Demand Planning Leadership Exchange: 10 Tips for SAP DP | Part 1 Presentation Transcript
DEMAND PLANNING LEADERSHIP EXCHANGEPRESENTS: The web event will begin momentarily with your host: & Guest CommentatorMarch 27th, 2013 plan4demand
Proven SAP Partner “Plan4Demand has consistently put in extra effort to ensure our Griffin More than 500 successful SCP plant consolidation and demand engagements in the past decade. planning projects were successful.” -Scott Strickland, VP Information Systems We‟re known for driving measurable Black & Decker results in tools that are adopted across our client organizations. Our experts have an minimum of 10 years supply chain experience. Our SAP team is deep in both technology and supply chain planning expertise; have managed multiple implementations; have a functional specialty.
3 Session 1 explores decisions that effect the technical design of the your system Guides you toward design aspects that builds a strong DP foundation A phase II redesign for these points will be painful Session 2 deals has a functional slant that will allow you to leverage the application Sets the stage for a successful go-live Guides you on successful forecast performance and process management
4 #1 Demand Planning Hierarchy - Characteristics & Navigational Attributes #2 Dealing with History - Multiple Demand Streams #3: Statistical Forecasting - Demand Pattern Recognition #4: Lifecycle Management #5: Total Forecast Fit for Purpose
5 The Demand Planning Hierarchy definition is the foundation of the Demand Planning Process. It is often a challenge for the demand team to determine what is needed for planning purposes vs. what is needed for reporting or forecast validation For APO DP this means understanding when a data element is a Characteristic vs. a Navigational Attribute. Get the Characteristic definitions wrong and it‟s a costly fix as they are developed/maintained in the Planning Object Structure (i.e. a building block in APO)
6 Demand realignments come into play when considering Characteristics and Navigational Attributes and represent a pain point for APO implementations Characteristics in APO DP must exhibit stability in their values or the use of demand realignments will be needed. A change to a Characteristic‟s value invalidates the use of the original CVCs after a realignment is executed. New historical demand will require a new statistical forecast Companies manage the complexity of this process by realigning every 1- 3 months based on market conditions such as customer mergers/acquisitions. When a Navigational Attributes‟ value changes there is no need for realignment; A Navigational Attribute is linked to a Characteristic and is not part of the key in the database. Example: CPG client required validation of the forecast for 16oz. Beverage Size was configured as a Navigational Attribute and was linked to the Product Characteristic
7 Planning Object Structure is where you specify the Characteristics you want to use for planning purposes. The more Characteristics you define the greater the number of Characteristic Value Combinations (CVC) CVC represents the master data for APO DP Increase in CVCs means more overhead to manage in APO both technically and from a planner perspective The use of Navigation Attributes as an alternative to Characteristics is to help minimize the number of
8 Why Navigational Attributes? Since no CVC is actually created for storage, we reduce the overall Navigational system processing times and then Attributes improve system performance. If you decide to add Navigational Attributes later you do not need to deactivate the Planning Object Structure to do so. The planner will not see the difference between Characteristics Characteristic and Navigational Attributes in Interactive Demand Planning (tcode: SDP94).
9Navigational Attribute Pitfalls Navigational Attributes cannot be used in promotion planning (reference SAP OSS Note 413526) so that must be considered during the design phase. Extensive use of Navigation Attributes leads to a large number of tables in the „join‟ during selections in Interactive Demand Planning and can impact system performance. Note: Use Navigational Attributes to support the forecasting process (i.e. validation); general reporting should be done in the organization‟s BI environment (e.g. SAP Business
10 #1 Demand Planning Hierarchy - Characteristics & Navigational Attributes #2 Dealing with History - Multiple Demand Streams #3: Statistical Forecasting - Demand Pattern Recognition #4: Lifecycle Management #5: Total Forecast Fit for Purpose
11 History used in Demand Planning needs to be viewed as Demand Planning History History Stream Use Case Multiple history streams used to validate forecast assumptions Main purpose for history use will be for the generation of a Statistical Forecast Gain better understanding of historical promotion trade acceptance and sell through
12 What types of Demand History Streams are used in your forecasting system? Answer on your screen Select ALL that apply A. Sales Order History B. Shipment History C. Point of Sale (POS) History D. Other
13Define historical demandstreams, their source andlevel of aggregation neededto support the forecastingprocess What data is available and what most closely maps to your definition of “true demand” ? Is there customer information available like POS or warehouse withdrawal data that better reflects true demand? Is there sufficient historical demand and can this data be consistently delivered to APO
14 Definition of “True Demand” Use of Shipments vs. Sales Orders - Best practice says to use sales orders because shipments only reflect a companies ability to meet unconstrained demand - If you are using shipments check to see if you have access to POS data or other data points to analyze as well - Cut & Ship policies in order management do create overstated sales orders Independent of the demand being used you need to validate that there is sufficient history and that the history can be cleaned for market intelligence supplied by Sales & Marketing
