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In transitioning from just traditional time-series modeling to predictive analytics, one of the key aspects is utilizing different causal inputs in your forecasting. It is not just relying only on internal shipment data or order history but considering external factors and a multitude of variables that paint a more complete picture.

Knowing how to integrate this new data and which ones to use can be intimidating and challenging but if done well, it is rewarding and profitable.

One of the most significant new models I have seen in a while helps bridge the gap between data and insights, and turns multiple inputs into valuable forecasted outputs. It is a powerful model that even inexperienced forecasters and data scientists can use. Using this new methodology, you are almost certain to get the highest fitted forecast or r-squared with little effort or concern. This is a brief introduction into this new method.

It is called Auto Phantom Regression with Integrated Linear Forecasting Operation and Ordinary Least Squared Estimator.

Although the name of the model is long (I am sure they will eventually come up with a good acronym), the name really highlights exactly what it does. Imagine a scenario where you have many predictor variables, or dont even know what variables exist and youre not sure how to include them. Because there are so many predictor variables, you would like some help in creating a good model automatically. It will do this by trying and testing many phantom variables during the exploratory stages, building a regression forecast based on ordinary least squares to find the best fit.

The way it accomplishes this is by using a type of stepwise regression in that it selects a model by automatically, adding or removing individual real and dummy predictors, a step at a time based on their statistical significance. The end result of this process is a single regression model, which makes it nice and simple. What makes this model extra special is that every time the model adds or removes predictors based on a statistical test, you also invoke a phantom degree of freedom because youre learning something from your data set but it does not show up as a degree of freedom.

These phantom degrees of freedom will not impact your number of observations per parameter estimate or affect the predicted R-squared. Instead, it allows the model to perform many statistical tests and try out many models based on actual and dummy variables until it finds a combination that appear to be significant and give you the highest r-squared.

Now there are some serious concerns and words of caution with this new revolutionary model. Firstly, when you try many different models and variables, youre bound to find one that best fits the data but doesnt fit reality. Second there is no magic bullet when it comes to building a model. And third, while there are components which are real and useful, I do not actually know of any Auto Phantom Regression with Integrated Linear Forecasting Operation and Ordinary Least Squared estimator model or, as it is known by its acronym, APRILFOOLS!

You can learn a lot from trying different variables and bringing multiple data sets into your forecasting process. But be careful. When using regression models, a degree of freedom is a measure of how much youve learned. Your model uses these degrees of freedom with every variable that it estimates. If you use too many, youre overfitting the model. The end result is that the regression coefficients, p-values, and R-squared can all be misleading and, while the model fits the data, it does not serve as a useful forecast.

Before throwing data about every potential predictor under the sun into your regression model, remember that it may not make it better. With regression, as with so many things in life, there comes a point where adding more is not better. In fact, sometimes not only does adding more factors to a regression model fail to make things clearer, it actually makes things harder to understand!

Yes, we absolutely need to start looking at various inputs to improve our forecast. There is still a place for conducting prior research into the important variables and their relationships to help you specify the best model. When you are using new variables, collect a large enough sample size to support the level of model complexity you need. Avoid data mining for what may work and keep track of how many phantom degrees of freedom you raise before arriving at your final model.

I dont care if you are using a traditional time-series model or sophisticated machine learning algorithm, when you hear the words best pick, be cautious. If you evaluate your model on the same data you used to train it, your model could very easily have overfitting. To help avoid this, have a testing data set or time series hold out periods. This is part of the overall data set you set aside and use to provide an unbiased evaluation of a final models fit before putting it into production.

Finally, if a model seems too good to be true and over-sophisticated to the point that you do not completely understand it, it may not be the right model. There is no replacement for experience and knowledge, and learning what is right for your forecasting process.

For further information on choosing the right forecasting model, click here. Alternatively, pick up a copy of my new book, Predictive Analytics For Business Forecasting.

For insight into real predictive analytics models that identify meaningful casual relationships in your data, attend IBFs Predictive Business Analytics, Forecasting & Planning Conference from April 20-22, 2021.

