Introduction
In this executive summary, I demonstrate my competence and knowledge of the analytical skills in the supply chain I acquired and learned during the Supply Chain Decision Analytics unit, outlining my experiences and learnings with anyLogistix (ALX) software. Decision-making and optimization in the supply chain are crucial in ensuring business efficiency, profitability and sustainability.[1] Consequently, optimizing the supply chain and making informed analytics decisions enable end-to-end quality management, from procurement of raw materials to customer delivery. First, I outline and reflect on my general understanding of supply chain optimization and simulation. Next, I outline my understanding of GFA, NO and SIM in ALX. After that, I outline how ALX can help SC managers improve the decision-making process. In addition, I give one example as a case study of possible SC management problems. Lastly, conclusion and recommendations are given.
Supply chain optimization and simulation, in general
My understanding is that at the most fundamental level, supply chain optimization involves all processes that help improve supply chain performance and efficiency. It involves the tools and processes by which the supply chain performance and efficiency are enhanced, considering all constraints and resources at hand.[2] The optimization is based on specific key performance indicators, including the overall operating costs and yields on the company’s inventory. I understand that optimizing these processes allows it to function at peak efficiency at the lowest total price possible while maintaining maximum profit yields. To achieve these, SC managers must balance expenses incurred and fulfilling customers’ expectations. According to Malinovskaya, supply chain optimization seeks to solve various supply chain challenges at strategic and tactical levels.[3] The strategic challenges involve the size and location of the manufacturing plants or distribution centres, the structure of service networks and supply chain design. On the other hand, tactical supply issues involve production, transportation and inventory planning challenges that must be balanced with supply and demand. However, I learned that even though operational issues might occur in the supply chain, the optimization process does not address these issues such as production scheduling, control, and vehicle routing.
Alongside supply chain optimization, simulation has mostly been applied for strategic planning. Ivanov asserts that simulation models are widely used to evaluate disruption propagation and the ripple impact across multiple tiers.[4] The leading benefit of simulation is the capability to consider real-time operation or the duration of the disruption when analyzing recovery policies towards improved supply chain performance and efficiency.[5] That is, simulation enables a more detailed view of network operations which cannot be achieved through optimization by adding dynamics to the models, such as situational behaviour changes. Therefore, this results in solving business challenges that could not be solved otherwise, including disruption propagation in the supply chain, such as time and levels, dynamic recovery policies, and multiple performance impacts dimensions such as financial, service level and operational outcomes. Choosing the best route optimization software depends on various factors, such as the approach’s performance objectives, available resources, and technical requirements. One of the most widely used simulation software is ALX which supports advanced optimization algorithms for accurate results in optimizing supply chain performance and efficiency.[6] In the next section, I discuss and outline my understanding of ALX software covered in the unit, reflecting on my understanding of GFA, NO and SIM in ALX.
GFA, NO and SIM in ALX
My experiences with the ALX simulation software taught me that we could optimize inventory and service levels across the supply chain, examine facility utilization, test replenishment and sourcing policies, analyze demand fluctuations and their impact on the supply chain and examine carbon footprints. One of the most used analyses is the Greenfield Analysis (GFA). Also referred to as centre-of-gravity analysis, GFA is used by companies to determine the optimal number and location of distribution centres.[7] In other words, GFA examines market and industry conditions like the demand and customer locations to determine the number of distribution centres ideal, the optimal location and the size to meet the defined service level. In ALX software, we represent customer location by an ordered pair of (x; y) coordinates.[8] These are input data or problem parameters that cannot be changed.
