System dynamics method in the external logistics of the supply chain network
Article: System dynamics: an approach to modeling supply chain performance measurement
1. Description of the scientific achievement related to the application of the SD method and information on what is unique in this study:
Scientific achievement: The study develops a system dynamics model using causal-effect diagrams (CAE), aimed at simultaneously increasing the flexibility and agility (AAF) of the supply chain, with an emphasis on cost reduction, shortening delivery time, and improving customer satisfaction.
Unique approach:
A clear indication that flexibility and agility alone do not guarantee increased profitability – an optimal level of AAF is required, which challenges the previous assumption that “more is better.”
The model uses expert mapping of AAF variables and scenario simulations to identify the optimal point, providing measurable recommendations for SCM managers.
2. Information on the strengths and weaknesses of SD, as well as opportunities and threats related to its application:
Strengths
* Ability to model complex systems and their behavior over time:
“A model was developed using these variables to simulate SCP using Vensim software.”
“SD is with a set of qualitative tools for dynamic process analysis including causal-effect loop diagrams, stock-flow diagrams, and simulation and optimization diagrams.”
* Ability to test strategic and tactical scenarios:
“The results are used to predict the future values of the factors.”
“...different scenarios are developed and tested by determining the different values of the effective indicators.”
Weaknesses
* The model is approximate, not precise:
“Modeling is performed through SD that is an approximate, not a definite model due to its properties.”
* Sensitivity to the selection and number of variables and the time horizon:
“Selecting the right time horizon plays a key role in the outputs from SD and helps produce more realistic results.”
“Not all models are correct and systems thinking needs some degree of satisfaction.”
Opportunities
* Increasing SC efficiency by finding optimal AAF levels:
“The results indicated that AAF alone and absolutely cannot lead to higher profitability. \[...] the highest profitability is gained when they are equal to 35.06% and 45.8%, respectively.”
* Possibility of adaptation to various industries (e.g., automotive):
“This study is an applied to Automobile companies.”
Threats
* Exceeding the optimal level of AAF may lead to decreased profitability:
“Investing in these two factors beyond a given limit is not profitable.”
* Simplified modeling may omit random or critical variables:
“Large errors may be due to the high scatter of random data in the model.”
3. Main conclusions from the article:
Flexibility and agility (AAF) should not be maximized – there is an optimal point
“The results showed that AAF alone and absolutely cannot enhance profitability. \[...] AAF do not need to be enhanced to the highest level, but an optimal point must be found.” (p. 1291)
This is a key conclusion from the simulations – more flexibility does not always mean better financial results.
The highest profitability is achieved at a specific level of AAF
“The highest profitability is gained when they are equal to 35.06% and 45.8%, respectively.” (p. 1311)
A specific range of AAF values brings the best results – beyond this point, investment becomes unprofitable.
System dynamics enables forecasting the effects of strategies without implementation
“By developing and simulating different scenarios \[...] an optimal level of AAF was estimated.” (p. 1291)
“The results are used to predict the future values of the factors.” (p. 1300)
SD simulations provide decision-makers with a tool to assess the effects of actions before actual implementation.
Scenarios indicate that the ability to adjust production in real time is the most profitable
“The first scenario yields the best outcomes for the SCPR.” (p. 1311)
“When the ability to change the production output increases, the best results are obtained.”
The ability to dynamically change production volume in response to the environment brings the greatest gains.
The application of SD in practice improves response time and customer satisfaction
“...the ability to respond to sudden demands is increased and as a result, the speed of covering these demands increases.” (p. 1291)
“An increase in the speed of responding to the customer’s needs will lead to higher customer satisfaction, which in turn leads to repurchase.” (p. 1302)
SD modeling showed that response speed in the supply chain directly influences customer satisfaction and loyalty.
The SD model accurately reflects the operational reality of the analyzed supply chain
“The simulated behaviors were compared with reality and the results were tested with possible methods.” (p. 1312)
This means that the developed model can be applied in business practice – it has high consistency with real data.
Article: APPLICATION OF SYSTEM-DYNAMIC MODELING TO IMPROVE DISTRIBUTION LOGISTICS PROCESSES IN THE SUPPLY CHAIN
1. Description of the scientific achievement related to the application of the SD method and information on what is unique in this study:
In the article, the authors propose the use of the System Dynamics (SD) method to model and improve distribution logistics processes in the supply chain. The main achievement lies in the comparison of two scenarios (BPMN process models) and the development of a dynamic SD model that allows for the analysis of changes in these processes.
Uniqueness of the study:
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Application of two different approaches: process modeling (BPMN) and dynamic modeling (SD), in order to capture both structural and temporal aspects of distribution logistics operations.
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Use of expert evaluation of customer satisfaction indicators, which adds a qualitative dimension to the SD simulation:
"System dynamics is used, not only as a causal loop diagram, but calculated measures of end-user satisfaction indicators were provided by experts, as well."
