Advanced forecasting in FP&A: Automation is here and expanding The Dbriefs Driving Enterprise Value series Miles Ewing, Principal, Deloitte

Transcription

1 Advanced forecasting in FP&A: Automation is here and expanding The Dbriefs Driving Enterprise Value series Miles Ewing, Principal, Deloitte Consulting LLP Adrian Tay, Managing Director, Deloitte Consulting LLP Jason Barnes, Partner, Deloitte & Touche LLP Mike Danitz, Senior Manager, Deloitte Consulting LLP August 31, 2017

2 Welcome to the next episode in Driving Enterprise Value Shareholder value Revenue growth Operating margin (after taxes) Asset efficiency Expectations Volume Price realization Selling, general & administrative (SG&A) Cost of goods sold (Cogs) Income taxes Property, plant & Equipment (PP&E) Inventory Receivables & Payables Company strengths External factors Acquire New Customers Retain and Grow Current Customers Leverage Income- Generating Assets Strengthen Pricing Improve Customer Interaction Efficiency Improve Corporate/ Shared Service Efficiency Improve Development & Production Efficiency Improve Logistics & Service Provision Efficiency Improve Income Tax Efficiency Improve PP&E Efficiency Improve Inventory Efficiency Improve Receivables & Payables Efficiency Improve Managerial & Governance Effectiveness Improve Execution Capabilities Product & Service Innovation Product & Service Innovation Cash/Asset Management Demand & Supply Management Marketing & Advertising IT, Telecom & Networking Product Development Logistics & Distribution Income Tax Management Real Estate & Infrastructure Finished Goods Accounts, Notes & Interest Receivable Governance Operational Excellence Marketing & Sales Account Management Price Optimization Sales Real Estate Materials Merchandising Equipment & Systems Work in Process & Raw Materials Accounts, Notes & Interest Payable Business Planning Partnership & Collaboration Retention Customer Service & Support Human Resources Production Service Delivery Program Delivery Relationship Strength Cross-Sell/ Up-Sell Order Fulfillment & Billing Procurement (Excluding Production Materials & Merchandise) Business Performance Management Agility& Flexibility Business Management Strategic Assets Financial Management The above graphic depicts Deloitte s Enterprise Value Map for building and enhancing shareholder value.

3 Topics of discussion Evolving Approaches to FP&A Planning and Forecasting Model Methods and Mechanics Selection, Implementation, and Maintenance Closing Summary

4 Evolving Approaches to FP&A Planning and Forecasting

5 What CFOs say These are huge datasets. The problem is, the hard part is, extrapolating valuable learnings, collaborations, information from that data for use

6 Defining Financial Planning and Analysis (FP&A) A typical FP&A function provides the following services to the organization Budgeting Forecasting Reporting & Analytics Budget Process and Guidelines Budget Preparation Strategic Planning & Target Setting Operational Planning Budget Reporting Procedures & Rules Budget Reporting Framework Forecast Preparation Forecast Planning & Target Setting Forecast Reporting Framework Forecast Reporting Procedures & Rules Business Case Preparation Business Performance Review/Impact Assessment Management Reporting Business Analysis, Modeling, and Decision Support Scorecard/Dashboards Creation KPI Definition & Monitoring Financial Statements Preparation and Approval

7 The 3Cs defined below are key levers to achieving a leading class FP&A function Capacity Getting the core planning, budgeting, forecasting and reporting right Capability Focus on strategic interventions to drive business value Collaboration Earning a seat at the table with the business through effective partnering Standardizing and streamlining transactional activities to create capacity Applying operating model innovation to make Finance cheaper and better Understanding what is most valued by the business (e.g. what types of insights help drive top and/or bottom line growth) Being proactive and innovative to impact performance Leveraging digital and technology to change the way work is performed Structuring Finance to align with business decision makers / commercial operations Recruiting, developing and retaining the brightest talent Advanced planning and forecasting enhances each of the 3Cs

8 FP&A challenges with planning and forecasting Misaligned goals Planning, budgeting, and forecasting processes are in siloes, and do not align with corporate strategy & metrics Uncertainty around inputs Executives do not have clear visibility on the drivers of the forecast or the rationale Inconsistent processes Without standard methods to manage planning and forecasting, models proliferate across the organization Inaccuracy Plans and forecasts are inaccurate Excessive time commitment Significant time and effort are spent on forecasting rather than business partnering Inability to drive action Planning and forecasting are a check-the-box exercise and do not drive actionable tasks