On the other hand, the new warehouse’s (x; y) coordinates (px; py) are variables that can be changed, and ALX determines them after computing the data provided to match the given parameters. In addition, we assume that the transportation cost is linearly proportional to the distance and demand – depending on the coordinates (px; py) of the prospective warehouse location and distance. Using GFA in ALX results in increased efficiency and cost reduction, reduced lead times and evaluation of potential growth prospects.[9]
Moreover, during the unit, I acquired crucial skills and competency in Network Optimisation (NO) analysis with ALX. Unlike GFA, which is built for speed, NO is built for detail.[10] In supply chain optimization, NO is used when modelling costs, capacities, constraints and policies. Also, it can be used alongside GFA to refine Greenfield potential networks. In the case of ALX, NO seeks to find an optimal combination of factories and distribution centres in the supply chain.[11] The best solution should match supply and demand in addition to finding a network configuration with minimal expenses. As a result, based on the computed values, a supply chain manager can compare potential network designs and analyze the highest profitability of each one to choose the one with the maximum yields.[12] The first step in NO with ALX is to input the customer data and distribution centres. The second step is to create and define each customer’s periodic demand. The third step is to define the cost variables for the existing supply chain. Lastly, after inputting the required data variables, we run the simulation. The simulation determines the optimal supply chain design to show the values of transportation and production flows, inventory at the end of each period and associated costs. In addition, the results give data on several potential network configuration options from which the manager can select the one with the minimal expenses or which fits the business to implement in the real business world.
Using ALX instilled me with Simulation (SIM) analysis skills in the supply chain. This involves using a set of objects and rules that describe dynamic behaviour and their interaction to represent the supply chain. In ALX, the SIM experiment is a simulation scenario based on the optimization outcome converted to the SIM scenario to analyze the network suggested at the NO stage in more detail. The ALX allowed me to SIM the actual product delivery on the map and with detailed statistics, which collected corresponding data during the experiment from different types of facilities involved in the supply chain scenario[13]. This is useful, especially when the operational logic and processes inside the supply chain have a substantial effect on financial performance and efficiency and need to be optimized during the supply chain design phase. Since the SIM imitates the dynamic behaviour of one system with another, by altering the simulated supply chain, we can better understand the physical dynamics, which is crucial in visualizing the processes and structures as well as bottlenecks before implementing the system in the real world.
How ALX can help SC managers to improve the decision-making process
Decision-making in supply chain management entails using qualitative and quantitative methods to make informed and accurate decisions that ultimately improve supply chain performance and efficiency. Through my experiences and learnings with ALX, I gained various insights into how the use of ALX can help SC managers improve their decisions in different supply chain management domains. In the facility location planning domain, SC managers can implement GFA and NO computation using ALX to decide on the best location and number of distributions based on demand and the minimal costs to yield the maximum profit.[14] I perceive that this enables managers to improve efficiency, reduce lead times and minimize costs. In inventory planning management, ALX can help SC managers to analyze facility inventory dynamics and examine varying demands in unpredictable business environments.[15] SC managers can use variable factors such as delivery time and demand volume to evaluate how they will affect supply chain resilience in a virtual environment before implementing it in a business environment. Thus, informed decisions can be made on stock estimation, inventory management levels, and predicting market demand volatility.
Subsequently, SC managers can benefit from ALX to arrive at better decisions on route and transportation optimization. Decisions on route optimization revolve around customer demand, customer locations, vehicle availability, date of service delivery, and hours of service restrictions for drivers. With ALX, SC managers can create a model with an optimized route and benefit from a detailed transport route schedule that helps track order status throughout the supply chain. In addition, SC managers can improve their fleet utilization and planning efficiency through ALX.[16] The ALX can compute the available vehicle and, based on their characteristics, choose the most appropriate and compute the best routes to ensure SC managers manage the customer delivery demand on time. Lastly, by testing transportation policies on ALX, SC managers can make informed decisions on improving current policies to maximize fleet utilization and support operation decision-making for each trip.[17]
Possible SC management problems of SIM in ALX
In spite of the various benefits and opportunities of using SIM in ALX to improve supply chain performance and efficiency, there are several possible SC management problems in the process. The first possible SC management problem of SIM in ALX is associated with data quality and preparation. SIM using ALX is time-dependent and requires quality and accurate data such as on sourcing of products, inventory policies and expenses incurred. Adequate data preparation is needed to simulate the most effective scenario. As a result, the company must invest a lot of time and effort in gathering quality and accurate data to create a precise model. Creating a SIM scenario from scratch is a laborious process which requires accurate data for detailed statistics generated in real time.
