This paper gives readers the possibility to brighten and deepen their knowledge in subjects which are closely related to Supply Chain Management.Predictive analytics, in the context of big data demand forecasting, is a revolutionary phenomenon in the modern world, and is expected to remain so in the foreseeable future. In this paper, the conditioning of big data, in the context of organizations that carry out a demand forecast, is differentiated into three parts. Accordingly, the procedure of data acquisition, data classification concepts, and forecasting methods are examined. The review of the literature has yielded an assessment method that supports demand planners to determine how big pools of data can be classified and utilized to carry out a forecast in a compatible manner. The case related application of the developed assessment method in organizations that forecast highly volatile stock keeping units with high monetary value was successful.
The following paper is subdivided into a total of six chapters. Accordingly, after the introductory chapter has shed light on the subject, further work on the topic will be conducted as follows: Chapter two will provide insights regarding the relationship between big data and the objective of this paper, by providing information about the acquisition of planning data. Within chapter three, different classification concepts are introduced considering the context of compatibility with specific forecasting methods of chapter four, respectively. This predominantly conducted literature review results in an assessment method that is applied in chapter five, where identified insights are considered in the context of different case studies. Lastly, chapter six states a brief conclusion about essential results found out in this paper.
Table of Content
1 Introduction
1.1 Motivation and Background
1.2 Objective and Outline of this Paper
2 Acquisition of Big Data
3 Classification Concepts
3.1 VED Analysis
3.2 FSN Analysis
3.3 SDE Analysis
3.4 HML Analysis
3.6 XYZ Analysis
3.7 ABC – XYZ Analysis
3.8 Classification Synopsis
4 Demand Forecasting Methods
4.1 Qualitative Forecasting Methods
4.1.1 Delphi Method
4.1.2 Jury of Expert Opinion
4.1.3 Market Survey
4.1.4 Qualitative Forecasting Synopsis
4.2 Quantitative Forecasting Methods
4.2.1 Time Series Models
4.2.1.1 Moving Average
4.2.1.2 Exponential Smoothing
4.2.1.3 Box-Jenkins
4.2.1.4 Time-Series Forecasting Synopsis
4.2.2 Causal Models
4.2.2.1 Regression Models
4.2.2.2 Econometrics Models
4.2.2.3 ANN Models
4.2.2.4 Causal Forecasting Synopsis
4.3 Forecast Error Measures
4.4 Contrasting Juxtaposition of Forecasting Methods and Classification Concepts
5 Case Studies
5.1 Application of Forecasting/Classification Compatibility Matrix
5.2 Industrial Application
6 Conclusion
6.1 Contribution of this Study
6.2 Managerial Implication
6.3 Limitations and further Research
Objectives and Research Focus
The primary objective of this paper is to identify the compatibility between various inventory classification concepts and demand forecasting methods. It addresses the challenge that organizations face in selecting the correct forecasting approach for different categories of stock keeping units (SKUs), particularly those with high value and high volatility, to improve supply chain efficiency and reduce costs.
- Analysis of data acquisition processes in the context of Big Data.
- Evaluation of inventory classification methods (e.g., ABC, XYZ, VED, FSN, SDE, HML).
- Review of qualitative and quantitative demand forecasting models.
- Development of a Forecasting/Classification Compatibility Matrix to guide decision-making.
- Application of the proposed assessment method through industry-based case studies.
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3.7 ABC – XYZ Analysis
A symbiosis of the two previously described analyses results in the “ABC – XYZ Analysis” (Stojanovic and Regodic, 2017, p. 45). Purposefully, this analysis contributes to an optimal inventory level, which is considered a key condition concerning cost reduction of an organization (cf. Stojanovic and Regodic, 2017, p. 46). According to (Blaszczyk, 2005, p. 106), making a large selection of goods in sufficient quantities available to the end customer is considered the main task of all organizations. Organizations offer a large variety of goods, which is why the management calls for a division into goods of lower and higher strategic importance (Bulinski, 2013, p. 90). Brown (1982, p. 155) stated that such classification is performed since the determination of the purchase policy, production planning, and store management plays an important role in any organization.
