understand lift decomposition)Incorporate syndicated data Relentless focus and continuously improve (plan-do-check-act)Inquire about our on-going managed service support for tuningLeverage our health dashboards
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Tune The Statistical EngineBest Practices
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How is Demantra Engine Different
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Demantra EngineBasic understanding of the different aspects of tuning the Demantra engine
300+ Engine Parameters
Forecast Tree
Process NodesEngineProfiles
Promotional Engine
17 Stats Models
Causal FactorsThousands of permutations to optimize the output
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Tuning Methodology
Report & Educate
Transition / Migrate Solution
Analysis & Tune
Assess & Benchmark
Forecast Metrics Environment Acclimatization30 to 60 Days
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Tuning Methodology Assess & Benchmark
Assess & Benchmark Key objectives of the Forecast and who is the composure / owner / User of the dataSupply Chain (Demand Planning)Sales (sales operations / field sales / sales mgmt.)Finance & budgeting Supports S&OP ProcessPromotions management High Level overview of the forecast process (What level(s) are you forecasting, frequency, etc.)
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Assessment High level review of current Demantra configuration, forecast process and data integration.Assess current Demantra forecast accuracy metrics, formulas / calculations, (if existing)Benchmark: Establish a forecast accuracy metric and lag baseline against which to measure the results. Decide on a subset of items/family and/or locations to benchmark Establish baseline statistical engine output Create worksheets used for Analysis and benchmarking Setup Engine tuning instance (latest copy of Production)Provide level effort for tuning implementation, areas of focus and estimated forecast error improvement expected
Analysis Based on the hierarchy structure, deep dive analyze the quality of data for, e.g. Volumes Gaps Missing data, zeros, negative data, intermittency, inconsistencies, nave forecast Spikes Outliers, causals, promotions, volatility, Patterns / trends, order frequency Business segmentation (Distribution / Retail) SeasonalityBackcast analysis and adjustments of the following based on iterative engine runs and proprietary SQL & Statistical analytical tools: Forecast Tree (analyze different levels) Causal Factors Engine parameters System parameters Statistical models used Create worksheets used for analysis
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Tuning Methodology Assess & Benchmark
Assess & Benchmark High level review of current Demantra configuration, and integrationsInitial review of the data (structure, data quality, completeness)Assess current metrics and benchmarkEstablish agreed upon baseline forecast accuracy metrics - formulas/calculations and lag against which to measure the results Setup Demantra test instance for tuning (latest copy of Production)Provide level of effort for implementationTuning strategy and areas of focusEstimate expected forecast error improvement
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Assessment High level review of current Demantra configuration, forecast process and data integration.Assess current Demantra forecast accuracy metrics, formulas / calculations, (if existing)Benchmark: Establish a forecast accuracy metric and lag baseline against which to measure the results. Decide on a subset of items/family and/or locations to benchmark Establish baseline statistical engine output Create worksheets used for Analysis and benchmarking Setup Engine tuning instance (latest copy of Production)Provide level effort for tuning implementation, areas of focus and estimated forecast error improvement expected
Analysis Based on the hierarchy structure, deep dive analyze the quality of data for, e.g. Volumes Gaps Missing data, zeros, negative data, intermittency, inconsistencies, nave forecast Spikes Outliers, causals, promotions, volatility, Patterns / trends, order frequency Business segmentation (Distribution / Retail) SeasonalityBackcast analysis and adjustments of the following based on iterative engine runs and proprietary SQL & Statistical analytical tools: Forecast Tree (analyze different levels) Causal Factors Engine parameters System parameters Statistical models used Create worksheets used for analysis
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Tuning Methodology Analyze & TuneAnalyze data, configuration and performance Volumes Gaps Missing data, zeros, negative data, intermittent Spikes Outliers, causals, volatility, Seasonality Patterns / trends, order frequency Business segmentation (Distribution / Retail)
Analyze & TuneBack-cast: analysis and adjustments of the following based on iterative engine runs and proprietary Statistical tools: Forecast Tree (analyze different levels) Causal Factors Engine parameters System parameters Statistical models used Create worksheets used for analysis
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Capture Unique Aspects of Your BusinessDifferent lines of business or business units or other aspects of the business may require separate / isolated engine configurationsForecasting with multiple profiles, enables line of business specific modeling and configurationSeparate engine profiles can be configured based on unique traits or nuances of a particular business segment and provider greater overall forecast error reductionMultiple profiles creation can reduce overall statistical error by an additional 5-10% beyond global tuningIncrease line of business ownership and acceptance
Analyze & Tune
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NPI New Product IntroductionUtilize Demantras powerful capabilities for NPI:Shape Modeling
Pooled Time Series
Jun 14History
Aug 15
Sept 16Future
Jan 14
Aggregation
Pooled Time Series
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Tuning Methodology Report & EducateDocument findingsPresent ResultsEducate / Knowledge transfer on the tuning configurationsImpacts on current forecast process On-going management of forecast qualityMigrate configuration to production
Report & Educate
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Frequently Asked QuestionsQ: When should I tune the engine? Typically, full tuning should be done 3-12 months after initial go-liveBaseline tuning is done during the implementation but thats not full tuning.Q: How frequently should I tune?Differs between clients, industries, product lines, etc., but best practice is to perform continuous monitoring, and have mini-tuning exercises on a more regular basis, for optimal accuracyLeverage our on-going health check dashboards and continuous monitoring & tuning managed service offeringAnytime there is a significant change to the business: Acquisition, Enter a new market, etc.
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Hidden GemVast majority of Demantra installs have not gone through an engine tuning exercise!!!AVATAs average forecast error improvement ranges from 8% - 16%Free Engine Tuning Estimation!No risk to customer Estimate forecast improvement potential
Engine Tuning Enhancements
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GET IN TOUCHwww.avata.com
Lets discuss yourbusiness needs.
Were here to help!
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Appendix A Metrics One size does not fit all understand what you are measuring and whyExample Error formula ((Actuals Forecast)/(Forecast or Actuals) changing to denominator determines what your focusing on: over-forecasting or under-forecasting. Depends on your objectives and business environment Demantra has the following out of box metric:
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Appendix A Metrics
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Appendix A Metrics
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