Another possible SC management problem of SIM with ALX is that we can only test different what-if scenarios to determine which is better but cannot compute the optimum.[18] We can run what-if scenarios by changing the inventory policy, such as altering the minimum and maximum values to analyze the optimal product stock volume, configure inventory policies and eliminate the chance of lost orders. However, unlike GFA and NO, which can be used for analytical and mathematical optimization, we cannot simulate the optimum supply chain. Therefore, we must run the simulation many times to achieve a near-optimal choice, which requires huge time and expenses. Especially for data-intensive models, it can take several days. Therefore, other analytical and mathematical SC optimization approaches, such as GFA and NO, are recommended.
Conclusion and recommendations
In conclusion, ALX software is one of the most effective supply chain simulation software widely used to offer insights into network dynamics and describe the details and complexity of a supply chain system. GFA, NO and SIM with ALX offer opportunities to SC managers to optimize the SC by identifying the best locations to set up distribution centres, finding the exact locations of the distribution centres and undertaking simulation experiments to optimize the supply chain. As a result, SC managers can make informed decisions on facility location planning, capacity planning, inventory management, transportation planning and policies and sourcing policies.[19] However, making informed decisions requires accurate data input on different input parameters. In this essence, one of the critical recommendations for using ALX in SC optimization is to ensure accurate and quality data to ensure the optimization output accurately represents real-world scenarios. In this case, no matter how good the ALX is, using data with low accuracy will result in highly uncertain results, which can cause a ripple effect to the entire supply chain. Thus, using highly validated and accurate input data is critical for having the best SC optimization using ALX.
Bibliography
Alnabet, Aisha. “Optimizing The Distribution Centre Locations For Agrico Food Company Using Anylogistix Simulation.” Master’s thesis, 2023.
AnyLogistix. “Simulation-Based Inventory Planning for the Digital Supply Chain Era.” (2023).
AnyLogistix. “Supply Chain Optimisation and Simulation- Technology Overview.” (2023).
Arisha, Amr, and Waleed Abo-Hamad. “Simulation optimization methods in supply chain applications: a review.” Irish Journal of Management 30, no. 2 (2010): 95-124.
Barykin, Sergey Yevgenievich, Andrey Aleksandrovich Bochkarev, Olga Vladimirovna Kalinina, and Vladimir Konstantinovich Yadykin. “Concept for a supply chain digital twin.” International Journal of Mathematical, Engineering and Management Sciences 5, no. 6 (2020): 1498.
Ivanov, Dmitry, Alexander Tsipoulanidis, Jörn Schönberger, Dmitry Ivanov, Alexander Tsipoulanidis, and Jörn Schönberger. “Facility location planning and network design.” Global Supply Chain and Operations Management: A Decision-Oriented Introduction to the Creation of Value (2021): 171-222.
Ivanov, Dmitry. “Advanced skills in CPLEX-based network optimization in anyLogistix.” (2023).
Ivanov, Dmitry. “Managing risks in supply chains with digital twins and simulation.” White Paper (2018).
Ivanov, Dmitry. “Operations and supply chain simulation with AnyLogic.” Berlin: Berlin School of Economics and Law (2017).
Ivanov, Dmitry. “Simulation-based ripple effect modelling in the supply chain.” International Journal of Production Research 55, no. 7 (2017): 2083-2101.
Luthra, Sunil, Kannan Govindan, Devika Kannan, Sachin Kumar Mangla, and Chandra Prakash Garg. “An integrated framework for sustainable supplier selection and evaluation in supply chains.” Journal of cleaner production 140 (2017): 1686-1698.
