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Machine Learning Application Improves Predictive Incident Detection

While they expertly deliver innovation and quality to their customers, Callaway Golf’s previous planning and reporting tool wasn’t making the cut. To evolve their enterprise, the sports giant decided to replace their outdated planning software with SAP Analytics Cloud for planning.

Together with Avvale, Callaway Golf successfully implemented SAP Analytics Cloud across their global enterprise in just 6 months. Now, Callaway Golf has a single, consolidated planning solution that has enabled streamlined expense and asset plans so they can focus on what they do best– revolutionizing the sporting world with cutting edge equipment.

This client is the primary company involved in the management of motorway sections in Italy and their maintenance. With around 3,000 kilometers managed in Italy, it is one of the leading concessionaires for construction and operation of toll motorways in Europe.

The Challenge

At the beginning of 2022, the client engaged Avvale to work on a Proof of Concept on accident rates for a sample of motorway sections with the aim of using Machine Learning algorithms to identify the most impactful variables, such as road surface condition, traffic flow, average vehicle speed, heavy vehicle density, and time slot. Our client needed a tailored solution that could process data and turn it into relevant information to reduce the road accident rate and thus the number of fatalities and injuries on the motorway network. The primary challenge was to manage the massive amount of data on vehicle traffic captured on motorway sections and its processing. The volume and quantity of data managed by our client was very impressive, also the detail and granularity represent a hard challenge. 


The Approach

Avvale accepted the customer's needs by working on the Proof of Concept, achieving outstanding results. This led to the activation of two Minimum Viable Products with an agile approach with the ultimate goal of capitalizing and extending the model to the entire motorway network managed by the customer. In December 2022, an application was released, thanks to a Machine Learning model on AWS, based on probabilistic calculation capable of reporting sections of the motorway network and time intervals potentially significant for accident risk, suggesting the most impactful contributing factors.


The Challenge

Before integrating SAP Analytics Cloud, Callaway Golf's planning landscape centered around SAP's legacy planning solution, Business Planning and Simulation (BPS). While BPS met Callaway Golf's planning needs for many years, the enterprise began to experience limitations with their old solution:

  • Disconnected plans across the board: As Callaway Golf expanded and acquired new brands, planning processes greatly differed across the enterprise with some business units using Excel spreadsheets and others using legacy solutions for planning.
  • Outdated planning functionalities: The existing legacy planning solution lacked many modern functionalities. Among them, salary calculations and asset depreciation automation needed improvement. As a result, it became difficult to create detailed and accurate forecasts across the globe.
  • Significant IT support: The legacy solution required continuous management from IT to maintain runtime. IT had to carve out a full day every week to manually maintain the solution.
  • No single source of truth: Various departments and brands used different data sources, which led to data silos across the organization, making it difficult to gain a clear picture of the enterprise's planned expenses.
  • Rigid user interface: Legacy forecasting system was difficult to navigate and lacked working functions and calculations across the board, such as planning on depreciation and existing assets, adding vendors or members on the fly, and breaking down their cost center expense planning forecasts.

The Approach

With Avvale experts on their side, Callaway Golf laid out the following goals for their new planning landscape with SAP Analytics Cloud:

  • Crowdsource and consolidate plans across the enterprise to gain a global overview of their planned expenses and expand the input capabilities to each responsible owner.
  • Leverage elevated planning functionalities to create faster and more accurate forecast cost center expense and asset reports. The ability to customize calculations enhances and automates the reporting metrics.
  • Harness the power of self-service analytics to alleviate IT from the burden of simple maintenance and data entry requests, so they can focus on high-ROI tasks.
  • Maximize user-driven admin capabilities to control the management of calculations and forms.
  • Create new KPls driven by the business needs.

When SEM-BPS was the center of Callaway Golf's planning landscape, flexibility was a huge factor that was missing. SAP Analytics Cloud contains powerful scenario planning capabilities that help organizations quickly uncover actionable insights to make data-driven decisions. For Callaway Golf, SAP Analytics Cloud's modern planning functionalities provided users with the ability to:

  • Add members on the fly
  • Plan on existing as well as planned assets and automatically calculate the depreciation of values
  • Test "what-if" scenarios for deeper analysis by creating private versions of plans
  • Customize the solution based on their current business process
  • Perform driver-based calculations
  • Empower users to execute end-to-end planning scenarios, without the help of IT

The Impact

Today, our customer has an application active on the entire motorway network and able to process the massive amount of data collected to return relevant information on accident prediction. The evolution of the application developed is aimed to connect it to the world of construction site planning, their signaling and their impact on accidents, and introducing the decay curves of road surface conditions to refine the model and thus the inference of accidents related to these factors. The new application allows customers to process large amounts of data using Machine Learning algorithms to receive relevant information quickly and easily from the entire managed motorway network, thus providing them with a tailor-made solution to meet the growing need to monitor numerous variables to improve their operation and maintenance service.