Predictive Planning Finance Transformation 12 June 2018
Three main pillars drive and increase the maturity of the forecasting process Aligning Financial Strategy, Modelling Finance & Predictive & Operations Analytics Aligning Driver based Strategy, forecasting Finance allows & Operations users to contributes effectively predict to improving and estimate organisational business efficiency, drivers and accountability immediately evaluate and insights. the financial impacts of those decisions. More specifically, the forecasting process should Predictive be part Analytics of an enables integrated companies planning to cycle to produce establish consistent and ensure and well-understood an environment for metrics / successful reports based execution internal of the and strategic external directions information of the organisation. available across the organisation. Both A single help version in understanding of the truth the is market, obtained the and product prevents portfolio workload-intensive new trends, reconciliations and reduce time and endless and cost required non-value to added effectively discussions analyse on validity information, of obtained forecast numbers. outcomes, and drive decision making. By avoiding a manual bottom up approach, forecasting manager can focus on reviewing data and analysing drivers, rather than submitting numbers.
Three main pillars drive and increase the maturity of the forecasting process Financial Modelling & Predictive Analytics In-Memory Aligning Strategy, Computing, Finance Multidimensional Operations OLAP & Cloud & Solutions Driver based forecasting allows users to effectively predict and estimate business drivers and immediately evaluate the financial impacts of those decisions. Predictive Analytics enables companies to produce consistent and well-understood metrics / reports based on internal and external information available across the organisation. Both help in understanding the market, the product portfolio new trends, and reduce time and cost required to effectively analyse information, forecast outcomes, and drive decision making. The Aligning flexibility Strategy, and scalability Finance & of Operations cloud based platforms contributes mean to improving they can organisational serve as a basis for financial, efficiency, commercial accountability as well and as insights. operational planning models. More specifically, the forecasting process In-Memory should be part Computing of an integrated offers the planning possibility cycle to model to establish different and what-if ensure scenarios an environment in real for time and successful immediately execution evaluate of the the strategic final outcome. directions of the organisation. Furthermore, these models can easily be linked and A single work version in seamless of the integration truth is obtained with one and another, prevents enforcing workload-intensive cross-functional reconciliations and endless collaboration. non-value added discussions on validity of obtained numbers. By avoiding a manual bottom up approach, forecasting manager can focus on reviewing data and analysing drivers, rather than submitting numbers.
Three main pillars drive and increase the maturity of the forecasting process Financial Modelling & Predictive Analytics Aligning Strategy, Finance & Operations In-Memory Computing, Multidimensional OLAP & Cloud Solutions Driver based forecasting allows users to effectively predict and estimate business drivers and immediately evaluate the financial impacts of those decisions. Predictive Analytics enables companies to produce consistent and well-understood metrics / reports based on internal and external information available across the organisation. Both help in understanding the market, the product portfolio new trends, and reduce time and cost required to effectively analyse information, forecast outcomes, and drive decision making. By avoiding a manual bottom up approach, forecasting manager can focus on reviewing data and analysing drivers, rather than submitting numbers. Aligning Strategy, Finance & Operations contributes to improving organisational efficiency, accountability and insights. More specifically, the forecasting process should be part of an integrated planning cycle to establish and ensure an environment for successful execution of the strategic directions of the organisation. A single version of the truth is obtained and prevents workload-intensive reconciliations and endless non-value added discussions on validity of obtained numbers. The flexibility and scalability of cloud based platforms mean they can serve as a basis for financial, commercial as well as operational planning models. In-Memory Computing offers the possibility to model different what-if scenarios in real time and immediately evaluate the final outcome. Furthermore, these models can easily be linked and work in seamless integration with one another, enforcing cross-functional collaboration.
