This is the last (for now) in a series of blog posts about whether ChatGPT can help us with Planning Analytics (PA) / TM1, more specifically with Turbo Integrator (TI) script. In the first post in the series, I provided some background on AI technology like ChatGPT and really useful AI tools for programmers, like GitHub Copilot. In the second post in the series, I shared some ways in which ChatGPT is already able to help understand TI script, which could be useful in tasks like system documentation. One other finding in that second blog post is that although ChatGPT does a good job of accurately describing TI scripts, it is not yet capable of helping to suggest ways of optimizing or improving those scripts; it suggests things that are not possible in TI script (like CASE statements) and even makes up functions which don’t exist. As a reminder, all ChatGPT responses in this series are from ChatGPT-3.5, unless otherwise specified.
Topics: planning analytics, ChatGPT
This is the second in a series of blog posts about whether ChatGPT can help us with Planning Analytics (PA) / TM1, and more specifically with Turbo Integrator (TI) script. In the first post in the series, I provided some background on AI technology like ChatGPT and really useful AI tools for programmers, like GitHub Copilot. In that post I also showed examples that highlight some of the strengths of ChatGPT, like its ability to understand TM1Py scripts, and some of its risks and weaknesses, like its hallucinations about PA’s history. All ChatGPT responses in this series are from ChatGPT-3.5, unless otherwise specified.
The machines are taking over, haven’t you heard? For all the legitimate concerns about Artificial Intelligence (AI) being used to generate deepfakes or write student essays, many of these AI tools can help us be more efficient and tackle repetitive or mundane tasks so that we can focus on far more interesting problems and things that we enjoy. This post focuses on newer natural language generative AI tools, specifically ChatGPT, which has been very much in the news.
QueBIT evaluates many solutions in the planning space every year. It’s rare we find a solution that is game changing and complements our corporate mission. It’s time for the next generation of business planning platforms (Pigment) and your favorite planning implementation partner (QueBIT).
Currently, IBM Planning Analytics Workspace (PAW) can be run on Windows 2016 Server, Windows 2019 Server, and Linux. PAW is designed to run in a containerized software management engine, which provides many benefits, such as making it easier to deploy consistently across a variety of operating systems. Support for PAW in Windows Servers was made possible when Microsoft ported the open source, free, Docker containerization platform from Linux over to Windows. Microsoft has considered Docker on Windows a “feature”, and access to it has been covered by a client’s regular Microsoft server licensing agreement. Essentially Docker on Windows has been free for customers because they are already paying for the server OS in a bundle from Microsoft. Microsoft has included support for Docker too, which was a bargain of sorts for customers. This licensing and support structure is about to fundamentally change.
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.
Topics: Analytics, Sales and Operations Planning, S&OP
We are in the age of data, it is all around us and drives our daily decision making. From the morning commute to the purchase of an afternoon coffee, we rely on data to drive how we participate in the world. As the world evolves data volumes grow with it and it can be a lot to manage. For our customers, particularly in the Office of Finance, keeping up with changing data is challenging. Business users need confidence in their actuals, flexibility to adapt to data changes, and intuitive software that makes data management and analysis a breeze. Whether it is workforce planning, basic consolidation, or predictive revenue forecasting, it all starts with data.
Topics: Data Management
If you work in Financial Planning & Analysis (FP&A) you regularly use financial metrics or KPIs (Key Performance Indicators) to measure and manage performance. As FP&A extends into xP&A (Extended Planning & Analysis), non-financial operational metrics begin to pop up. This QueBIT blog post provided metrics for Lead Time and Customer Fulfilment (OTIF = “on-time, in-full”, and CFR = “case fill rate”) as just two of many possible examples of these.
Topics: ESG Reporting