9 Rise of advanced planning and forecasting Advanced Planning and Forecasting combines statistical techniques with automated platforms and data visualization to empower the FP&A organization to identify insights and trends ADVANCED PLANNING & FORECASTING Forecasted financial Econometric and statements driver based models Full-financial statement Platform leverages forecasting provides a advanced data comprehensive picture science techniques to of financial health model a holistic (Profit and Loss, picture of the Balance Sheet, Cash business environment Flow) Visual analytics and CFO dashboards More robust and intuitive visualizations providing visibility into key business drivers Dynamic scenario planning An integrated approach enabling scenario planning and analysis to expedite decisions in changing market conditions Improved accuracy, increased efficiency via automation, deeper insights

10 Characteristics of advanced planning and forecasting More organizations are leveraging data science and improved computing power to transform traditionally manual intensive planning and forecasting processes (Machines + humans) > humans Data aided planning reduces human biases Incorporation of key drivers into the planning approach helps increase transparency, capture institutional knowledge Exception based planning places focus on the most significant items Highly repeatable Proliferation of data enhances forecasting accuracy and allows for more complex analysis

11 Advanced planning and forecasting model use cases Advanced Planning and Forecasting models can be used to achieve specific outcomes the top 3 use cases are outlined below Use cases Description Prime candidate Top of the house model Corporate level model that is used for strategic scenario models, target setting, and validation of business unit forecasts Organizations looking to: Turn around forecasts very frequently Create guardrails to compare bottom up numbers with Generate high level forecasts and simulations Baseline model Business unit or product level predictive model that creates a baseline plan at forecast at the granular business unit level and cascades to lowest level Organizations looking to: Generate first pass statistically generated baselines Have users focus on adjustments and exceptions to statistical forecast Generate bottom up planning and forecast level of detail Specialized model Specialized predictive models used for targeted and strategic forecasting, demand planning for sales forecasting, risk forecasting for R&D expense, etc. Organizations looking to: Focus on specific, highly complex P&L line items E.g. New product and SKU revenue forecasting

12 Advanced planning and forecasting models are helping companies address and solve common forecasting issues Top of the house model Baseline model Specialized model Telecom holding company Consumer products company Technology company Issue Corporate FP&A team wanted a reliable, driver based forecast to validate the projections provided by the local markets and also help align on a single version of the truth between corporate and the business units Company needed better business insights and understanding of variance drivers across the organization. In addition, the company wanted to improve its bottom up plan and forecast, accuracy and integrate P&L, Balance Sheet and Cash Flow forecasting Company wanted to increase accuracy and decrease variability of quota setting targets, as well as reduce the excessive time commitment spent on quota planning processes Solution and impact Determined key operational drivers, developed statistical, driver based models to forecast the P&L, and improved the forecasts with a dashboard of key metrics to enhance business value/insights, enhancing forecast alignment between corporate and the local markets Identified key drivers through driver analysis and leveraged statistical regression and driverbased modeling to understand business trends with significantly less effort and iteration than previous forecast approaches, achieving ~99% accuracy in full-unit sales forecasting Embedded predictive analytics model in the company s finance and quota planning and in-period sales performance tracking, fundamentally changing the quota planning process and improving quota targets setting accuracy and decreased variability

13 Poll question #1 How does your company predominantly use advanced modeling as part of the overall planning process? A. Top of the house model B. Baseline model C. Specialized model D. All of the above E. Don t use advanced models F. Don t know/not applicable

14 Methods and mechanics

15 What CFOs say There weren t nearly enough people who knew how to deal with big data and what to do with it. We needed those people not only in IT, but also in internal audit and FP&A. That s a skill set that is very much lacking that s going to be very important in the next five years.

16 Advanced planning and forecasting methods Models are constructed using multiple methods, with varying degrees of complexity and sophistication Increasing complexity/sophistication Input based Direct input by the business Example: Forecasting Legal expenses Driver based Key business drivers (e.g. unit volume, headcount) are used in the form of volume*rate equations Example: Forecasting Payroll expenses Focus of section Statistically based (Common) Statistical methods are applied on historical data to identify best fit equations for planning and forecasting Example: Revenue forecasting Statistically based (Sophisticated) Deep learning and self learning methods are used to identify complex relationships. Unstructured data is combined with structured data. Example: Revenue forecasting that incorporates competitor actions

17 Advanced planning and forecasting methods (cont.) Methods should be carefully applied to specific financial line items based upon its characteristics Examples Revenue plan Method: Statistical Based (Common) A larger, more mature country sources independent variable data and inputs into the system. Depending on the type of variables and amount of history, the system will determine the statistical technique to project the dependent variable. COGS plan Method: Statistical Based (Sophisticated) Machine scans various sources of standard costing expense data, analyzing historical trends, monitoring external reports, and identifying key cost drivers through an automated periodic refresh, generating a standard cost Method: Driver and Input Based OPEX plan Existing headcount and employee salary data is sourced from the HR system directly into the financial system and system uses this data to calculate the base salary payroll forecast, the employee benefits forecast is uploaded via input form