2. Information on the strengths and weaknesses of SD, as well as opportunities and threats related to its application:
Strengths
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Possibility of integrated assessment of logistics services:
"System dynamics is used, not only as a causal loop diagram, but calculated measures of end-user satisfaction indicators were provided by experts, as well." (p. 29) -
Facilitating process analysis and improvement:
"Modeling processes by describing actions and measuring the results of their processes allows organizations to constantly analyze them, thereby contributing to their improvement." (p. 30)
Weaknesses
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Need for expert involvement and process complexity:
"Calculated measures of end-user satisfaction indicators were provided by experts" -
Difficulties for smaller companies in implementing logistics strategies:
"Small and medium ones, cannot always ensure full compliance with the above principles and goals of logistics, due to insufficient resources to improve the quality of service, dynamically changing needs of their customers and other complex factors." (p. 29)
Opportunities
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Increasing customer satisfaction through modeling and process improvement:
"...contributing to their improvement." (p. 30 – refers to the potential to enhance processes through modeling)
Threats
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Variability of customer requirements and market environment:
"Dynamically changing needs of their customers and other complex factors." (p. 29)
3. Main conclusions from the article:
The use of system dynamics (SD) modeling in distribution logistics enables more accurate analysis of logistics processes and supports their improvement.
Through process modeling and measuring their outcomes, organizations can continuously monitor their activities, identify weak points, and systematically improve them:
"Modeling processes by describing actions and measuring the results of their processes allows organizations to constantly analyze them, thereby contributing to their improvement."
Combining BPMN with SD allows not only understanding the structure of logistics processes, but also simulating their course over time, which supports sound managerial decision-making.
The article presents two process scenarios whose structures were described using BPMN, and whose operational dynamics were assessed using SD. This combination provides a holistic view of logistics.
Expert evaluation of service quality indicators can be effectively integrated into the SD model, increasing the realism of the simulation and its usefulness for business practice.
The authors included expert opinions in the model, reflecting customer service quality in the context of various scenarios:
"Calculated measures of end-user satisfaction indicators were provided by experts.
Small and medium-sized enterprises may have difficulty fully implementing SD-based strategies due to limited resources and the variability of customer requirements.
This limitation presents a significant challenge in adopting the SD approach in less resourceful organizations:
"Small and medium ones, cannot always ensure full compliance with the above principles and goals of logistics, due to insufficient resources [...] and other complex factors.
SD modeling provides managers with tools to assess the effectiveness of different process variants and identify bottlenecks and possible improvements.
Article: A Model of Systems Dynamics for Physical Flow Analysis in a Distribution Supply Chain
1. Description of the scientific achievement and uniqueness of the study
"This article proposes a model of systems dynamics for the analysis and study of physical flows in a distribution logistics chain."
The authors developed a System Dynamics (SD) model aimed at analyzing physical flows in a distribution supply chain. The model includes elements such as transport costs, inventory costs, and environmental issues (e.g., a CO₂ emission tax):
"The proposed model is a decision support tool that allows testing several scenarios in order to study the behavior of physical flows within a supply chain depending on the inventory and transport costs and taking into consideration the environmental issues through the integration of a CO2 tax in transport costs."
The uniqueness of the study lies in combining economic analysis (inventory and transport costs) with environmental components, as well as in the ability to test different decision-making variants through computer simulations.
2. Strengths and weaknesses of SD, along with opportunities and threats
Strengths
* System dynamics enables the study of complex relationships between different elements of the supply chain:
"Systems dynamics modeling is one of the methods and approaches used to understand and study the different phenomena and problems of the supply chain."
* It is a decision-support tool that allows for "what-if" analysis and testing various strategies over the long term:
"The developed model can be used to analyze various scenarios and perform various 'what-if' analyzes, as well as to answer questions about the long-term operation of distribution chains."
Weaknesses
* To obtain results, the authors had to simplify reality, which naturally limits the accuracy of representing complex logistics processes:
"Assumptions have been made to simplify the analysis, such as a constant rate of delivery and uniform demand."
* These assumptions may lead to an incomplete reflection of real system behavior:
"These simplifications may not capture all the complexities of the actual system and may affect the accuracy of the results."
Opportunities
* The model allows for analyzing the impact of logistics decisions on several key aspects of supply chain management:
"The model allows understanding the impact of logistics decisions on transportation costs, inventory costs, customer satisfaction and customer order."
* The article emphasizes that the tool can serve as a platform for simulation and cost analysis, which is very useful in both operational and strategic planning:
"They show also that the model is a well decision-support tool for calculating and analyzing transport and inventory costs."
Threats
3. Main conclusions from the article
The SD model proved useful in demonstrating that logistics decisions have a real impact on costs and system performance:
"The results show that logistic decisions and strategies taken at the supply chain level influence inventory and transport costs."
Additionally, the article emphasizes that the developed model has strong potential as a managerial decision-support tool:
"The model is a well decision-support tool for calculating and analyzing transport and inventory costs."
Article: Dynamical System Modeling for Disruption in Supply Chain and Its Detection Using a Data-Driven Deep Learning-Based Architecture
https://www.mdpi.com/2305-6290/9/2/51
1. Scientific achievement and uniqueness of the study
The authors proposed an innovative system dynamics (SD) model that allows for the analysis of disruptions in the supply chain. They then used the data generated by this model to train a deep learning-based neural network. The novelty of the study lies in the combination of an SD model with a deep learning architecture to enable automated disruption detection.
