Increasing demand from customers, high market competitions, fluctuating demand from customers and faster response, in addition to advancements in technology have turned the market into an unstable environment. All this conditions together creates a greater uncertainty in the supply chains and leads to the bullwhip effect phenomenon. It happens when the orders to the supplier tends to have larger variations than demands to the buyer such that the distortion propagates upstream in an amplified form. The major problems associated with the bullwhip effect are increasing the safety stock & carrying cost at each supply chain echelon, decreasing the customer satisfaction and inefficient production process at each echelon.
So it is important to identify the bullwhip effect associated with the supply chain. One of the important step in analyzing the bullwhip effect is the quantification of bullwhip effect in an accurate way. Usually bullwhip effect is calculated as a ratio between order variance to the demand variance. This method is used by assuming that the variables in the data sets are independent and identically distributed. But in actual practice the data associated with the order and sales shows some kind of dependency within the data set. This independency is represented by a parameter known as Hurst exponent, which is identified by methods of rescaled range analysis, detrended fluctuation analysis (DFA) etc. A better method for the quantification and analysis of bullwhip effect is introduced in this paper by considering the independency of data set.
Table of Contents
CHAPTER 1. INTRODUCTION
1.1. Bullwhip Effect
1.2. Complex Systems
1.3. Hurst Exponent
1.4. Importance of The Topic
CHAPTER 2. LITERATURE REVIEW
CHAPTER 3. METHODOLGY
3.1 Data collection
3.1.1 Computer Consumable
3.1.2 Automotive Components
3.1.3 Washing Powder
3.1.4 Fresh Juice
3.2 The Present Method
3.3 The Proposed Method
3.4 Hurst Exponent Calculation
3.4.1 Rescaled range analysis
3.4.2 Detrended fluctuation analysis
3.5 Lyapunov Exponent
3.6 Matlab Software
CHAPTER 4. COMPUTATIONL RESULTS AND ANALYSIS
4.1 Variance Change as a Function of H
4.2 Variance Change as a Function of Number of Data
4.3. Hurst Exponent Calculation from Data
4.3.1 Computer consumable-production
4.3.2 Computer consumable-demand
4.3.3 Automotive component-production
4.3.4 Automotive component-demand
4.3.5 Washing Powder-Production
4.3.6 Washing Powder-Demand
4.3.7 Fresh Juice-Production
4.3.8 Fresh Juice-Demand
4.4 Lyapunov Exponent Calculation from Data
CHAPTER 5. CONCLUSION AND FUTURE WORKS
Research Objectives and Core Themes
The primary objective of this thesis is to improve the accuracy of quantifying the bullwhip effect in supply chains by accounting for data dependency, which is often ignored in traditional variance-based methods. The research investigates how Hurst exponent analysis and Lyapunov exponent calculations can better represent the non-random nature of demand and supply data, thereby enabling more robust supply chain management strategies.
- Quantification of the bullwhip effect using complexity calculations.
- Application of the Hurst exponent to analyze long-term memory in time series.
- Utilization of Detrended Fluctuation Analysis (DFA) and Rescaled Range (R/S) analysis.
- Assessment of system predictability using the Lyapunov exponent.
- Comparative analysis of traditional versus proposed bullwhip calculation methods.
Excerpt from the Book
1.1 BULLWHIP EFFECT
The bullwhip effect is one of the most popular concepts in the operations management/research field. The term ‘bullwhip’ was coined to describe the effect by which slow moving consumer demand creates large swings in production for the suppliers at the other end of the supply chain. This is analogous to the handle of the bullwhip causing a loud crack at the popper. The bullwhip effect is referred to as ‘demand amplification’, ‘variance amplification ‘or the ‘Forrester effect’.This effect becomes significant when the cost from fluctuations in production outweighs the cost of holding inventory.Over the years, evidence has suggested that bullwhip costs play a pivotal role in some businesses.