Predictive planning overcomes day to day planning hurdles Common planning Model based hurdles forecasting attributes Misaligned Goals Comprehensive Our planning, Engage with budgeting multiple and stakeholders forecasting aligned processes around work corporate too much strategy in siloes, they are not always aligned with our corporate strategy & metrics Uncertainty Around Inputs Customer-Tailored Our executives Not one size do not fits have all drivers any clear pinpointed definition to of your the drivers business in the forecast or the rationale Inconsistent Processes Consistent Because Control we do process not have and standard approach methods variability on how to manage forecasting, the departments compete and all claim to have the best method Inaccuracy Accurate Due to Enable our matrixed transparency business and model, visibility there into is forecast a confusion accuracy in ownership and and mistrust productivity in the planning opportunities processes Excessive FastTime Commitment We spend Shorten too much the forecast time and cycle effort by up on to forecasting 30%, freeing rather Finance than business to deliver partnering analytical insight Inability to Drive Action Dynamic Our forecasts Model multiple have become scenarios a check-the-box on-the-fly and exercise perform and sensitivity is not a analysis process to help lead value-creation and action
Predictive planning overcomes day to day planning hurdles Common planning hurdles Model based forecasting attributes Misaligned Goals Our planning, budgeting and forecasting processes work too much in siloes, they are not always aligned with our corporate strategy & metrics Comprehensive Engage with multiple stakeholders aligned around corporate strategy Uncertainty Around Inputs Our executives do not have any clear definition of the drivers in the forecast or the rationale Customer-Tailored Not one size fits all drivers pinpointed to your business Inconsistent Processes Because we do not have standard methods on how to manage forecasting, the departments compete and all claim to have the best method Consistent Control process and approach variability Inaccuracy Due to our matrixed business model, there is a confusion in ownership and mistrust in the planning processes Accurate Enable transparency and visibility into forecast accuracy and productivity opportunities Excessive Time Commitment We spend too much time and effort on forecasting rather than business partnering Fast Shorten the forecast cycle by up to 30%, freeing Finance to deliver analytical insight Inability to Drive Action Our forecasts have become a check-the-box exercise and is not a process to help lead value-creation and action Dynamic Model multiple scenarios on-the-fly and perform sensitivity analysis
Improving the business partnering activities Moving to a predictive model for sales forecasting will improve insights and free-up time for value adding business partnering activities. Deloitte will help you seize this opportunity. As a business partner finance: Understand the business needs and concerns Anticipates and guide the business Is one of the first person called when there is a crisis Provide value added service and actionable insights Provide the right insight even if it is hard insight Well connected in the business and always stays in close contact with them Deloitte will guide you on the path to improving your business partnering capabilities Role Agree on the broad shift required from Finance towards improving as business partners Moments that Matter Identify moments when Finance should respond and moments that Finance can create Interventions Pinpoint the specific areas where Finance can have a more meaningful impact on the business Improvement Evaluate how to address the challenges in capacity, capability and collaboration for each intervention Moments of impact Create a manifesto that articulates how things will change and what is different
High maturity of statistical models enables proactive decisions making for the business From data to impact What is happening? Why did it happen? What is likely to happen? What should I do about it? Hindsight Insight Foresight Descriptive Diagnostic Predictive Prescriptive Standard analysis Complex analysis Complex analysis including scenarios Complex analysis including scenarios to take decisions Types of analysis Transactional reports Pivot tables Crunchy questions Trend analysis OLAP analysis Price volume analysis Data mining (prediction) Sensitivity / What-if analysis Monte Carlo simulation Determine optimal allocation Target measure distribution What is the performance of my current finance processes and do they satisfy my internal client? The gross margin for this month is 4.3M$, compared with last month this is an increase of 2%. The main increase is coming from the APAC region. To what extend did we realise the strategic objectives by doing several projects and continuously improving processes? The impact of the new elected government has a negative price impact of 3% while the total volumes sold increased over the last months on a certain product segment. What are the leading external indicators that can improve my latest estimate? What volume do I expect to sell over the next 3 years in Africa and what is the country with the biggest growth potential. Given the fact I expect an epidemic in the largest country, what impact could this event have on my planned sales level in 2 years. How should I grow optimally based on different possible scenarios and taking into account company constraints? What is the optimal price I can ask for my product in Germany given the constraint my local production plant still have 20% free capacity. What will be the impact be on the volumes sold in the neighbour countries applying the optimal price
