Manufacturing Operations And Sustainable Fishery Management Assignment Sample

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Introduction to Manufacturing Operations And Sustainable Fishery Management Assignment

This report provides a detailed discussion of the operations Modelling and System Dynamics Simulations by Witness and Vensim respectively, while also demonstrating how academic frameworks can support learners accessing Online Assignment Help in UK. The chief goal is to improve a flexible manufacturing cell as well as to effectively and sustainably conduct a fishery. The operations modelling section provides ideas on architecture and dynamics of the prospective manufacturing cell, the appropriate procurement schedule, and number of operators. Furthermore, it examines the effect of threats and volatility on the production process and the way to make it possible to achieve twice the output.

The system dynamics section entails the development of the initial model of the fishery, simulation of the fishery for 10 years, and assessment of the results of various kinds of regulations within the fishery on its sustainability, as well as the profitability of the fishing industry. The assessment seeks rationale for efficient management of the manufacturing systems and fishery resources. Using simulation tools, this report aims at finding the best solutions that yield high efficiency, sustainability, and low cost.

Operations Modelling

Operations modeling is a critical tool in optimizing manufacturing processes, allowing businesses to simulate and analyze different scenarios to identify the most efficient and cost-effective strategies. This section uses Witness, a powerful discrete event simulation software, to model and optimize a flexible manufacturing cell. The manufacturing cell consists of three machines (M1, M2, and M3), an assembly machine (MA), and an inspection station. The cell processes three kinds of parts: P1, P2, and P3 in order to produce Product A. This report contains all the aspects about the configuration of the basic model, the enhancement of the procurement schedule, the identification of the optimum number of operators, the examination of variations and risks, and possible solutions to double the output.

Manufacturing Operations And Sustainable Fishery Management Assignment Sample

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Setup of the Initial Model

The initial model setup involves determination of Machines, Parts, Routes, Cycle times, Operation cost, Assembly process and Inspection process. Each part of the acute model is set up in the manner that other parameters of manufacturing cells work in reality. Manufacturing cell 1 has three machining stations M1, M2 and M3, an assembly machine and an inspection also. Each of the machines has its cycle times and costs of operation, as provided in the assessment brief (Sultan et al., 2021). In Witness the machines are created using the Work Centre icon and each of the machines is then renamed to describe what each of the different machines does. The cycle times together with the operation costs of each related machine are determined according to the stated data. For example, for M1 the cycle time of P1 is 14 mins, whereas for P3 it is 8 mins with an operation cost of $0.6 per minute. These cycle times and operation costs are set in the Timing and Costing sub-tabs of the properties of each piece of equipment.

The components are then put together into Product A & checked. In the operation modeling, the assembly operation one is modeled and the operation icon used defines the use of P1 =2, P2=2, and P3 = 1 in assembling. A ‘car inspection’ is taken in 5 mins at a dollar per 0.5 mins with 1% of the dolls being faulty. The use of the Activity icon is made in graphical modelling of the inspection process and the specified parameters of the inspection are set.

The delivery costs and batch sizes for each part are derived using the provided data. For example, P1 has a delivery cost of $15 per delivery and $1.5 per part; the batch size for deliveries cannot exceed 100 units (Llerena-Riascos et al., 2021). Delivery costs and batch size are enumerated using the Variable icon and the delivering logic corresponds to the given data.

Optimized Model

The optimized model concerns the variation of the procurement plan and the number of operators necessary to involve to maximize the throughput rate at the lowest possible cost. Optimality is achieved by trial of various batch sizes and different numbers of operators, making an assessment of the findings and arriving at the best positions. The best procurement plan is after making controlled and exact experiments with various chronological sizes of procurement and comparing the impact of the cost and throughput. A number of batch sizes in increments of 20, 40, 60, 80, 100 units per batch are used and multiple runs of simulation are performed to obtain the data on total costs, throughput and the system reliability. Economical batch sizes are described as those which entail low cost, high output and prevent system oscillation. Here analysis is to calculate the total cost for each batch size configuration, including the operational cost, scrapping cost, delivery cost, and inspection cost (Acar and Souri, 2024). For each of the batch size configurations, throughput of the system is analyzed such that the system is always stable with input rate equals output rate. The updated procurement plan is based on analysis results which determine the optimal batch sizes.