15 Advantages of using POS or warehouse withdrawal data when forecasting in weekly time buckets Week-to-week continuity for POS data which may not be present when using sales orders or shipments at granular levels Customer/consumer focused information which moves the organization to being “demand driven” or “pull driven”
16 Define what Level of Aggregation for inbound historical demand is required to support the forecast process For example, POS might be by Item x Key Account and sales orders by Item x Key Account x Location - Define approach for loading POS if APO DP is defined as Item x Key Account x Location at the lowest level of detail Sales Orders Key 1. Use Sales Orders to Item Location Account calculate proportional factors (Key Figure: APODPDANT) Key 2. Use APODPDANT to Item ??? Account disaggregate POS across Locations POS
17 Promotional Insights History captured with defined promotional information will have many uses Allows for easy cleansing to support forecasting future promotions Allows Demand Planners to better understand timing characteristics for shipments - Early ship – Late ship, reorders, and cannibalization of other products
18 #1 Demand Planning Hierarchy - Characteristics & Navigational Attributes #2 Dealing with History - Multiple Demand Streams #3: Statistical Forecasting - Demand Pattern Recognition #4: Lifecycle Management #5: Total Forecast Fit for Purpose
19 Analyze and understand the historical demand patterns for your product portfolio to enable the best Statistical Forecast at the appropriate level of aggregation Analyze and identify the logical grouping of CVCs that will accurately reflect seasonality/trend and allow for forecast profiles to be streamlined and efficiently implemented A common problem in using APO for statistical forecasting is that planners do not know how the system thinks (e.g. ex-post forecast) In addition, they become overwhelmed because APO has ≈15 forecasting techniques that become ≈ 30 variants (e.g. exponential smoothing with or without alpha optimization)
20 Look for products that are promoted together or exhibit similar seasonality/trend patterns Hint: If the similar demand pattern grouping exhibits seasonality then pick a model that includes seasonality Not necessary to test for seasonality via auto model selection procedures in APO. Week 2011 Sales 2012 Sales Notice the shift in 1 97 101 180 seasonality from 2 3 89 93 98 99 160 one year to the 4 95 85 140 120 next; This data 5 6 147 127 88 110 100 could fail a test for 7 145 125 80 2011 Sales 2012 Sales 8 125 158 seasonality but it 9 76 140 60 is clear 10 11 72 98 76 104 40 seasonality exists 12 85 88 20 0 1 2 3 4 5 6 7 8 9 10 11 12
21 We want to aggregate to smooth through the noise and yet forecast Item the variability in demand that can be estimated Estimate A A We define noise as g Item x Location l random or g l r o unexplained error e c For example, a small g a retailer reducing their a t t Item x Customer x Location e inventory by 5% is e insignificant (i.e. noise) but Wal-Mart doing the same would be a true event.
22 By aggregating Similar Demand Patterns - Total demand 10000 8000 generating a 6000 4000 forecasting 2000 0 disaggregating the forecast we are not Total only improving statistical forecast Similar Demand Patterns 2000 accuracy but also 1500 creating fewer alerts 1000 500 for demand planners 0 to manage Product A Product B Product C Product D
23 Measure Statistical Forecast Error If using regular turn business as definition of baseline demand then still need to measure and analyze statistical forecast error Too often clients do not measure regular turn forecast error because there are no regular turn sales orders in the transactional system This leads to overreacting to what you are seeing (i.e. too subjective) rather than analyzing and synthesizing data Measure the total forecast against the sales orders and then analyze the forecast components (i.e. statistical, Marketing input, Sales input) to gain insights on what may be driving the total forecast error Base Increment Total Sales Statistical al Orders Forecast Forecast Forecast Derive Base Error Total Error
24 #1 Demand Planning Hierarchy - Characteristics & Navigational Attributes #2 Dealing with History - Multiple Demand Streams #3: Statistical Forecasting - Demand Pattern Recognition #4: Lifecycle Management #5: Total Forecast Fit for Purpose
25 Where a product is on its life cycle curve determines the forecasting approach we use Introduction/Growth: Use of Like Modeling & Phase-In Assumptions in conjunction with more reactive or aggressive algorithms Maturity: Fairly stable demand pattern links well to traditional time series techniques Decline: Phasing out of forecasted demand based on assumptions is required (e.g. continue to supply but Highly dependent on industry not manufacture) (e.g. Electronics vs. Food Understanding the forecast process Manufacturer) based on where the product is in the lifecycle and what statistical model is appropriate falls under demand classification
26 Define profile strategy and approach for using product lifecycle management in APO DP Determine how product replacements / transitions are handled by the business and then define approach in APO that aims to balance business scenario coverage with maintenance/overhead of too many profiles Replacement Launching of a New Product Old Product New Product ? New Product x , å, å a x i i Assigning the Like Profile to characteristic values (maintain in the Forecast Profile)