The digital journey leads to the business oriented finance community of tomorrow Foresight Added value Emergence of model based forecasting The Big Shift Emergence of digital transformation era Cognitive computing Cloud based Solutions Business transformation mindset Finance of tomorrow Driver Based modelling Statistical modelling Analytics Robotics Insight Hindsight Double entry book-keeping Manual Accounting Desktop Decentral Data mindset computing org. capture The Shift Performance & profitability tracking Mobile services Big data & analytics Shared service Business Partnering mind-set The current development is exponential the next step has never been larger The gap between the lowest and leading functions has never been larger Lowest quartile performed 1340 1970 1980 1990 2000 2005 2010 2015 2020 2025 Accounting Business partnering Business transformation Activity focus
Selecting the right forecasting methodology We will clearly define the business objectives and how the forecast should provide value helps to select the right mix of approaches Human estimation Driven by expert input while not necessarily relying on a specific (documented) model, potentially involving a consensus component Driver-based Decomposing a (to be predicted) outcome into its underlying operational drivers (e.g. PxQ) to make a better estimate Can be leveraged in a Monte Carlo approach by sampling said drivers from their (assumed) underlying distribution Statistical Using a mathematical approach that based on a set of assumptions (e.g. there is a linear relation ) describes a deterministic relationship between variables often many models are tested to find the best fit (through backtested MAPE calculation) Machine learning Approach to learning from data without relying on pre-defined rules, i.e. algorithms that rely on data-driven discovery to achieve predictive power Rule-driven Data-driven Appropriate method depends on complexity of the (forecasting) problem as well as intended outcome More data-driven means more difficult to understand why questions (black box effect) especially applicable to machine learning Conceptually speaking all are increasing levels of sophistication of similar approach (describing ever more complex relations between ever more variables) In practice a combination (e.g. based on segmentation) will typically be relevant
Selecting the right forecasting methodology We will clearly define the business objectives and how the forecast should provide value helps to select the right mix of approaches Human estimation Driven by expert input while not necessarily relying on a specific (documented) model, potentially involving a consensus component Balance A well balanced mix of driver-based modelling and predictive analytics can leverage the advantages of all forecasting types. Driver-based Decomposing a (to be predicted) outcome into its underlying operational drivers (e.g. PxQ) to make a better estimate Can be leveraged in a Monte Carlo approach by sampling said drivers from their (assumed) underlying distribution Statistical Using a mathematical approach that based on a set of assumptions (e.g. there is a linear relation ) describes a deterministic relationship between variables often many models are tested to find the best fit (through backtested MAPE calculation) Machine learning Approach to learning from data without relying on pre-defined rules, i.e. algorithms that rely on data-driven discovery to achieve predictive power Rule-driven Data-driven Based on the organisational needs and forecasting requirements, the forecasting models use different mixes of the forecasting types, with a made to measure mix for each forecasting need and solution. Increases understanding of new markets, trends and events Reduces time and cost required to analyse information, forecast outcomes and drive decision making Appropriate method depends on complexity of the (forecasting) problem as well as intended outcome More data-driven means more difficult to understand why questions (black box effect) especially applicable to machine learning Conceptually speaking all are increasing levels of sophistication of similar approach (describing ever more complex relations between ever more variables) In practice a combination (e.g. based on segmentation) will typically be relevant Actuals Forecast A B C D
