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In the ever-evolving landscape of finance, organizations are constantly on the lookout for powerful planning solutions that can streamline their processes, integrate data seamlessly, and provide actionable insights for effective decision-making. IBM Planning Analytics has emerged as a leading enterprise performance management platform, offering a comprehensive suite of tools and features to design and build advanced models.

In the realm of financial planning and analysis, the key to success often lies in the tools and technologies professionals leverage. IBM Planning Analytics stands out as one such powerful tool that can significantly transform the way finance professionals operate. This article delves into how mastering this platform can be a pivotal moment for any finance expert seeking to optimize planning and analysis processes.

Finance leaders face the challenge of effectively integrating emerging technologies like AI and ML into their planning processes. With the abundance of hype surrounding these technologies, it's crucial to separate the noise from the strategies that truly deliver measurable value. In this blog, we will explore the top five cross-functional AI use cases that Finance should own and drive, focusing on how to begin leveraging AI to produce results. By incorporating these strategic initiatives into the planning process, Finance teams can gain valuable insights and drive outcomes that positively impact the organization's success.

Consensus demand planning is a collaborative approach to demand forecasting that involves input from multiple stakeholders across an organization, and potentially outside the organization. This approach can help to improve forecast accuracy by taking into account a wider range of factors that influence demand.

Sales and Operations Planning (S&OP) is a powerful process that integrates the sales and operations teams of a company to improve planning and collaboration, optimize supply chain management, and drive better business outcomes. In this blog post, we'll discuss the basics of S&OP and its benefits.

The world of business is always changing. To keep up, companies must embrace new technologies and methods to streamline their processes. One area that can be greatly improved with automation is revenue forecasting. Automated revenue forecasting can reduce the amount of time spent on data gathering and analysis, leading to more accurate predictions and a better bottom line. However, automated forecasting with machine learning (ML) can take things one step further by incorporating past data, trends, and other factors that may not be easily observed or quantified. This leads to even more accurate predictions and a better understanding of the business landscape.

Forecasting is a discipline that reduces companies' exposure to risk. The process of forecasting alerts us to upcoming shortages in supply or demand, allowing us to take precautionary measures before we find ourselves in a negative cash-flow scenario. Forecasting is truly a critical business function within any organization. Automating your forecasting process can help you maximize efficiency while reducing costs and improving accuracy. This article will outline how you can use a variety of tools and approaches to automate various aspects of your forecasting process.

It is no secret that analytics and planning projects are based on data. Typically projects involve wrangling some historical datasets, each with their own peculiarities, and then adding in some projection or prediction. Because data is essential, it is also a common stumbling block as business users consider beginning a new analytics and planning project.

QueBIT routinely works with clients that have either selected a poor-performing planning software tool, or who are suffering from the poor implementation of a good planning software tool. Both of these scenarios-- bad software and a bad implementation-- can dramatically hinder the cross-functional planning process and make improvement efforts less impactful and responsive. The challenge for a given client often then comes in recognizing which scenario applies to the situation at hand. Is this client underperforming because their software isn't a good fit? Or is it really about how the software is set up inside that particular company?