The bullwhip effect appeared for the first time in literature as the subject in 1961 year [9].The author of the study noticed this effect of batches executed at bargain of simulation analyses. He determined this problem initially with name of increasing of demand. The problem of the bullwhip effect is resulting from the system according to it along with its policy, the organizational structure and delays in flows of materials and information, rather than is coming from the external sources. The bullwhip effect is defined as the effect of lack of the information exchange between components of the chain of supplies and of occurring of non-linear interactions which they are causing for the difficulty in administration with them.
Summary of Chapters
CHAPTER 1. INTRODUCTION: Outlines the instability of modern markets, defines the bullwhip effect as demand amplification, and introduces the importance of addressing supply chain uncertainty through advanced complexity metrics.
CHAPTER 2. LITERATURE REVIEW: Examines existing academic perspectives on supply chain complexity, fractal analysis, and prior methodologies used to quantify variance amplification.
CHAPTER 3. METHODOLGY: Describes the data collection process for four distinct products and details the mathematical models, including Hurst and Lyapunov exponents, used to propose a more accurate bullwhip calculation.
CHAPTER 4. COMPUTATIONL RESULTS AND ANALYSIS: Presents the computational results of the Hurst and Lyapunov exponents for the studied products, highlighting the deviation between traditional and proposed bullwhip measurement methods.
CHAPTER 5. CONCLUSION AND FUTURE WORKS: Synthesizes the research findings, confirming that data dependency significantly impacts bullwhip quantification and suggesting that deep study of data characteristics is essential for accurate supply chain management.
Keywords
Bullwhip Effect, Detrended Fluctuation Analysis (DFA), Hurst Exponent, Lyapunov Exponent, Supply Chain Management, Variance Amplification, Complex Systems, Data Dependency, Time Series Analysis, Fractal Analysis, Rescaled Range Analysis, Operations Management, Production Smoothing, Predictive Modeling, System Complexity.
Frequently Asked Questions
What is the core focus of this research?
The research focuses on the bullwhip effect, a phenomenon where small fluctuations in consumer demand lead to amplified production swings upstream in a supply chain, and proposes a more accurate quantification method.
What are the central thematic areas covered?
The themes include supply chain complexity, the quantification of demand amplification, the role of data dependency, and the application of complexity science methods like Hurst and Lyapunov exponents.
What is the primary goal of the study?
The goal is to refine the bullwhip effect calculation by acknowledging that supply chain data is rarely independent and identically distributed, thereby correcting errors inherent in traditional variance-based ratios.
Which scientific methods are employed?
The study utilizes Rescaled Range (R/S) analysis, Detrended Fluctuation Analysis (DFA) to calculate the Hurst exponent, and Lyapunov exponent analysis to evaluate system predictability.
What is covered in the main body of the work?
The main body details the data collection for four products (computer consumables, automotive components, washing powder, and fresh juice), the mathematical implementation of the proposed methods, and the analysis of computational results.
Which keywords define this work?
Key terms include Bullwhip Effect, Hurst Exponent, Detrended Fluctuation Analysis (DFA), Lyapunov Exponent, and Supply Chain Complexity.
How does the Hurst exponent affect the calculation of the bullwhip effect?
The Hurst exponent measures the long-term memory of a time series; by incorporating it into the variance formula, the study accounts for persistent or anti-persistent data behavior, which traditional models ignore.
Why is the Lyapunov exponent used in this thesis?
The Lyapunov exponent is used to measure the sensitivity of the supply chain system to initial conditions, helping to determine the level of chaos and predictability within the demand and production data sets.
What did the analysis of washing powder data reveal?
The study found that washing powder demand exhibited a high Hurst exponent (above 0.9) and high persistence, leading to a maximum percentage error when using conventional bullwhip calculation methods compared to the proposed model.
- Quote paper
- Jino James (Author), 2020, Quantification and Analysis of Bullwhip Effect using Complexity Calculations, Munich, GRIN Verlag, https://www.grin.com/document/917534