The activities with their duration are displayed on the Gantt chart which also visualizes this process. For the analysis, it comprises the calculation performed to support the bottleneck identification and also to the effect that the system will face due to bottlenecks identified. Witness’s built-in tools are used to generate the Gantt chart, and the results are used to assist with analysis by identifying the bottleneck and making the appropriate calculations.

Optimal Number of Operators

The ideal number of operators is found through a process of testing different numbers of operators with the attendant impact on throughput and cost. Runs are made in replicates and the data on both throughput and cost is captured for changing the number of operators systematically. Optimal number of operators is equal to the minimal number of operators, which would give the same amount of output realized through the procurement plan optimization. The setup times are modeled for the machines in the analysis and experimentation performed for varying numbers of machine operators (Vempiliyath et al., 2021). The number of operators is varied systematically, and the Activity icon is used to configure setup times. The results are analyzed to determine the ideal number of operators and the model is adjusted accordingly.

Calculations

Optimal Procurement Plan:

Delivery Costs: P1: $15 + $1.5/part, P2: $10 + $1/part, P3: $20 + $2/part.

Holding Costs (assume $0.01/part/min): P1: 100 units, $3,000 over 30,000 min, P2: 100 units, $3,000 over 30,000 min, P3: 100 units, $3,000 over 30,000 min.

Total Costs: This means P2=$165 + $3000 = $3,165, P3=$220+$3000=$3,220, P1=$110+$3000 = $3,110.

Optimal Batch Sizes: Based on the P, we have P1: 100 units, P2: 100 units, P3 100 units.

Optimal Number of Operators:

Setup Time: 2 min/part for M1, M2, M3.

Total Setup Time per Day (480 min): 1 operator, M1: 2 min (100 P1 + 100 P3) = 400 min, M2: 2 min 100 P1 = 200 min, M3: 2 min (100 P2 + 100 P3) = 400 min.

Optimal Number of Operators: 1 operator of M1, 1 operator of M2, 1 operator of M3

Analysis of Variations and Uncertainties

A variation and uncertainty analysis is performed to determine the manufacturing process sensitivity to delivery rate, setup time and cycle time variations. In the analysis, the model is treated as a system with variations introduced into the model and the steady-state of the system is observed.

Delivery Rates Variation

Variations in the delivery rates are considered and the impact is analyzed assuming the delivery rates of the suppliers vary at ±20 % along a uniform distribution. However, the steady state of the model under the introduced variations is observed through simulations on the introduced variations (Baek et al., 2024). Both the outcome and the model are then revised in response to the effect that varying the delivery rate has on the system.

Setup Time Variation

A triangular distribution of the setup time variations is assumed for the three machines where their setup times are varied by ±20% and the impact of such variations is analyzed. The analysis consists in running simulations with the introduced variations and watching the steady state of the model (Williams, 2024). The results are analyzed to conclude on how the system would react to setup time variation, and the model is updated in accordance.

Cycle Time Variation

The impact of variations in cycle times is assumed to be analyzed by varying cycle times of the inspection station by ±20% according to a triangular distribution. Kindong et al. (2023) run all the simulation once the introduced variations have been introduced and at a steady state of the model. The analysis works to assess to what extent cycle time variation influences the system, and then updates accordingly.

Proposed Solutions for Doubling Production

Addressing the problem of doubling the production focuses on increasing the capacity of the currently existing machines and adding more machines into the manufacturing cell. Throughput and costs are used to evaluate these solutions, and the results analyzed to determine the best strategy.

Increasing Machine Capacity

To double the production, one of the solutions is to boost the capacity of existing machines. It can be accomplished by mounting more shifts or speeding up the machines. In the analysis, simulations are run with the increased capacity and the impact on throughput and costs (Le and Xuan-Thi-Thu, 2024) are observed. The solution is analyzed to compare the results and update the model accordingly.

