The work describes two new heuristic approaches to time series analysis and forecasting for business purposes. Both approaches avoid any assumptions according to assumed process attributes behind the data (stochastic process, stationarity, normal distribution of random noise). Those methods engineer data of any kind of business processes. Only unidentified (inherent) process structures are used for forecasting.
Speed represents the drive of current business development. IT is about allowing automatic self-synchronizing (production) processes. Big Data potentially offers the identification of hidden structures. In many companies mobile information access is being used. Multi-Channel B2C, B2B and M2M are gaining the managerial pole position. But nevertheless the quality of data is the key for producing excellent results. It is important that planning is based on as realistic data as possible. After roughly more than 35 years Business Forecasting is back in the focus.
Table of Contents
1 Introduction
2 Brief Forecast-Methodology Overview
3 Business Forecasting
4 Comments on Forecast Accuracy
5 New Methods supporting Business Forecasting
5.1 Business Process Forecasting - The REVINDA-MM-Approach
5.2 "ERP-Type"- Forecasting - METRIX Approach
6 Conclusions:
7 Literature:
Objectives and Topics
The paper aims to introduce two innovative, heuristic forecasting approaches—REVINDA and METRIX—designed to enhance business planning quality by avoiding restrictive assumptions typically found in traditional statistical modeling. Instead of relying on predefined stochastic processes, these methods focus on engineering data based on inherent process structures identified from historical data.
- Comparison of standard forecasting practices vs. model-free approaches.
- Introduction of the REVINDA-MM-Approach for business process forecasting.
- Explanation of the METRIX Approach based on similarity metrics.
- Practical implementation strategies for forecasting short-term business cycles.
- Discussion on forecast accuracy in the context of real-world operational environments.
Excerpt from the Book
Business Process Forecasting (MM-Forecast)
To give a B2C example: A LSP requires from his customer (a B2C company) the forecast of daily picks for two weeks ahead for capacity planning, because the 24 hours service only is guaranteed in case the forecast meets the range between 75% and 120% around actual orders. – Of course the B2C-company knows the order pattern from past experience. But daily B2C orders are influenced by standard and special promotions of Marketing. Another aspect affecting daily sales is given by temporary top selling items and/or the launch of new products, etc…. The effects of promotions and special market conditions are estimated 'manually' by Marketing.
So, the final forecast has 2 input sources: a "basic process forecast" and the impact of special company activities. Under those scenario conditions a manager will be responsible for the final forecast quality and data maintenance. So, the final forecast summarizes the basic forecast plus – in this example – Marketing "brain-input". – What about the data to be used in next year's forecasts? As top selling items and special promotion will not occur next year at the same period the actual data have to be re-adjusted for providing high quality input data for next year's basic process forecasts. Therefore it makes sense documenting the promotional and special market effects in a log-file. In case of daily forecasts the re-adjustment of weekly data is sufficient.
Summary of Chapters
1 Introduction: Provides an overview of the role of IT and Big Data in modern business development and the historical evolution of forecasting methods.
2 Brief Forecast-Methodology Overview: Lists various established forecasting methods used in ERP systems and acknowledges the challenges of selecting appropriate models.
3 Business Forecasting: Defines two primary categories of forecasting scenarios: Business Process Forecasting (MM-Forecast) and "ERP-Type" forecasting.
4 Comments on Forecast Accuracy: Discusses the metrics and challenges associated with short-term forecast accuracy and the limitation of standard modeling assumptions.
5 New Methods supporting Business Forecasting: Details the algorithmic approach and data requirements for the REVINDA and METRIX methods.
6 Conclusions:: Summarizes the competitive advantage of the proposed methods and their proven value in real-world business applications.
7 Literature:: Provides a list of cited academic papers and industry studies relevant to the presented forecasting techniques.
Keywords
Revinda, Metrix, Business Forecasting, Similarity, Model-free forecasting, Time Series Analysis, Planning Quality, ERP-Systems, Heuristic Approaches, Data Engineering, Process Structures, Predictive Accuracy, B2C Forecasting, B2B Forecasting.
Frequently Asked Questions
What is the fundamental focus of this paper?
The paper focuses on introducing two heuristic, model-free forecasting approaches—REVINDA and METRIX—designed to improve business planning quality without relying on traditional statistical assumptions.
What are the central thematic fields covered?
The paper covers time series analysis, the limitations of standard ERP forecasting methods, and the application of structural identification techniques for business processes.
What is the primary objective of the research?
The primary objective is to offer practitioners alternative, "nearly right" forecasting tools that avoid the complexities and rigid constraints of traditional, assumption-heavy statistical models.
Which scientific methodology is applied?
The research uses a data-driven heuristic approach that identifies inherent process structures and similarity metrics, rather than applying conventional stochastic or distribution-based modeling.
What is covered in the main section of the paper?
The main section details the algorithms, data requirements, and practical application logic for both the REVINDA-MM-Approach and the METRIX-Approach.
Which keywords characterize this work?
Key terms include Revinda, Metrix, Business Forecasting, Similarity, Model-free forecasting, and process structure identification.
How does REVINDA handle the "brain-input" from marketing?
REVINDA incorporates manual human adjustments—or "brain-input"—from marketing departments into the basic process forecast to account for special promotions and market conditions.
What makes the METRIX approach different from standard ERP forecasting?
Unlike standard ERP methods that often remain fixed despite changing business conditions, METRIX provides flexible forecasting based on similarity metrics that can be adapted to specific data attributes.
What role does the "Structural Tracking Band" play?
It is used as a new comparative measure for tracking forecast signals, serving as an alternative to traditional error measures like MSE or MAPE.
- Quote paper
- Klaus Spicher (Author), 2015, The Heuristic Business Forecasting Methods Revinda and Metrix, Munich, GRIN Verlag, https://www.grin.com/document/542856