Most commonly, this analysis is conducted by the aid of past consumption data and is considered of high importance for manufacturing strategies regarding supply and inventory control (Scholz-Reiter, 2012). By utilizing the ABC - XYZ analysis, an organization can identify products that might rupture or overstock and control this issue by defining a proper stock and security stock coverage and optimize production volumes (AbcSupplyChain LTD, 2020). Consequently, this analysis is a popular tool in supply chain management (SCM), which also builds upon the principle of Pareto, already described as the 80/20 rule, which expects that the minority of cases has a disproportional impact to the whole (Swamidass, 2000 and Kourentzes, 2016). Goldman (2010) elaborates, that as for the ABC part of this analysis in regard to SCM, it is assumed that 20% of the items establish 80% of the value of demand. In other words, “the important few versus the trivial many” (cf. Ravinder, 2014, pp. 257-263).
Summary of Chapters
1 Introduction: Provides the motivation for effective inventory management and outlines the research objective of identifying compatibility between classification and forecasting.
2 Acquisition of Big Data: Discusses the transition from traditional data to Big Data and its role in modern supply chain forecasting.
3 Classification Concepts: Introduces various methods for categorizing SKUs based on different criteria like criticality, turnover, and value to support better planning.
4 Demand Forecasting Methods: Details both qualitative and quantitative approaches to forecasting, explaining their underlying mathematical foundations or expert-based structures.
5 Case Studies: Demonstrates the practical application of the developed compatibility matrix within real-world business scenarios across different organizations.
6 Conclusion: Summarizes the key findings, including the development of the decision-making tool and recommendations for future research.
Keywords
Predictive Analytics, Big Data, Demand Forecasting, Statistics, Inventory Management, ABC Analysis, XYZ Analysis, Supply Chain Management, Stock Keeping Units, Data Classification, Forecasting Accuracy, Industrial Application, Quantitative Models, Qualitative Models, Decision-Making Tools.
Frequently Asked Questions
What is the fundamental goal of this paper?
The paper aims to develop a decision-making tool that helps organizations identify the most compatible demand forecasting method for specific categories of stock keeping units, thereby optimizing inventory efficiency.
Which thematic areas does the work cover?
The work covers Big Data acquisition, various inventory classification techniques (such as ABC, XYZ, VED), and a comprehensive categorization of qualitative and quantitative forecasting methods.
What is the primary research question?
The research seeks to determine which specific combinations of inventory classification methods and forecasting models are most suitable for managing high-value, highly volatile stock keeping units.
Which scientific methodology is applied?
The paper follows a theoretical literature review approach, synthesizing existing academic and industry research to create a compatibility matrix, which is then validated through empirical case studies.
What is the main focus of the "main body" chapters?
The main body focuses on classifying data into logical categories and then selecting appropriate forecasting algorithms—ranging from simple moving averages to complex artificial neural networks—that match those categories.
Which keywords define this work?
The work is defined by terms like Predictive Analytics, Big Data, Demand Forecasting, ABC-XYZ Analysis, and Inventory Management.
What is the significance of the A/Z category in this paper?
A/Z SKUs represent items with high monetary value and high demand volatility, which are the primary focus of the author's investigation due to the complexity they add to inventory management.
How does the "Forecasting/Classification Compatibility Matrix" work?
It acts as a decision support tool that maps specific SKU classification segments against compatible forecasting methods, allowing managers to quickly identify the best approach for their specific datasets.
What role does "Big Data" play in the forecasting process described?
Big Data provides the historical foundation required for accurate forecasting, allowing organizations to track trends and demand patterns that traditional data sources might overlook.
- Quote paper
- Sebastian Neumann (Author), 2020, Big Data Demand Forecasting Regarding High Value/Highly Volatile Stock Keeping Units, Munich, GRIN Verlag, https://www.grin.com/document/918406
