Trufa – The Future of Analytics
An Analytics Application for the Digital Enterprise
Trufa is an analytics application that provides insights about business optimization in the enterprise in high speed and precision. In the past, Analytics meant to aggregate numbers into metrics, turn them into pies and bars, and perhaps even calculate trend lines. The complex, but value-adding work – namely to derive insights and actions from the information – has to be done by humans.
With Trufa, Go Beyond Classical Process Mining
Trufa goes beyond this mere visualization of facts. Its machine intelligence offers a new level of analysis. For business goals, such as saving working capital, the software analyzes which measures are effective and how much cash can be freed up. It also calculates the likelihood of this optimization measures.
Trufa in a Nutshell
Process Mining +
Trufa offers fully automated discovery of cash management, profitability and pricing optimization opportunities, classified by economic value
Trufa performs analysis across (sub-) processes of the cash-to-cash cycle – especially based on order-to-cash (O2C) and procurement-to-pay (P2P).
Trufa is an award-winning user interface, no modelling, no programming, no data scientist needed.
More than 50 clients from multiple industries and company sizes have already successfully used the Trufa analytics solution.
Trufa Covers the Whole Data Lifecycle
From descriptive to prescriptive analytics to identify relevant business opportunities
Descriptive Analytics: Data Visualization
Create transparency about what happened in your processes
Diagnostic Analytics: Root-cause Analysis
Identify relevant patterns in your processes
Predictive Analytics: Impact Simulation
Simulate the impact of process changes to your cash and profit
Prescriptive Analytics: Opportunity Identification
Automatic discovery of process optimization potentials
Alerts – The Future of Reporting
Monitor performance indicators identified as critical
Get information about defined business occurrences
Monitor changes in cash optimization opportunities
Trufa Use Case Examples
Working Capital & Cash Management
Identification of opportunities to reduce inventory, optimization of payment terms or accounts receivable & payable
Revenue & Margin Growth
Optimization of profits by analyzing customer satisfaction, product profitability, or pricing
Turnaround & Restructuring Strategy/Drivers
Identification of low performing products, plants, countries, or complexity reduction potentials
Supply Chain Optimization & Strategic Sourcing
Optimization of supplier network, inventory levels and purchase prices, analysis of customer payment history or plant profitability
Target Setting & Planning Support
Identification of realistic targets, of profit drivers and simulation of impacts of measures
S/4HANA Implementation Preparation
Identification of current ERP usage patterns, configuration complexity and their business benefits
The Science Inside - Whitepapers
About the author: Prof. Dr. Andreas Mielke is Managing Director of Deloitte Digital. He joined Deloitte in 2018 co-heading the Trufa team. Along with his work at Deloitte Digital, Andreas Mielke is apl. Professor at the Institute for Theoretical Physics at the University of Heidelberg, Germany. He studied in Frankfurt and Heidelberg, where he received the habilitation in 1994. He worked for extended periods of research in Lausanne, Cambridge, Paris, Prague, Budapest, and Vienna.
Whitepaper No 1: Robust Statistics
Statistics is known to everybody, simply because in our current live we often see statistical analyses. But what is robust statistics, why should statistics be robust? And why should we use robust statistics instead of well known usual statistics, when we analyze business data? The present paper answers this questions and explains why only robust statistics guaranties reliable results when we analyze business data. The aim is to provide some very basic understanding of the why and how of robust statistics. For details of the mathematical foundation or the implementation of robust procedures we refer to the literature mentioned at the end.
Whitepaper No 2: Maximum Entropy
Entropy is a quantity which appears in statistical physics. How is it connected to business data and why should it be maximized? The present paper tries to answer that question. We show that the maximum entropy principle ensures that the outcome of a statistical model is unbiased, robust, and actionable. The maximum entropy method is a general-purpose technique in machine learning. It has a simple and precise mathematical foundation. A number of aspects make it well suited for the modelling of distributions of business data.
Whitepaper No 3: Time Series Analysis, Modelling, and Forecast:
The paper aims at explaining how time series analysis can be used for the prediction of business data. We present which methods are available, which methods are suitable for business data, and how prediction of business data can be implemented. As an example, we will use liquidity prediction.
Whitepaper No 4: Regression Outliers
Standard regression often yields bad results if outliers are present. There are two ways out of this dilemma. The first is to remove the outliers. The second is to use robust methods for which the result does not depend on outliers. If the data set is large, the second possibility is often the only possible one. In this paper, we point out that robust methods can and should be used for outlier detection. The reason is that outliers often contain additional information and are thus important. They may for instance show that an additional factor is relevant to understand the behavior of the full data set. A precise outlier detection is therefore mandatory.