27 For example, CPG client built 12 phase-in/phase- out profiles per demand planner based on analysis of past product replacements / transitions Reduced manual maintenance effort and enabled more focus on improving forecast accuracy Built credibility with Sales/Marketing & Supply Planning because the process was a well managed, stable ramping up and ramping down of forecasted volume
28 #1 Demand Planning Hierarchy - Characteristics & Navigational Attributes #2 Dealing with History - Multiple Demand Streams #3: Statistical Forecasting - Demand Pattern Recognition #4: Lifecycle Management #5: Total Forecast Fit for Purpose
29 Our Demand Planning Process Includes Collaboration and Input from: Answer on your screen A. Supply Chain Only B. Supply Chain and Finance C. Supply Chain, Finance, and Commercial D. Supply Chain, Finance, Commercial, with inputs from Customers, Marketing & Consumer Insights E. No Collaboration Used
30 Define your Total Forecast as a consolidation of the wealth of forecast information available within your organization. Maintain transparency to the Total Forecast definition so everyone understands their accountabilities from design to execution. Define what will make up the Total Forecast by understanding what information is available and who will communicate that information Baseline Statistical Forecast: Should it reflect regular turn business (less promotional activity) or total business? Marketing Activity: Will Promotional Activity, FSI and Advertising volume impacts be communicated by Marketing? Trade Promotion Management: How will we integrate trade promotion information such as products, customers, timing & volume from Sales?
31 Maintaining Transparency Example Core project team needs to have Statistical Base Lift transparency with the wider business Estimates stakeholder team so that during UAT there are no surprises Example of a dysfunctional approach when stakeholders have not been aligned: - Demand Planning: Assumed forecasting regular turn business and promotional activity delivered by Sales & Marketing in a Trade timely manner Promotions Promotional - Marketing: Assumed Demand Planning Calendar was forecasting total demand and looking for exceptions - Sales: Knew timing and high level volume estimates in the short term but Demand Planning wanted the information by Item - Result: There was no alignment in what makes up the total forecast requirements
32 Properly Defining Baseline Demand Define baseline demand as regular turn or total business? You first decide how baseline demand and incremental demand will be managed - Will estimated lift be communicated by Sales/Marketing? - Will the lift cover the entire forecast horizon? - At what level of detail will the lift be communicated? - Will the communication be in a defined, timely and consistent manner ? Is it on the demand planning calendar? If you have a promotionally driven business then baseline demand is typically defined as regular turn business and you negotiate the lift accountability with Sales/Marketing If you are constrained by your ERP system, how do you derive regular turn business when historical demand is stated in either total sales orders or total shipments? - First you clean the total historical demand to create regular turn demand history. - To do this you need help from Sales/Marketing to identify
33 Market Intelligence Integration of Market Intelligence What is Market Intelligence ? - Competitive Intelligence such as competitor pricing strategies - Market Share & Market Trends - Customer Insights such as brand loyalty The challenge is how do we turn this intelligence into a volume estimate or % impact to be integrated into the consensus forecast? - Business Intelligence tools can often support that process - Forecast drivers are often based on business assumptions. Collecting and tracking them is important because the drivers may shift and vary These must always be re-evaluated
34Market Intelligence Example of Highly PromotedProducts: Marketing Forecast Inputs Communicated promotional volume, timing by product / product grouping for next 12-18 months in monthly buckets Captured & tracked strategic planning assumptions became reference point for use in validating that logic was still on target Sales Inputs: Communicated promotional volume, timing by account x product for the next 1-3 months in weekly buckets Captured and tracked sales execution assumptions became reference point for use in validating that logic was still on target Demand Planning had to validate if the communicated forecast was attainable or just reflected sales quotas - Make sure you are not polluting the forecast with bias
35 The CVCs and Navigational Attributes are the foundation of APO DP - Design it right and results will be: System performance gains through fewer CVCs Fewer demand realignments CVCs need to be static master data (slowly changing can be accommodated) Whereas navigational attributes are used with filtering and validating forecast information Finding true demand is a journey best facilitated by considering multiple demand streams to create the baseline demand definition that feeds the generation of the statistical forecast. Use demand pattern recognition to define the appropriate level of aggregation (e.g. better depict seasonal trends) to generate the statistical forecast Improved prediction and a more manageable number of CVCs Fewer forecast alerts, lifecycle profiles, etc. Take into consideration CVCs required to support the integration of Market Intelligence at the appropriate review level.
36 Product lifecycle management in APO DP is a high maintenance business process for the demand team Define a robust profile strategy that is repeatable Define a stage gate approach to the process for scalability. Maintain transparency to the Total Forecast definition so everyone understands their accountabilities from design to execution Minimize go live surprises Produces a higher quality Forecast
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