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The New Predictive Analytics Model All Forecasters Should Be Using | Demand-Planning.com - Demand-planning.com

IBM and Red Hat have struck a partnership with Celonis to push process mining software that identifies inefficiencies in business processes across an enterprise.

The Celonis Execution Management System (EMS) pulls real-time data from Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) systems and applies process intelligence and automation capabilities to improve execution of business processes across an organization. The product primarily targets organizations engaged in digital transformation projects.

"This software can examine every business process a company has, going across finance teams to supply chains to order management," said Malhar Kamdar, chief ecosystem officer with Celonis. "It can root out inefficiencies and present people with the truth about how their processes are working so they can make the appropriate changes to them."

While there appears to be no other top-tier software companies with straight-up process mining products, smaller companies are entering the market. Nintex Promapp offers a cloud-based process mining program that features a repository for storing core processes and procedures. ProcessGolds process mining tool connects all system data and creates a digital visualization of business processes. MPM ProcessMining has a collection of applications that combine process mining and business analytics that enables not just self-service process mining but business intelligence reporting.

The Celonis partnership is the first for IBM in the process mining market. IBMs only entry in this market is IBM Blueworks Live, a cloud-based business process modeling tool. The product allows inexperienced users to quickly learn the bsics of business process mining.

IBMs Global Services unit will integrate Celonis' product into its consulting work in multiple areas including supply chain, finance, procurement HR and application modernization. The company also plans to embed Celonis software into its Garage offering to improve workflow analysis and speed up workflows for processes such as production, customer service, manufacturing and logistics.

The Celonis deal should benefit IBM and Red Hat corporate users that have not only IBM Cloud and OpenShift, but also have platforms and applications from other top-tier vendors on which users have built business processes that travel across the enterprise.

"The Celonis product right out of the box has connectors that hook into SAP, Salesforce, Workday and ServiceNow," said Matthew Candy, global managing partner with IBM iX, a professional services organization within IBM Services. Big organizations have mixed platforms and their [business] processes run end-to-end with data traveling across all those platforms. [The Celonis product] allows us to plug in to see the process flows and locate where the inefficiencies are across the enterprise, he said.

By deriving intelligence from the data being examined, users can take more effective actions to correct the inefficiencies and drive higher ROI levels, Candy said.

"This helps them extract more value from the investments they have already made across the estate and in the base platform," Candy said. "This is the prospect we find most interesting now -- the ability to unlock inefficiencies, allowing companies to extract the benefits from what they have already invested in such as ERP platforms," he said.

One analyst believes the Celonis offering should work well with IBMs Garage methodology.

"IBMs Garage methodology examines and rebuilds workflow processes and resources for customers at a high level," said Alan Webber, IDC program vice president focused on customer experience. "But the problem is theres an immense amount of inefficiencies in any workflow you have to deal with. The Celonis product uses the data to mine processes and locate where those inefficiencies exist. Adding this on top of Garage users should realize higher value than they had before, " he said.

The Global Business Services unit also plans to build custom applications on the Celonis EMS for selected industries and domains specifically targeting regulated industries. The purpose of these applications will be to bring more actionable data and intelligent workflows to larger enterprises.

As part of its commitment to pursuing a hybrid cloud strategy, Kamdar said Celonis will use Red Hats OpenShift across any public or private cloud installation its users operate. This level of flexibility, he believes, gives his offering an advantage in highly regulated industries by offering greater interoperability across users existing systems.

Celonis said it plans to re-platform their entire portfolio of product to run on OpenShift over time. This will give users more options as to where they want to run their Celonis workloads.

As Editor At Large with TechTargets News Group, Ed Scannell is responsible for writing and reporting breaking news, news analysis and features focused on technology issues and trends affecting corporate IT professionals. He has also worked for 26 years at Infoworld and Computerworld covering enterprise class products and technologies from larger IT companies including IBM and Microsoft, as well as serving as Editor of Redmond for three years overseeing that magazines editorial content.

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IBM, Red Hat partner with Celonis on process mining software - TechTarget