Customer Success Story: Streamlining Workforce Planning for a regional banking institution
Customer :
Regional credit union.
Challenges :
A regional credit union was planning a major expansion, opening multiple new branches across different regions. Leadership recognized that accurate headcount forecasting and allocation were critical to successful launches and sustained growth. However, their workforce planning process was manual and lacked the integration needed to incorporate essential data points—such as role requirements, language skills, and transaction volumes. They needed a holistic solution to gauge workforce demand and determine the right mix of positions, skills, and staffing levels across various locations.
Solution :
The institution implemented Adaptive Workforce Planning to address these challenges. The solution pulled data from multiple sources including:
- Workday HCM: Provided headcount, job/position details, and skill data (e.g., bilingual Spanish skills).
- Workday Time Tracking: Offered insights into regular hours worked, overtime, and PTO usage.
- Third-Party Operational System: Captured key branch metrics, such as the number of transactions by location and the split between teller and transactional tasks.
Using this integrated data, the credit union built advanced workforce models to forecast staffing needs for new branch openings. These models factored in projected transaction volumes, local language requirements, and existing employee availability. The solution also enabled the team to plan for the reallocation of staff between branches when unforeseen gaps or spikes in demand arose.
Results :
- Data-Driven Forecasting: By consolidating workforce, time, and operational data, the institution gained a clear, real-time view of branch staffing needs, allowing them to make informed decisions about how many FTEs to hire or reassign.
- Optimized Role and Skill Mix: The new solution ensured each branch was staffed with the appropriate skill sets, such as bilingual capabilities, to meet community needs and improve customer service.
- Increased Efficiency: Automated workforce planning replaced manual processes, significantly reducing the time spent gathering and reconciling data, and enabling the team to focus on strategic decision-making.
- Agile Resource Allocation: With the ability to identify staffing surpluses or shortages across locations, the institution can quickly redeploy team members where they are most needed, improving overall service levels and cost efficiency.
Conclusion :
By implementing an Adaptive Workforce Planning solution, the credit union transformed its ability to manage a growing network of branches. The integrated approach—leveraging data from multiple systems—provided precise, actionable insights into headcount requirements, role definitions, and skill distributions. As a result, they are now well-positioned to support future expansion, improve customer experiences, and maintain operational excellence in an ever-changing financial services landscape.
Customer Success Story: Demand Forecasting using AI/ML
Customer :
Auto services retailer with stores nationwide.
Challenges :
A national retailer faced significant hurdles in accurately predicting store guest counts and the corresponding impact on Gross Sales, COGS, Net Sales, and Labor Hours. Their existing manual approach—primarily using spreadsheets—was time-consuming, prone to human error, and unable to incorporate complex data elements like inclement weather. Furthermore, they required an advanced forecasting model that could handle large volumes of data, including historical guest counts at 30-minute intervals and external data, to better understand the influence of external factors on store performance. No off-the-shelf solution met these highly specific requirements without extensive customization.
Solution :
To address these challenges, the retailer implemented Workday’s Demand Planning solution within Workday Adaptive Planning powered by AI/ML algorithms. The solution combines historical data from the customer’s Point of Sale system with historical and predicted independent variables, in this instance, leveraging predicted temperature data as a regressor to forecast future guest counts. By structuring data into cube sheets—including 30-minute increments—and creating a separate modeled sheet for weather inputs, they established a robust predictive framework.
Product mix assumptions (Product Factor) were then applied to the guest count ML forecasts, allowing the retailer to calculate volume. Which in turn drives Gross Sales, COGS, Net Sales, and Labor Hours. Additionally, Merge Cube sheets were leveraged to seamlessly consolidate different dimensions, eliminating the need for complex triggers and ensuring a streamlined data flow across various forecasting components.
Results :
This inaugural use of Workday’s Demand Planning, incorporating emerging machine learning capabilities, delivered transformative outcomes:
By integrating historical guest counts, regressor data, and product mix factors, the retailer gained a far more precise and nuanced forecast.
The previously manual, spreadsheet-based process was replaced by automated workflows, freeing teams to focus on higher-value activities such as strategy and analysis.
The organization’s unique data elements were successfully incorporated, offering a fully customized solution without the extensive development required by alternative products.
The retailer can now predict the financial impact of external factors like severe weather, enabling proactive resource allocation and store operation adjustments.
Conclusion :
By adopting Workday’s Demand Planning solution, this retailer achieved a significant leap forward in forecasting sophistication and operational efficiency. The integration of multiple data streams—ranging from historical guest counts to predictive temperature models—empowered them to plan for seasonal shifts and external disruptions more accurately than ever before. The success of this adaptive implementation underscores the power of machine learning-driven forecasting to tackle complex, mission-critical business challenges, paving the way for continued innovation and growth.
Customer Success Story: Streamlining Supplier Invoice Processing for a Growing Gaming Company
Customer :
A fast-growing gaming platform with a robust user base and an expanding team of developers.
Challenges :
As the company experienced significant growth, their finance team struggled with inefficiencies in supplier invoice processing. These invoices, typically for third-party developers and contractors, were manually tracked using Excel spreadsheets and other isolated data sources. The manual process was slow, prone to errors, and difficult to scale as the number of invoices increased.
With the growth in demand for new game development and frequent updates, the finance team quickly realized that the current process could no longer support their operational needs. The result was delayed payments to developers, increased risk of errors, and a bottleneck in financial workflows that slowed down their ability to keep up with project timelines.
Solution :
The company turned to Workday Adaptive Planning to resolve these challenges. By integrating multiple data sources—including the manual Excel files, third-party systems, and Workday Financials—into Workday Adaptive, we created a seamless flow of data that automatically calculated supplier invoices.
Workday Adaptive served as the calculation engine, pulling in data from the various sources, and used its robust planning capabilities to perform the necessary computations. Once the invoices were generated, the results were outputted back into Workday Financials, which automated the processing and ensured that all supplier payments were handled efficiently.
The integration allowed the finance team to move from a cumbersome, error-prone manual process to a streamlined, automated one. Workday Adaptive became the central hub for managing invoice calculations, which connected seamlessly to Workday Financials for final processing.
Results :
The solution delivered substantial improvements in invoice processing times. With Workday Adaptive Planning automating the generation of supplier invoices, the finance team was able to reduce invoice processing time by more than 50%, freeing up valuable time and resources. The automated workflow minimized errors, improving accuracy and reducing manual oversight.
The company now has a fully integrated financial process, allowing them to scale as their operations expand. The improved efficiency means faster payments to developers, better relationships with external partners, and more time for the finance team to focus on higher-value activities like strategic planning and financial analysis.
In addition, the ability to leverage real-time data from Workday Financials and third-party sources has enabled the company to make more data-driven decisions and improve financial visibility.
Conclusion :
By implementing Workday Adaptive Planning, this rapidly growing gaming company not only solved their immediate challenges with invoice processing but also set themselves up for continued growth and financial efficiency. Through a collaborative partnership, Workday Adaptive helped them transform their financial processes and scale efficiently, all while ensuring that developers were paid on time—boosting operational productivity and driving value across the business.
