Reinvigorating Your Investment in SAP APO Forecasting - Slide Deck from ToolsGroup webinar - 30 NOV 2017
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Slide Deck from the webinar The odds of achieving successful outcomes when using enterprise demand management technology for supply chain planning are the same as a flip of the coin. Why? The programs are just too labor intensive and the engines are not fit for purpose. The odds improve when optimizers are added to increase the output from ERP systems like SAP and Oracle. In this webinar, we share insights on the benefits of using an optimizer for SAP. Gain insights from Lora Cecere, Founder of Supply Chain Insights, and Gerrott Faulkingham from ToolsGroup on the steps to take to reinvigorate your demand planning system.
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Reinvigorating Your Investment in SAP APO Forecasting - Slide Deck from ToolsGroup webinar - 30 NOV 2017
- 2. Supply Chain Insights LLC Copyright © 2016, p. 2 Presenters Lora Cecere, Founder of Supply Chain Insights Gerrott Faulkingham, Business Development, ToolsGroup Bryan Semple, FCILT | VP Healthcare, ToolsGroup
- 3. Supply Chain Insights LLC Copyright © 2016, p. 3 Demand Error and Uncertainty Growing
- 4. Supply Chain Insights LLC Copyright © 2016, p. 4 The Long Tail is Growing
- 5. Supply Chain Insights LLC Copyright © 2016, p. 5 Probabilistic Approaches
- 6. Supply Chain Insights LLC Copyright © 2016, p. 6 Impact of Localized Assortment
- 7. Supply Chain Insights LLC Copyright © 2016, p. 7 Demand Management Success Is Like a Flip of a Coin
- 8. Supply Chain Insights LLC Copyright © 2016, p. 8 Satisfaction with Demand Planning is Low
- 9. Supply Chain Insights LLC Copyright © 2016, p. 9 Satisfaction
- 10. Supply Chain Insights LLC Copyright © 2016, p. 10 Data Inputs Engines Demand Plan Outputs Align Engines with Outcomes Planning Master Data
- 11. Supply Chain Insights LLC Copyright © 2016, p. 11 Companies Make the Mistake of Trying to Get Precise on Imprecise Numbers. Instead, they need to manage demand flows.
- 12. Supply Chain Insights LLC Copyright © 2016, p. 12 • A pattern caused by order frequency, order quantity or batch size. • A type of demand: trade promotion, new product launch, seasonal consumption. • A product build to execute a supply chain strategy. The longer the tail, the more skewed the distribution. Life for a supply chain planner is not as easy as it used to be. What Is a Demand Flow?
- 13. Supply Chain Insights LLC Copyright © 2016, p. 13 Business Pain
- 14. Supply Chain Insights LLC Copyright © 2016, p. 14 Because of Issues Most Companies Use Spreadsheets Twitter Hashtag: #SCIWebinar
- 15. Supply Chain Insights LLC Copyright © 2016, p. 15 Summary • Demand flows through the supply chain. It is a river. • Outside-in processes, reduce demand latency. • Engines should be aligned with flows. • The fit of the engine is a more significant factor to user satisfaction than purchase from the same vendor. • Test and Learn. Focus on outcomes.
- 17. Product brands ADAPTIC™ Non-Adhering Dressings TIELLE™ Silicone Border TIELLE™ Hydropolymer TIELLE™ Non- Adhesive with LIQUALOCK™ Technology V.A.C.ULTA™ Therapy Unit with V.A.C. VERAFLO™ Therapy CELLUTOME™ Epidermal Harvesting System PROMOGRAN™ Collagen / ORC Dressings ABTHERA™ Open Abdomen Negative Pressure Therapy with SENSAT.R.A.C.™ Dressing PREVENA™ Incision Management System ACTIV.A.C.™ Therapy SILVERCEL™ Antimicrobial Alginate Dressings with Silver SNAP™ Therapy System Development and commercialization of innovative healing solutions, including negative pressure wound therapy, negative pressure surgical management, and epidermal harvesting, specializing in advanced devices and advanced wound dressings. Focus BIOSORB™ Gelling Fiber Dressing 17 Advanced Wound Therapeutics
- 18. Support Dillon, MT San Antonio, TX Charlotte, NC Budapest, Hungary Business centers San Antonio, TX Gatwick, UK Manufacturing Athlone, Ireland Gargrave, UK Peer, Belgium Technology centers San Antonio, TX Ferndown, UK Gargrave, UK Our Global Footprint With 5,000 global employees, Acelity offers products in more than 80 countries supported by world-class sales and service organizations around the globe. ©2017 KCI Licensing, Inc., and/or Systagenix Wound Management, Limited. All rights reserved.18 Activities in Gargrave • Product development • Production / Sterilisation • Distribution
- 19. Our planning challenges 19 Dynamic Demand Flows Fast moving and long tail products Mature, volatile and emerging markets Continual innovation and NPI Difficult to capture market intelligence Long timescale to deliver monthly forecast No real input to inventory and production plan to meet service levels Lack of consensus forecast Poor planner productivity due to time spent on data manipulation Planning team second guessing commercial input Cumbersome data capture and reporting tools Limited Tools and Systems Demand Volatility ©2017 KCI Licensing, Inc., and/or Systagenix Wound Management, Limited. All rights reserved.