A successful output is a combination of historical data and forward looking expert judgement Iterations with your Finance data and commercial expert will significantly improve model efficiency Evaluate unexpected incidents and expected behaviors and include them in the model setup Eg : Anticipate possible uncertainties in demand landscape, integrate both therapeutic area and product lifecycle characteristics Our flexible methodology incorporates : external inputs and Pharma industry experts inputs, sensing the market and competition, factors in therapeutic areas and product lifecycle considerations
Knowing underlying drivers in order to understand your forecast and the assumptions Market Insight Drivers Finance Drivers Analysis Brand Share Growth Non-Annual Events (Bi/Quadrennial) Volume Share Growth Seasonality / Holiday Shifts Regulatory Changes Price Elasticity Weather (Off Pattern) Net Product Introduction Discounts & Allowances Clawbacks Jan Mar Feb May Apr Updating Volume planning Price planning Gross to Net Sales price planning Planning of budget stretch on S&OP Plan Include the forecast of patents, launches of generics, competitors pricing strategies, Including Customer and Product profitability planning Sales Planning Cross Country Analysis Accuracy and growth Analysis Growth Analysis Price Volume Analysis Pricing analysis Sensitivity analysis Competitor launches Legislation Tracking Understanding Comparing External Data bases (e.g. IMS) as comparison Comparison purposes Comprehensive Engage with multiple stakeholders aligned around corporate strategy Customer-Tailored Not one size fits all drivers pinpointed to your business Consistent Control process and approach variability Accurate Enable transparency and visibility into forecast accuracy and productivity opportunities Fast Shorten the forecast cycle by up to 30%, freeing Finance to deliver analytical insight Dynamic Model multiple scenarios on-the-fly and perform sensitivity analysis
The integration in the integrated business planning Integration of sales forecasting process with the Budgeting & Forecasting (FP&A) process is key to achieving integrated business planning Sales & Operations Planning Financial Budgeting / Forecasting Potential feedback loops Demand planning Supply planning (Net) Sales price planning S&OP output Jan Feb Mar Apr May Volume planning Price planning Gross to Net Sales price planning Planning of budget stretch on S&OP Plan Include the forecast of patents, launches of generics, competitors pricing strategies, Including Customer and Product profitability planning Gross margin Sale forecasting Cross Country Analysis Accuracy and growth Analysis Growth Analysis Price Volume Analysis Pricing analysis Sensitivity analysis COGS planning Operations scenario optimization STD cost Transformation STD cost planning Direct Material STD cost planning Supply Chain STD cost planning Including Labor costs (routing) and direct overhead costs Including BOM s and raw material costs Including costs related to warehousing, storage, (3 rd party) transport etc. OPEX planning CAPEX and Depreciation planning Further integration with various function can exists, e.g. Integration with workforce planning P&L Operating income (EBIT) Capital Employed Cash & Liquidity planning Balance sheet Legend Operations driven Finance driven
The forecasting horizons of the different plans and the relations between, budget and plans Y-1 Y Y+1 Y+2 ( ) Y+5 Y+6 ( ) Y+10 Jan Feb Mar The predictive engine to automate the process and to increase accuracy Dec Annual Budget Plan Mid-Term Plan Adapt drivers and revisit assumptions based on accuracy checks on different levels Long-Term Plan Forecast Annual Budget Mid-Term Plan Long-Term Plan Typically updates the budget made the prior year Depending on changed assumptions or on deviating actuals. Usually this exercise is done once every quarter, with a focus on the year-end of the ongoing fiscal year or in case of a rolling forecast for a fixed number of quarters / months The annual budget can either be combined with the mid-term plan exercise or the third quarterly forecast, considering the overlap in time horizon It can however also serve as an exercise for a very specific purpose, as in setting forth the ambitions and/or cascading targets for the upcoming fiscal year The Mid-Term Plan provides an outlook on the near future It can be particularly relevant for pipeline products or indications reaching their expected launch date in order to correctly anticipate production and supply chain requirements A longer outlook, typically at a less granular level of detail Often as a form of extrapolating the trends from the previous exercise and/or by modeling very specific events and assumptions Considering the uncertainty of some of these events, a risk- or uncertainty adjustment factor can be modelled in to provide a range rather than a fixed number Actuals Forecast* Forecast* (optional) Forecast generated automatically by the predictive model
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