Helping clients meet their business challenges begins with an in-depth understanding of the industries in which they work. That’s why KPMG LLP established its industry-driven structure. In fact, KPMG LLP was the first of the Big Four firms to organize itself along the same industry lines as clients.

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6 Industry use cases for intelligent forecasting

Learn about the real-world use cases of intelligent forecasting

Intelligent forecasting: Real world use cases

Most companies understand the potential for enhanced data and predictive analytics to significantly improve business planning with more intelligent—and valuable—forecast insights. But, understanding that potential is not the challenge. The real question is: Where do I start?

We help our clients solve this obstacle with a step-by-step approach that demystifies the process and allows them to establish a clear roadmap that can evolve with their needs and capabilities.  This proven approach starts with a few high-value forecasts that can demonstrate results quickly, while also pointing the way for steady expansion across the enterprise as you establish the optimal mix of data and predictive models that works best for your company.

This approach is essential to getting that mix right because each company’s relevant data and business drivers are highly unique. 

Here are some real-world use cases—which represent some first steps we have helped companies take on their journey to intelligent forecasting.

Industry: Consumer and Retail

  • Automated the core enterprise revenue forecast and separate product-level roll-ups using a suite of integrated predictive models
  • Results included: 30 percent increase in accuracy, significant reduction in staff time, and enhanced confidence in business planning on an 18-month view

Industry: Energy

Supply chain: Market demand planning

  • Improved accuracy, time horizon and trust in demand planning for the overall market of a chemical company’s key product
  • Tested the new data across dozens of model types to determine the optimal combination
  • Delivered a 70 percent reduction in error rate on demand planning
  • Identified powerful new business drivers

Industry: Financial Services

  • Improved scenario-modeling of optimal locations for new retail locations
  • Deployed machine learning models to estimate the two-year demand at any new location
  • Improved sales accuracy by 2X
  • Added increased visibility into areas like labor scheduling, intra-day forecasting and demand planning

Industry: Healthcare and life sciences

Finance: Gross profit and net income

  • Developed bespoke models for each aspect of the client’s P&L to automatically generate monthly gross profit and net income forecasts for the next year
  • Implemented models into client’s dashboard software to allow for scenario adjustments
  • Improved accuracy in net sales and cost of revenue of 75% and 26%, respectively

Industry: Technology

Finance: Regional and product category sales volume

  • Enhanced the accuracy of an IoT-based forecast model using advanced analysis and statistical testing techniques
  • Identified a dozen key weaknesses and new enhancements for the model framework
  • Improved accuracy by 70–80 percent
  • Established an ML-based process to monitor data anomalies going forward

Finance: Sales revenue

  • Eliminated a time-draining data bottleneck between sales pipeline and order ledger models with an integrated model framework
  • Increased sales forecast accuracy, speed and confidence a full year out
  • Significant reduction in time spent on manual bottom-up analysis

Finance: Operating expenses

  • Developed and deployed a fully ML-driven system for forecasting all PO expenses
  • PO spend was forecasted daily using multiple predictive algorithms, with a unique algorithm for each unique forecast based on accuracy testing
  • Built trust with users in the ML forecasts through training, and ultimately reduced time on manual validation

Industry: Telecommunications

CRM: Subscriber churn

  • Improved customer churn forecasting at a hyper-local geographic level
  • Deployed an optimized model into a managed signals repository
  • Delivered a 70 percent reduction in error rate on demand planning
  • New approach allows company to continuously monitor subscriber health and forecast growth/churn by market on a weekly basis

How KPMG can help

KPMG has worked with a wide variety of clients to transform their business planning and forecasting across multiple functions and time horizons. By bringing deep digital transformation expertise and a signature suite of intelligent forecasting services that spans project architecture, data and signals, and world-class data science and business talent, KPMG delivers leading digital solutions that unlock lasting value.

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