- 20. Our solution STATISTICAL FORECAST FORECASTING COLLABORATION SALES NPI REGULATORY FINANCE CONSENSUS FORECAST TARGET SERVICE LEVELS INVENTORY OPTIMISATION SAP APO/SNP Historical Demand Forecast & Safety Stock Levels for each location ©2017 KCI Licensing, Inc., and/or Systagenix Wound Management, Limited. All rights reserved.6 ToolsGroup SO99+ DP/Fulfillment
- 24. Traditional Forecast Methods 1. Are adequate at handling fast moving items 2. Do not leverage existing data 3. Cannot take advantage of additional data streams/external inputs
- 25. Traditional Forecast Methods 1. Are adequate at handling fast moving items 2. Do not leverage existing data 3. Cannot take advantage of additional data streams/external inputs
- 26. Normal Distribution SKU/L SalesVolume Anything But Normal The “Long Tail” is Growing
- 27. Volatility (COV) WMAPE Forecast Error % 0 .5 1.0 1.5 2.0 2.5 20 30 40 50 60 70 80+ Opportunity Demand Modeling Traditional Forecasting Tail Items Risk Forecast Error is a Difficult Problem to Manage
- 28. What is Demand Modeling 0 1 2 3 4 5 Demand Demand Modeling is the Science of Calculating Probabilities or Ranges of How Demand Could Occur
- 29. What is Demand Modeling 0 1 2 3 4 5 Demand Forecast Demand Modeling is the Science of Calculating Probabilities or Ranges of How Demand Could Occur
- 30. What is Demand Modeling 0 1 2 3 4 5 Demand Forecast Demand Modeling is the Science of Calculating Probabilities or Ranges of How Demand Could Occur
- 31. What is Demand Modeling 0 1 2 3 4 5 Demand Forecast Demand modeling understands there is inherent uncertainty associated with future demand whether that SKU is a fast mover or a slow mover Demand Modeling is the Science of Calculating Probabilities or Ranges of How Demand Could Occur
- 32. Traditional Forecast Methods 1. Are adequate at handling fast moving items 2. Do not leverage existing data 3. Cannot take advantage of additional data streams/external inputs
- 33. Point of Sale Data Daily Ship-To Daily Ship-From Weekly Shipments Monthly Shipments by Customer Leveraged using the “traditional” approach Detail lost in “traditional” approach Data Leveraged: Traditional vs. Probabilistic
- 34. Why Demand Details Matter Same aggregate historical sales SKU: A SKU: B Traditional
- 35. Why Demand Details Matter Same aggregate historical sales Same forecast result SKU: A SKU: B Traditional
- 36. Why Demand Details Matter SKU: A SKU: B Same aggregate historical sales Traditional Different detailed ordering pattern Probabilistic Same forecast result
- 37. Why Demand Details Matter SKU: A SKU: B Same aggregate historical sales Vastly different forecast certainty Traditional Different detailed ordering pattern Probabilistic Same forecast result
- 38. Traditional Forecast Methods 1. Are adequate at handling fast moving items 2. Do not leverage existing data 3. Cannot take advantage of additional data streams/external inputs
- 39. Probabilistic Forecast Trend, Seasonality, Calendars and Daily Sale Patterns Market Intelligence 3 6 7 Trade Promotion Media Event Effect4 Special Actions and Events 5 New Product Introduction THE DAILY BASELINE DemandInsightIncreasing 1 2 STOCHASTIC MODELING MACHINE LEARNING DEMAND SHAPING PLANNER “Layers” of Demand Modeling
- 40. Stocking Program Growth Program… 0% (% Change YOY) 0 20 40 60 80 100 120 Planning Hours Before After Wayfair Results
- 41. Traditional Forecast Methods 1. Are adequate at handling fast moving items 2. Do not leverage existing data 3. Cannot take advantage of additional data streams/external inputs Why are companies still using the same traditional forecasting methods which have been around for decades to solve the business problems of today?
