Driving a new age of connected planning through Optimisation, Machine Learning and Artificial Intelligence in the Supply Chain

Transcription

1 Driving a new age of connected planning through Optimisation, Machine Learning and Artificial Intelligence in the Supply Chain

2 Platform A cloud Planning & Analytics platform Apps upon which Anaplan and our Partners have built hundreds of Apps People that connect people and data unlike any technology ever has Planning, Budgeting, and Forecasting Workforce Workforce Planning Workforce Optimisation Onboarding ESOP & Equity Comp Predictive Labor Modeling IT Agile Implementation Corporate Strategic Planning Real Estate Management Zero Based Budgeting Project Capacity Analysis Shared Services Allocations S&OP Demand & Supply Planning Sourcing Optimisation Anaplan is Finance The Connected Planning Platform Supply Chain Revenue Modeling Financial Consolidations Product Profitability Analysis Sales Territory and Quota Planning Sales Forecasting Channel Mgt Budgeting Incentive Compensation Marketing Marketing Performance Mgt Trade and Promotion Planning Customer Account Planner Production Capacity Planning Inventory Optimisation Materials Costing & Profitability Account Segmentation/Scoring

3 Anaplan s Unique Planning Technology 2018 LEADER 2017 LEADER 2017 NICHE 2016 NICHE 2016 HYPE CYCLE LATEST MARKET GUIDE Sales Performance Management Magic Quadrant Strategic Corporate Performance Management Magic Quadrant Financial Corporate Performance Management Magic Quadrant Sales & Operations Planning Systems of Differentiation Magic Quadrant Human Capital Management Software, Workforce Planning & Modeling Trade Promotion Management and Optimisation 2018 Best Corporate Performance Management

4 Empower business people to own plans Your models Your data Your hierarchies Your versions Your time periods Your dashboards Your drivers and methods Your formulas Choice of 140+ functions Leverage 200 apps

5 Dynamically scale Business value Billions of cells Thousands of users Hundreds of models

6 Fast Time to Deliver with Low IT Involvement Anaplan Projects are delivered in weeks, not months 6 Weeks FP&A Aviva Insurance 14 Weeks Open to Buy Carter s 15 Weeks FP&A, Consolidations Fanatics 8 Weeks BOM based costing Del Monte I was looking for a platform that was flexible, fast, collaborative, could scale, was business owned, and users had to like I was looking for a unicorn that I didn t think existed and then I found Anaplan. Anaplan is that unicorn. Sarah Park, VP Business Development

7 Supply Chains can be complex

8 Connected Supply Chain Solutions Solution Description Supply Chain Finance Sales Demand Driven S&OP Drives top- and bottom-line via tradeoff and scenario analyses over multiple planning horizons with near-time demand signals and optimisation across all functions Supply Planning Capacity Planning S&OP What-If / Scenario Analysis Promotion Planning COGS Planning OpEx Planning Workforce Planning Operations Planning Rev./Mktg. Planning Sales Planning & Forecasting Demand Collaboration Customer and channel collaboration across multiple dimensions with accessible, real-time sharing of assumptions and forecasts Demand Planning Promotion Planning Demand Forecasting Inventory & Service Policy Analysis Rev./Mktg. Planning AOP Sales Planning & Forecasting Account Seg. Territory Planning Sales Coverage Product Portfolio Planning Rapid, distributed approach to identify and capitalize on profitable product opportunities NPI/EOL/Cannibalization Brand/Platform Planning Demand Forecasting Financial Insights Product Costing R&D Planning Sales Insights Sales Forecasting Order Profitability Near-time what-if support for cost versus lead time and promotion versus service level decisions Inventory Planning Distribution Planning Promotion Planning Policy Analysis What-if Analysis OpEx Planning COGS Planning Rev./Mktg. Planning Operations Planning AOP - Cost to Serve Management Transparent accountability to drive continuous cost improvement Supply Planning Inventory Planning OpEx Planning Operations Planning Workforce Planning ZBB -

9 Connected Enterprise Connected Network Anaplan Intelligence Connecting the Network Optimisation & Solver Process Workflow

10 What is optimisation? Optimisation is a mathematical method applied to business needs in order to determine an optimized goal. It enables businesses to maximize goals AND/OR minimize costs.

11 Common Optimisation Problems Production Production Scheduling Machine Allocation Maintenance Planning Investment Portfolio Optimisation Fund Cloning Bond Management Distribution Network Design LTL Loading Fuel-use Minimization Purchasing Inventory Optimisation Vendor Selection Shipment Planning Human Resources Workforce Scheduling Office Assignment Other Applications Combinatorial Auctions Catalog Layout Advertisement Selection

12 Optimisation Problem Definition Objective Variable Constraint Maximize or minimize the value of some function, F(x 1,x 2 x n )or Determine Feasibility e.g., maximize profit Component of Function that can be changed. e.g., unit volume Constraints on individual x s and/or combinations of x s e.g., production capacity

13 y = 2x - 8 y = x + 4 Linear Programming Visualization Feasible Region y = -0.25x + 6 y = -0.5x + 7 y = -0.5x + 3

14 Flexible optimisation usually requires programming background

15 Delivered Technology in Anaplan UI Driven Model Based Fast Calculation Problem definition relies on a simple, Anaplan standard UI to give more flexibility to users. Using only model based line items and formulas, the Optimiser does not require any specific modeling skills. Gurobi is one of the fastest optimisation engines on the market. Because the operation locks the model, it s a prerequisite for the Optimiser to be fast.