18 Common statistical based method overview The most commonly used (not comprehensive) statistical based techniques for advance planning and forecasting models are reflected below. Technique Description Linear regression Models the relationship between a dependent variable (y) and one or more explanatory variables (x) Time series Time series model that regresses a variable against its own lagged (prior) value in order to predict future trends Dynamic regression Similar to time series but incorporates one or more explanatory variables to help improve predictive capabilities Exponential smoothing Uses statistical filters to smooth historical data. Apply additional weighting to more recent data points and less weight to older ones

19 Sophisticated statistical based method overview More sophisticated statistical based techniques go beyond the single-iteration analysis of structured data and focus on learning from historical data Methodology Decision trees Description Methods that break down data into smaller and smaller subsets to identify a homogenous group. Each data elements potential values is a fork in the tree, and the paths taken for each unique record is different. Ensemble Models that create multiple models and aggregate the results to produce a stronger result (gradient boosting, random forests). Helps avoid generating false positive results from overfitting. Deep learning Models built as a series of interconnected processing nodes across layers which each evaluate data and pass on to neighboring nodes to match patterns and understand information (neural networks)

20 Poll question #2 What s the most complex method your company uses to forecast today? A. Input Based B. Driver Based C. Statistical Based (Common) D. Statistical Based (Sophisticated) E. None of the Above F. Don t know/not applicable

21 Selection, implementation, and maintenance

22 Selecting and implementing an advanced planning and forecasting model Define Develop Deploy Purpose Define business issue Understand analytical needs Understand data availability Depict results in wireframe Define & consolidate relevant data and influence factors Select the appropriate model(s) Translate requirements into model Review initial prototype approach and results Expand/refine hypotheses based on more data Test and iterate on model Final testing of the model Presentation of results and approval from stakeholders Integration with existing end to end planning and forecasting processes Activities Conduct Interactive Showcases and Workshops Refine model & data Study results Retest model Iteratively develop model Conduct final workshops on visualization & use Outputs Data requirements, use cases Algorithms, Prototypes, Test Results Final model

23 Key considerations when implementing advanced planning and forecasting models Uncover your data s faults Lay the groundwork Encourage group coordination Improve through automation Incentivize managers to act on insight Use understandable data Clean your data

24 Poll question #3 What do you see as the largest barrier within your organization to implement these advanced forecasting techniques? A. Talent B. Data C. Technological Capabilities D. Adoption/Change Management E. All of the above F. I do not see any barriers to adoption G. Don t know/not applicable

25 Key talent and organization considerations Get the right people and processes in place to support advanced planning and forecasting models Are we ready to implement predictive forecasting? Do we have the right people to maintain and mature these models? Do we properly understand the data we collect? Do we have the right people? What types of systems manage the source data? Do we generate an inconsistent level of forecasting detail? Are our forecasting processes manually intensive? Do we have systems that can provide the right data? Are we spending too much time on data manipulation? Is our data mature enough? Are we ready to act on the insights? Do we have people with a desire and ability to understand these models? Do we have employees with the right background/systems knowledge? Are these employees able to think at the macro level? Are managers empowered to make decisions based on data? Will employees provide cross functional input to help refine models?

26 Poll question #4 How much time until you can see your company utilizing one of the advanced forecasting methods discussed today? A. Next 6 12 months B. 1 2 years C. Probably not until the next 5 years D. Never E. Already use advanced forecasting methods today F. Don t know/not applicable

27 Summing it up Advanced planning and forecasting in action Assessing the best forecasting technique Analyzing the fit and sensitivity of key variables / drivers Applying statistical forecasting techniques on the P&L

28 Question and answer

29 Join us September 27 at 3 p.m. ET as our Driving Enterprise Value series presents: Thriving in uncertainty: The CFO s margin improvement playbook in a digital world

30 Eligible viewers may now download CPE certificates. Click the CPE icon in the dock at the bottom of your screen. CPE

31 Contact information Miles Ewing Principal Deloitte Consulting LLP Adrian Tay Managing Director Deloitte Consulting LLP Connect with me on LinkedIn Connect with me on LinkedIn Mike Danitz Senior Manager Deloitte LLP Jason Barnes Partner Deloitte & Touche LLP Connect with me on LinkedIn Connect with me on LinkedIn

32 This presentation contains general information only and Deloitte is not, by means of this presentation, rendering accounting, business, financial, investment, legal, tax, or other professional advice or services. This presentation is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision or taking any action that may affect your business, you should consult a qualified professional advisor. Deloitte shall not be responsible for any loss sustained by any person who relies on this presentation.

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