Hinweis der Redaktion
- Bryan Semple – Vice President Healthcare at ToolsGroup UK. Previously was a customer of Systagenix when I worked in procurement and supply chain for the UK National Health Service Systagenix then became a customer of mine when I joined ToolsGroup in 2012. Systagenix is now part of Acelity, the world’s largest wound care company. Acelity globally supplies more than 20 million advanced wound dressings per month. Revenue (2015) was approximately $1.9B
- Offer a full range of dressings and therapeutic devices for healing severe wounds and preventing them from leading to more serious consequences. We initially worked with the Systagenix company from 2012. Following their acquisition in 2014, we worked with them through their integration process and have expanded the solution to plan all KCI products as well. Some of Acelity’s products are high volume, mature products. However, like many medical devices companies they are continually innovating and introducing new solutions which improve healthcare outcomes. There is therefore a continual change in dynamics of their demand flows and challenges in forecasting demand for new products and end of life products. This is further complicated by variable regulatory approval and clinical trial timescales.
- Faced a challenging supply chain. Products were distributed to (100 countries?) via six regional 3PLs. Some markets, including US, Canada and Western Europe are mature and serviced locally by own sales companies. Feature is regular ordering, often directly by hospitals and healthcare provider groups, relatively stable, although even this can be disturbed by regulatory and other factors.. Other regions including Middle East, Eastern Europe and Asia Pacific are serviced by distributors. Order profile is more variable with potentially large MOQs and irregular order intervals. Emerging markets are often sold on a tender basis – some speculative, very large volumes, potentially disruptive and difficult to predict certainty and timing.
- Following a successful seven month pilot, Systagenix went live with the hosted SaaS version of the ToolsGroup software in July 2013. SO99+ extracts historical demand data from Systagenix’s SAP ERP system to automatically calculate a demand forecast. SO99+ has a unique ability to factor in the demand variability at the order-line level in order to optimize our safety stocks. Next, the forecasts are refined further with input from the commercial, finance team before finally being used to calculate optimized safety stocks based on target service levels. The system forecasts SKU level demand by individual market and then calculates safety stock targets at six 3PL stocking locations across the global supply chain for all 800+ SKUs. Finally, the forecast and these dynamic safety stocks are loaded back into the ERP system, which then executes the planned replenishment actions.
- Reduced time to produce a forecast by SKU from one week to one day. The forecaster’s remaining time is also used much more productively and satisfyingly, to refine the forecasts with input from the commercial team. The second forecaster now supports another part of the business. Visibility of demand and ability to model potential changes to demand (scenario planning) is now possible. Despite targeting higher 99 percent service levels at the 3PL distribution sites, inventory levels have been reduced by up to 15 percent. On this measure alone, the investment paid off in the same year as implementation, allowing us to invest in other areas to maximize global service levels. The monthly global forecast that used to take the two planners an entire week now can be accomplished by one full-time person in a single day, a 10 X improvement in planning productivity A highly successful implementation. This has now also been adopted across the Acelity Woundcare business, which also included integration with KCI’s Oracle ERP and APCS solution
- Picture and role and email
- Cannot handle medium/slow movers at all Requires a deep understanding of statistics and/or a PHD level education to correctly identify a method or algorithm which works for the business “high touches” are required to keep the model running. When planners do have to switch between algorithms because an item changes sales behavior – It adds unnecessary volatility into the model, making inventory and supply planning even more difficult because the forecast is a “moving target” Does not scale well with a company as their complexity increases (forecasting 100 items can be trivial, forecasting 10,000 items can require a team of 20) Weekly/ daily level granularity Includes downstream data (POS, Neilson, IRI, etc) Assumes that all items have a normal distribution of demand and orderlines Prone to overfitting, does not properly predict future behavior Businesses have many experts with a vast array of niche information Stored both systemically and in a business process Loses the ability to merge promotional data, external variables, product introduction, end of life planning, and other signals in one model Is a type of problem that machine learning can leverage - vast amounts of data and complexity which has high impact to forecast error The future of demand planning is rapidly approaching and business needs are ever changing. Companies need to be more agile than ever.