Maheshwari, Pratik, Sachin Kamble, Amine Belhadi, Cristina Blanco González-Tejero, and Sunil Kumar Jauhar. “Responsive strategies for new normal cold supply chain using greenfield, network optimization, and simulation analysis.” Annals of Operations Research (2023): 1-41.
Malinovskaya Anastasiya. “Supply Chain Optimisation Explained – With Example.” anyLogistix [online] (2021). https://www.anylogistix.com/resources/blog/supply-chain-optimization-explained-with-example/
Marmolejo-Saucedo, Jose Antonio. “Design and development of digital twins: A case study in supply chains.” Mobile Networks and Applications 25, no. 6 (2020): 2141-2160.
Xu, Lai, Paul Ton de Vrieze, Rushan Arshad, and Oyepeju Oyekola. “Enhance Supply Chain Resilience through Industry 4.0–A view of designing simulation scenarios.” (2022).
[1] Luthra, Sunil, Kannan Govindan, Devika Kannan, Sachin Kumar Mangla, and Chandra Prakash Garg. “An integrated framework for sustainable supplier selection and evaluation in supply chains.” Journal of cleaner production 140 (2017): 1686-1698.
[2] Arisha, Amr, and Waleed Abo-Hamad. “Simulation optimisation methods in supply chain applications: a review.” Irish Journal of Management 30, no. 2 (2010): 95-124.
[3] Anastasiya Malinovskaya, “Supply Chain Optimisation Explained – With Example.” anyLogistix [online] (2021). https://www.anylogistix.com/resources/blog/supply-chain-optimization-explained-with-example/
[4] Dmitry Ivanov, “Operations and supply chain simulation with AnyLogic.” Berlin: Berlin School of Economics and Law (2017).
[5] Ivanov, Dmitry. “Managing risks in supply chains with digital twins and simulation.” White Paper (2018).
[6] Dmitry Ivanov, 2017.
[7] Ivanov, Dmitry, Alexander Tsipoulanidis, Jörn Schönberger, Dmitry Ivanov, Alexander Tsipoulanidis, and Jörn Schönberger. “Facility location planning and network design.” Global Supply Chain and Operations Management: A Decision-Oriented Introduction to the Creation of Value (2021): 171-222.
[8] Dmitry Ivanov, 2017.
[9] Alnabet, Aisha. “Optimizing The Distribution Centre Locations For Agrico Food Company Using Anylogistix Simulation.” Master’s thesis, 2023.
[10] Maheshwari, Pratik, Sachin Kamble, Amine Belhadi, Cristina Blanco González-Tejero, and Sunil Kumar Jauhar. “Responsive strategies for new normal cold supply chain using greenfield, network optimization, and simulation analysis.” Annals of Operations Research (2023): 1-41.
[11] Barykin, Sergey Yevgenievich, Andrey Aleksandrovich Bochkarev, Olga Vladimirovna Kalinina, and Vladimir Konstantinovich Yadykin. “Concept for a supply chain digital twin.” International Journal of Mathematical, Engineering and Management Sciences 5, no. 6 (2020): 1498.
[12] Marmolejo-Saucedo, Jose Antonio. “Design and development of digital twins: A case study in supply chains.” Mobile Networks and Applications 25, no. 6 (2020): 2141-2160.
[13] Ivanov, Dmitry. “Advanced skills in CPLEX-based network optimization in anyLogistix.” (2023)
[14] Dmitry Ivanov, 2017.
[15] AnyLogistix, “Simulation-Based Inventory Planning for the Digital Supply Chain Era.” (2023).
[16] AnyLogistix, 2023.
[17] Ivanov, Dmitry. “Simulation-based ripple effect modelling in the supply chain.” International Journal of Production Research 55, no. 7 (2017): 2083-2101.
[18] AnyLogistix, “Supply Chain Optimisation and Simulation- Technology Overview.” (2023)
[19] Xu, Lai, Paul Ton de Vrieze, Rushan Arshad, and Oyepeju Oyekola. “Enhance Supply Chain Resilience through Industry 4.0–A view of designing simulation scenarios.” (2022).
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