"This study analyzes and models supply chain disruptions using system dynamics as a key tool, focusing on the disruptions caused by delays in scheduled orders and their impact on service levels within automotive supply chains in Mexico." (p. 1)
"This approach allowed us to capture the dynamic relationships and cascading effects associated with inventory shrinkage at Tier 2 suppliers, highlighting how these delays affect the chain’s overall performance." (p. 1)
"In addition to modeling using system dynamics, a deep-learning-based network was proposed to detect disruptions using the data generated by the dynamic model." (p. 1)
The SD model identified the domino effect, while the neural network was used to detect patterns in the data suggesting disruptions.
2. Strengths and weaknesses of the SD method, opportunities and threats
Strengths:
Ability to reflect cascading (domino) effects:
"This approach allowed us to capture the dynamic relationships and cascading effects associated with inventory shrinkage at Tier 2 suppliers..." (p. 1)
The SD model as a tool for generating training data:
"The simulation results were used to generate a dataset that could be used to train and test different machine learning models to detect supply chain disruptions." (p. 6)
Weaknesses:
Assumption of constant disruption over time (a simplified model):
"The disruption in Tier 2 is modeled as a reduction in the amount of raw materials received by Tier 1 and is considered constant throughout the simulation." (p. 6)
Lack of real industrial data – reliance on synthetic data
Opportunities:
Increasing supply chain resilience by integrating SD with AI:
"This work demonstrates the potential of combining system dynamics and machine learning to improve supply chain resilience." (p. 10)
Application of the model to support managerial decisions:
"This approach can help supply chain managers to detect disruptions early and make informed decisions to mitigate their effects." (p. 10)
Threats:
The effectiveness of the model depends on the characteristics of the data
3. Main conclusions from the article
The SD model effectively simulates the propagation of disruptions:
"The propagation of disruption through the supply chain was successfully modeled using a dynamical system approach." (p. 10)
The integrated system (SD + deep learning) achieves very good classification results:
"The proposed model achieved the highest AUC (0.87) compared to other models used in the study." (p. 9)
The model can be used as a decision support system in logistics:
"This approach can help supply chain managers to detect disruptions early and make informed decisions to mitigate their effects." (p. 10)
Artcicle: System Dynamics Approach to Supply Chain Performance Measurement in Small and Medium Enterprise
https://index.ieomsociety.org/index.cfm/article/view/ID/2686
1. Scientific achievement and uniqueness of the study
The article presents an original application of the System Dynamics (SD) approach for modeling and measuring the performance of a responsive supply chain in a small and medium enterprise (SME). The uniqueness of the study stems from several aspects:
SD was applied in the context of SMEs, which – as noted – "have found it difficult to measure their performance due to the complex external environment."
The study considers the specific limitations of SMEs, such as lack of resources and difficulties in implementing traditional performance measurement models.
Causal Loop Diagrams and Stock and Flow Diagrams were used to visualize the impact of variables such as lead time, product quality, and availability.
"What-if" simulations were conducted, which made it possible to understand system behavior over time when key variables changed (e.g., an increase in domestic cocoa production reduced the need for imports and shortened lead time).
"A system dynamics model is developed from the causal loop diagram of lead-time reduction."
"This SD approach can help managers and top management to know where and on what to make decisions to achieve their responsive supply chain objectives."
2. Strengths and weaknesses of the SD method, opportunities and threats
Strengths:
* SD enables modeling of complex and dynamic systems with feedback loops, which is crucial in the uncertain environment of SMEs.
* It facilitates visualization of cause-and-effect relationships, helping managers better understand the impact of actions on supply chain performance.
* It allows scenario-based simulations that support strategic decision-making.
Weaknesses:
Opportunities:
* SD can serve as a foundation for developing a customized performance measurement system for SMEs that reflects their constraints and environmental volatility.
* It can help companies achieve responsive strategy goals, such as reducing lead time and improving product availability.
Threats:
* Difficulty in maintaining an up-to-date SD model in a dynamic environment and lack of staff competencies:
"The ability of keeping a performance measurement system continuously updated is a challenge for every firm especially SMEs’."
3. Main conclusions from the article
The application of SD in SMEs allows the identification of key variables affecting supply chain performance, especially lead time, product quality, and availability.
Domestic sourcing of raw materials (in this case, cocoa) significantly reduces delivery time and dependency on imports, which is crucial for competitiveness:
"Domestic purchase of the principal raw material (cocoa powder) and producing quality products will help to reduce the lead-time by making products available at all times."
SMEs often lack effective performance measurement systems, which is why system dynamics can be a practical decision-making tool in a challenging environment.
The article highlights the need for process integration, collaboration with suppliers, employee training, and the implementation of IT tools as essential elements for improving performance:
"The company must focus on getting raw materials locally... together with process integration, planning in collaboration with local suppliers, training of workers, good IT structure and market sensitivity."
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