16 Gurobi is on the Anaplan platform Anaplan Core Anaplan Optimiser High Speed Transfer Result Data Optimisation

17 Anaplan + Gurobi Optimisation greatly accelerate the planning process! A Typical Planning Cycle for a Decision Service Input Collection Execution Plan Creation Communication Anaplan enables streamlined Data Collection and Communication Mathematical Optimisation accelerates and improves Plan Creation +

18 Requirement by Category Advanced Statistical Forecasting Transportation Optimisation Workforce Optimisation Supply Network Planning Production Scheduling Price & Promotions Optimisation Order Promising (GATP) Resource & Capacity Allocation Optimisation & Advanced Analytics Use Cases Aligning forecast components to drive highest accuracy. Provide forecasters with confidence intervals to help assess risk Zero-based forecasting techniques for high lifecycle turnover products Minimization of transportation costs Lower total cost to deliver (a component of total cost to serve) Automated, capacity constrained shipment assignments Labor Optimisation by site, qualification, etc Staffing new production locations Work scheduling for all employees by day, hour, etc Right-size labor, management and leadership Optimal allocation of available supply across the distribution chain Inventory Optimisation, considering opportunity cost and cost to serve Asset Footprinting Detailed, time phased scheduling Arrangement of process orders for execution Demand shaping Inventory reduction Channel pricing sensitivity Available to Promise / Global Available to Promise including capable to produce/procure Constraint-based RCCP Scheduling constraints Optimal allocations for maximum profitability Optimisation & Solver

19 Optimisation with Anaplan Full Anaplan core support UI driven no coding required Optimal Feasible problem Linear problem only

20 Connected Network Planning Machine Learning Algorithms Optimisation & Integrated Solver highly autonomous continuously learning decision making business network

21 Opportunities with technology

22 Which SCM use cases to consider to apply ML? Realized Gain $ Optimize Production Inventory & Working Capital Product Launch Optimize pricing Trade Promotion Supply Chain Demand Sensing Predictive Maintenance % Improvements over Statistical Models

23 Demand Forecasting

24 Demand Forecasting - Traditional Sales Volume Time Series Data Statistical Model Forecast

25 How does the ML forecasting model work? Inputs Model Forecast Seasonal Trends Forecast Transaction Data Sales Volume Demographics Linear Regression Linear Classification Gradient Boosted Trees Deep Neural Networks Wide and Deep Neural Network Promotional Data Recurrent Neural Network

26 Supply Chain Planning Customers Sales & Operations Planning End to End Supply Chain Planning Supply Planning Operations Demand Planning Trade Promotion Merchandise Planning

27 Innovation Planning (NPI, Collaboration, Capacity Planning) Profile Largest Beverage Company in the World 130 Years Old Headquartered in Atlanta, GA 1.9B Daily Servings in 200+ Countries $46B Net Operating Revenues with $184B Market Cap 23M+ Retail Customer Outlets 7,000 Associates, 900 Plants, and ~250 Bottlling Partners Current State Manual Spreadsheet Calculations in Excel Distributed and collected via Inconsistent Method Minimal Standardization Either too high or too low detail Disconnected Assumptions throughout process

28 Innovation Planning (NPI, Collaboration, Capacity Planning) Future State Automated All in One Tool All Data Sharing through the tool Consistent Method More Standardization Lowest level data in the tool à roll ups to appropriate levels Fully Connected Connected from S&OP to Ops Forecast Communication through tool and Why Anaplan? Collaboration with multiple entities Speed of Calculation-large data sets Intuitive and easy for end-users Flexible Modeling Workflow management among multiple user groups Tracking changes and assumptions documentation Multiple Use Cases across various functions in Organisation CPG Presence and Growth

29 Supply Planning Consumer goods and retail Major American manufacturer and retailer of children's apparel Founded in 1865 Sales of $3.1B 654 locations Convoluted sourcing rules known by one resource in an antiquated Access database Wanted to compute a weekly buy for 250K SKUs (1.2B cells) end to end in less than 10 minutes

30 Supply Planning Solution Scalable, robust, and intuitive solution to calculate buy quantities and make adjustments based on sourcing rules to support the buy/forecast/capacity planning processes 16-week implementation Connected Plans Merchandise & Assortment Planning Results 7 days of inventory saved (or $15M per buy, $30M annually) Reduced forecast process from 3+ days to 2.5 days Reduced planner s work in building buy from 4-5 hours to under one hour Buy model takes less than 10 minutes compared to hours in their previous process More time to analyze output and identify any potential issues prior to submitting

31 Supply Planning