sábado, 22 de agosto de 2020

Vamos tomar um café e falar de inovação?

 Vamos tomar um café e falar de inovação?


Os convidados são os ganhadores do prêmio Global de inovação da SAP, "SAP Innovation Awards 2020", nossos amigos Alexandre Faraco e Manuel Robalinho do Grupo Aço Cearense, utilizando o SAP Analytics Cloud (SAC) para otimizar o processo de planejamento orçamentário e junto com o nosso parceiro SolvePlan.

Venham conhecer e interagir ao vivo com os nossos convidados sobre o cenário campeão!

Data: 28/08 as 16:00h.
Link de inscrição: https://lnkd.in/dDfhPJ8



quarta-feira, 5 de agosto de 2020

SAP ® Innovation Awards 2020 Entry Pitch Deck






“Grupo Aço Cearense uses SAP Analytics Cloud to manage and streamline the company’s budget
processes and operations. Thanks to this unique platform, we’re able to spend less time on budget processes and more time on scenario analysis, so we can improve future sales and margin results.”
Mr. Manuel Robalinho
SAC Consultant of GAC

GAC uses SAP Analytics Cloud (SAC) with specific models for OPEX, CAPEX, payroll, supplies, inventory, production and reporting. Each model interprets its calculation algorithms through scripts sharing information with subsequent models in terms of budget flow.

GAC budget is distributed among various companies and operating areas, with a total of 130 users who enter the information on the SAC platform. With the new SAC system, scenario presentation is fast and assertive so that we can make multiple game plan versions, which allows us to adapt to steel production and trade scenarios within the global market. GAC has moved from a mid year review budget to quarterly forecasts.

sábado, 1 de agosto de 2020

Creating an Automated Regression Model with SAP Predictive Analytics


Let's walk through the steps of creating an automated regression model with SAP Predictive Analytics.

 

We’ll use two datasets (one for training, one for application) related to predicting the energy use of household appliances (https://archive.ics.uci.edu/ml/datasets/ Appliances+energy+prediction), provided by the UCI Machine Learning Repository.



Editor’s note: This post has been adapted from a section of the book SAP Predictive Analytics: The Comprehensive Guide by Antoine Chabert, Andreas Forster, Laurent Tessier, and Pierpaolo Vezzosi.


Book SAP Press:

https://www.sap-press.com/sap-predictive-analytics_4491/?utm_source=sappressblog&utm_medium=contentmarketing&utm_campaign=Blogs&utm_term=chapter7&utm_content=1592

 

*The full citation for the dataset above is as follows:

Luis M. Candanedo, Veronique Feldheim, and Dominique Deramaix, “Data driven prediction models of energy use of appliances in a low-energy house,” Energy and Buildings, Volume 140, 1 April 2017, Pages 81–97, ISSN 0378-7788, https://archive.ics.uci.edu/ml/datasets/Appliances+energy+prediction.