- Cannot handle medium/slow movers at all Requires a deep understanding of statistics and/or a PHD level education to correctly identify a method or algorithm which works for the business “high touches” are required to keep the model running. When planners do have to switch between algorithms because an item changes sales behavior – It adds unnecessary volatility into the model, making inventory and supply planning even more difficult because the forecast is a “moving target” Does not scale well with a company as their complexity increases (forecasting 100 items can be trivial, forecasting 10,000 items can require a team of 20) Weekly/ daily level granularity Includes downstream data (POS, Neilson, IRI, etc) Assumes that all items have a normal distribution of demand and orderlines Prone to overfitting, does not properly predict future behavior Businesses have many experts with a vast array of niche information Stored both systemically and in a business process Loses the ability to merge promotional data, external variables, product introduction, end of life planning, and other signals in one model Is a type of problem that machine learning can leverage - vast amounts of data and complexity which has high impact to forecast error The future of demand planning is rapidly approaching and business needs are ever changing. Companies need to be more agile than ever.
- Why the long tail is getting bigger
- … this is why we need to move towards a demand model, and away from traditional historical level forecasting
- Clear statement that there is a better way of doing this – “new way of modeling”
- Cannot handle medium/slow movers at all Requires a deep understanding of statistics and/or a PHD level education to correctly identify a method or algorithm which works for the business “high touches” are required to keep the model running. When planners do have to switch between algorithms because an item changes sales behavior – It adds unnecessary volatility into the model, making inventory and supply planning even more difficult because the forecast is a “moving target” Does not scale well with a company as their complexity increases (forecasting 100 items can be trivial, forecasting 10,000 items can require a team of 20) Weekly/ daily level granularity Includes downstream data (POS, Neilson, IRI, etc) Assumes that all items have a normal distribution of demand and orderlines Prone to overfitting, does not properly predict future behavior Businesses have many experts with a vast array of niche information Stored both systemically and in a business process Loses the ability to merge promotional data, external variables, product introduction, end of life planning, and other signals in one model Is a type of problem that machine learning can leverage - vast amounts of data and complexity which has high impact to forecast error The future of demand planning is rapidly approaching and business needs are ever changing. Companies need to be more agile than ever.
- Two different items, A and B…
- Results in the same forecast in traditional systems
- However, in probabilistic demand models the devil is in the details Forecast actually changes as well….
- Many small orders result in more certainty than few large orders
- Cannot handle medium/slow movers at all Requires a deep understanding of statistics and/or a PHD level education to correctly identify a method or algorithm which works for the business “high touches” are required to keep the model running. When planners do have to switch between algorithms because an item changes sales behavior – It adds unnecessary volatility into the model, making inventory and supply planning even more difficult because the forecast is a “moving target” Does not scale well with a company as their complexity increases (forecasting 100 items can be trivial, forecasting 10,000 items can require a team of 20) Weekly/ daily level granularity Includes downstream data (POS, Neilson, IRI, etc) Assumes that all items have a normal distribution of demand and orderlines Prone to overfitting, does not properly predict future behavior Businesses have many experts with a vast array of niche information Stored both systemically and in a business process Loses the ability to merge promotional data, external variables, product introduction, end of life planning, and other signals in one model Is a type of problem that machine learning can leverage - vast amounts of data and complexity which has high impact to forecast error The future of demand planning is rapidly approaching and business needs are ever changing. Companies need to be more agile than ever.
- Update logo “Cortana – look for star graph
- Self learning, highly autonomous
- Cannot handle slow movers at all Requires a deep understanding of statistics and/or a PHD level education to correctly identify a method which works for the business “high touches” are required to keep the model running. Switching between algorithms because an item changes sales behavior adds unnecessary volatility into the model, making inventory and supply planning even more difficult Does not scale well with a company as their complexity increases (forecasting 100 items can be trivial, forecasting 10,000 items can require a team of 20) Includes downstream data (POS, Neilson, IRI, etc) Assumes that all items have a normal distribution of demand and orderliness Prone to overfitting, does not properly predict future behavior Businesses have many experts with a vast array of niche information Stored both systemically and in a business process Loses the ability to merge promotional data, external variables, product introduction, end of life planning, and other signals in one model Is a type of problem that machine learning can leverage - vast amounts of data and complexity which has high impact to forecast error The future of demand planning is rapidly approaching and business needs are ever changing. Companies need to be more agile than ever.