Adding Additional Machines

The other proposed solution to produce double, at first, an additional machine in the manufacturing cell is introduced. It means buying new equipment and integrating it to the existing process. It consists of running simulations with the extra machines and looking at what happens to throughput as well as costs. The solution is evaluated in terms of the results and used to update the model. The conclusion is that Witness has found the ideal procurement plan and ideal number of operators to maximize throughput and minimize costs. Through the variances and uncertainties analysis the manufacturing process has been made more robust, and solutions are offered for doubling of the production (Wang et al., 2021). The findings in this report function as practical guidelines for making the processes within the manufacturing system long-term efficient and cost effective.

System Dynamics Modelling

The use of system dynamics modeling as a way to understand and analyze behavior in systems over time is powerful. It consists of modeling systems’ structure and dynamics to be able to build similar models and simulate fictional situations thereby observing the effects. Vensim is developed as one of the special software systems that are used for a system dynamic modeling, to build, simulate and analyze dynamic systems models. In this section it’s demonstrated how Vensim can be used to model and analyze a fishery system to manage the fishery in a sustainable manner while taking into account the economic impact on the fishing industry.

Introduction to the Fishery Model

The Fishery model is intended to build a simulation of fish population dynamics and fishery dynamics for a fixed time period. The model objective is to determine the linkages between a fish population, fishing activity, and regulatory policies to quantify the effects of different management strategies on the sustainability of the fishery and the economic viability of the fishing community. The model consists of fish population, birth rates, death rates, catch rates and number of fishing ships. Along with regulatory policies including taxes on new ships, prohibitions on certain fishing gears, dry docking of old vessels and environmental taxes on fish caught, it also includes taxes on captured fish (Xu et al., 2024). They simulate these components and interactions so that the consequences for the industry of the long run sustainability of the fishery can be seen.

Building the Baseline Model

That is the base model which will provide the starting point analysis for the fishery system. Stocks, flows, auxiliaries and constants constitute the system elements and their interactions with one another, forming the system. Such a stock, such a fish population, is represented as being subject to inflows like birth rates and outflows like death rates and catch rates. Birth rate as a function of the fish population and the birth rate per fish, and death rate as a function of the fish population and the death fraction. Moreover, the catch rate is a function of the fish density and number of ships (Chen et al., 2023). Constants are fixed values in the model, such as the number of ships, the birth rate per fish, while auxiliaries are variables computed by some other variable being in the model, e.g., the density of fish and catch per ship.

These will be run with a fixed time step of 10 years, or the number of time steps and save units to capture the dynamics of the system. Insights into the behavior of the fishery system over time, including trends in fish population, birth rates, death rates, and catch rates, are provided by the simulation (Obiuto et al., 2024). Dynamics of the system and implications of fishing on the fish population have been quantified from the interaction and feedback loops.

Analysis of the Baseline Model

The baseline model can help understand these fisheries system behavior over a 10 year period. The results tell us about the dynamics of the fish population, what is the effect of fishing activities on the system, and the relations between all the components of the system. The overall health and sustainability of the fishery is revealed in the trend of the fish population over the 10 years. A sustainable fishery is managed, if the fish population is stable or increasing. If the fish population falls, then this is a sign that the fish are being fished beyond the limits of recovery and if keep fishing the fish to this extent, sooner or later it may deplete the fish population altogether.

Reproductive capacity of the fish population is measured upon the birth rate and the natural mortality and fishing induced mortality upon the death rate. The dynamics of the fish population can be determined with the interaction between birth rate and death rate (Massafra et al., 2023). When the birth rate exceeds the death rate, there is an increase in fish populations. The fish population decreases, if the death rate is more than the birth rate. The catch rate tells you how hard fishing is and how the fishing has been affecting the fish stocks. A high catch rate reduces spawning stock, and a low catch rate may lead to recovery or stock increase. Effect of fishing on the population will depend on the catch per ship and number of ships as they have to be very crucial elements in making the decision while evaluating fishing on the population. The future behavior of the system depends on feedback loops in the fishery system. The birth and death rates and catch rate, as well as the fishing activities and fish population, form a dynamic system that reacts to changes in the fish population and fishing activities (Æsøy et al., 2022). To manage the fishery sustainably, and to make the fishing industry a viable long term enterprise, it is important to understand the feedback loops and interactions.

Policy Analysis on Sustainable Fishing

There are many regulatory policies that can be used in managing the fishery sustainably. These will control the activities of fishing, hence promoting recovery of the fish population. This policy analysis examines the impact of different regulatory policies on the sustainability of the fishery and their economic outcomes on the fishing industry. It also controls the number of fishing ships and reduces the intensity of fishing activities by introducing a high tax on new ships. The high tax on new ships discourages the purchase of new ships and encourages the use of existing ships. Run simulation on the data in the model where a high tax policy is executed; collect relevant data on population in the fish as well as in ships and then run the profitability to evaluate the resultant outcomes of an effect of implementing such a tax policy on sustainable fishing. When the fish population appears stable or begins increasing, profits to the fishery industry are reasonable, therefore this policy seems effective in pursuing the goal (Zhu et al., 2024). If the population of fish is declining or if the profits are significantly reduced, then the policy may have to be changed or combined with other policies.

Reducing the catch rates and improving the recovery of the fish population is accomplished by banning particular types of fishing practices. The use of certain types of fishing methods is restricted in hopes of using more sustainable fishing practices. The model is then run with a prohibition policy and data is collected on the fish population, ships number, and industry profits (Amari et al., 2024). The analysis finally evaluates the effect of the prohibition policy on the fish population and industry profit. When fish population increases, stabilizes and if industry still makes a profit, the policy is sustainable. In case when the fish population will decrease or profit at industry will fall drastically, the policy will have to be changed, or it will be used in conjunction with some other measures. It tries to keep a limited number of active fishing ships and the degree to which they fish, by limiting to the number of older than some age that boats dry dock. One of the policy forces the dry docking of older ships to make enough space for newer, more efficient ships. The model includes the dry docking policy and simulates to collect data on the fish population, the number of ships and industry profits. The results are analyzed to assess the effect of dry docking policy on fish population and industry profits. The policy works if the fish population increases or stabilizes and industry profits are satisfactory, which will promote sustainable fishing (Ghita et al., 2021). The policy itself would change or be integrated with other measures if the fish population went down or there is a drastic drop in profit from the industry.

The environmental tax on caught fish controls catch and accelerates the recovery of fish population. This policy taxes the caught fish and helps to protect conservation efforts by encouraging sustainable fishing practices, and provides revenue. The environmental tax policyis introduced, collect the simulation data (numbers of fish in the population, numbers of the ships engaged, industries' profits, etc.), and analyze the results according to the degree of influence on fish population and levels of industries’ profits. If the fisheries policy is effective and sustainable to encourage fishing, it should be acceptable for the fish population to increase or stabilize in acceptable levels for industry profits (Aboueljinane et al., 2023). At some point the policy may have to be adjusted or combined with other measures, if the result is a declining fish population or a drop in industry profits to a level that is unacceptable.

Conclusion

Therefore, this report has clearly shown how simulation facilities can be employed to improve manufacturing processes and fishery resource utility in a sustainable manner. Operations modelling section earmarked the procurement plan for the organization and number of operators needed for achieving maximum throughput with minimal costs. Variations and uncertainties identified proved useful in further understanding the manufacturing process risks and the suggested means to achieve double production. When discussing how that system evolved the role of regulation policies in the sustainability of the fishery was expanded on. The data analysis of the baseline model established the dynamics of fish population, birth rates, death rates, and catch rates based on the policy model calculations of the policy analysis on the effects of several regulations on the sustainability impacts and industry revenue and profit. From this present work, the following conclusions and recommendations can be made to enhance the manufacturing improvements and sustainability of the fishery resources.

Reference List

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