6E6Z0004: Business and Operations Management Assignment Sample

Introduction to Witness Horizon Simulation Assignment Sample

This assessment highlights the manufacturing simulation model, which has been estimated with the help of Witness Horizon to produce skateboards within eight weeks. The layout, which accords with the business and manufacturing operations plan developed in task one, is the actual version of the manufacturing line that has been duplicated to make a simulation model on which the proposed manufacturing strategy can be validated in real time.

The model details the flow of operations in skateboard production, from the point of selecting the wood material to packaging, including quality control checks. The described manufacturing stages include the corresponding statistical distribution to reflect the overall variability of the process based on actual conditions. It involves material receiving bins, storage places, processing equipment, conveyors, handling equipment, people, shift timings, quality control measures, and disposal.

One of the major highlights of this simulation is the built-in KPI Dashboard which gives an immediate insight on performance indicator measures at intervals for production effectiveness, resources, and bottlenecks. The model also reveals that it is possible to produce 4,074 skateboards within the period of 8 weeks with capacity and quality constraints. This system provides both an analysis tool and a means for managing the manufacturing of skateboards.

This Witness Horizon Simulation Assignment Sample explores an eight-week skateboard manufacturing process, covering production workflows, quality control, material handling, and simulation modelling. An Assignment writing helper can support students in understanding manufacturing operations, process variability, and strategies for improving production efficiency.

Methodology

1. Data Collection and Analysis: The first stage included gaining a broad understanding of data about the entire process of manufacturing skateboards in the company. This involved time taken in each of the 16 activities that constituted the manufacturing processes from the selection of wood to packing. All processes were reviewed to identify its mean cycle time range, required labour and tools. Based on the data collected, a distribution was estimated to show the variations in the process time (Krynke, 2021). For instance, the distribution used for the wood selection process which requires 3-4 minutes, is uniform U(3,4).
2. Classification: After completing the data analysis process, a conceptual definition of the manufacturing system for building a model was developed. The manufacturing flow chart depicted all the activities required in the production of skateboards as a series of operations that are connected by precedence relationships. Much emphasis was laid on the three quality control points where products could be sorted to the recycling point if they failed to meet the required quality at stages 4, 10 and 15. The production parameters such as; single shift production, time for tea/ lunch breaks, time taken for any kind of maintenance as well as labor force were included in the conceptual model.
3. Model Flow Development: The sequence of the manufacturing process was modeled in Witness Horizon following the manufacturing process of skateboards. The model consisted of sixteen stations simulating each operation, with buffers in between that range with the goal of emulating cycle time. Incorporation of three decision points to represent random screening exercises that are done to reject defective items were made (Koulouris et al., 2021). It was supposed to be made to accommodate the specified production schedule of 8 hours a day, five days a week for eight weeks with allowances for time off from production and for maintenance breaks.

1. Process creation and mapping: All the above processes were developed in Witness Horizon as a machine element with cycle time distribution. Since work-in-progress inventory is considered undesirable, buffer elements were created between work processes. Labour resources were established as sundry entities that get incorporated into the descriptions whenever it is needed in the course of the workflow performed by machines (Kurbanzade et al., 2023). The elements in the skateboard system at various levels of production were described to fit several attributes to enable tracing of their status. Conveyors were used to depict material movements between some processes when the distances were long.
2. Work organization: Effective workflow was organized ,aiming at controlling the movement of material through the production system in line with the set knitting logic. These were the routing logic of parts that were rejected at quality checks, shift calendar for labor, machine usage calendar according to its maintenance needs, and changeover time for those that require expiry of a certain cycle. Batching operation was separately done for the pressing stage where seven veneers are placed together to make one skateboard deck.
3. Minimal Element Integration: To achieve realistic results, stochastic aspects were included in the simulation process. Some factors, such as time taken in a process, quality inspected results and frequency of machine failure, were modeled appropriately with some probability distributions (Eibeck et al., 2024). For instance, the quality inspection process was done using Bernoulli distribution; the probabilities of passing and failing were used as probabilities; process times were done using uniform or triangular distributions,depending on the time ranges given.
4. Verification and validation: The actual stage included verification of the overall structure and organization of the completed model to assure that it was consistent with the conceptual model and along with it, validation was done according to goals set in the business plan for production. This included the processes such as going through the flow of logic in the model, comparing the simulation results with theoretical results and differentiating and values, and running analyzes in order to test alteration of fundamental parameters in order to verify the basic behavior model of the system (Afifi et al., 2022).

1. Experimentation: Several simulation experiments have been run to AVI to generally examine many of the production aspects and to identify possible improvements. This was done in the form of changes in staffing, shift working, buffer stock, and quality control limits (Gejo-García et al., 2022). Each experiment was carried out to the length of 8-week’s production time and each run was preceded with warm-up time to negate any effects of initialization.
2. Outcome Analysis and Communication: Throughput, resource usage, cycle time, as well as quality performance measures were post-simulation gathered for evaluation. To support these KPIs, a dashboard was created in the Witness Horizon system that would help monitor simulation performance on an ongoing basis (Magnanini and Tolio, 2021). To achieve this, the organization developed charts and graphs to display the production rates so as to determine areas that needed improvement.

Simulation Results

The Witness Horizon simulation model proves useful in identifying the performance of the manufacturing process in the production of skateboards over the weeks 1 to 8. From the simulation results, it is realized that there are certain critical parameters in the operation of the system and their problems.

According to the utilization report of the machines, there is a lot of disparity throughout the line of production. Within the first steps (Lamination, Press001, Check001, Drill001, Shaping, Contouring) more satisfactory utilization rates are established but not the highest. However, the downstream process reveals more worrisome behaviour: the Packing and the Final Assembly stations are choked at 99.982%, which means severe downtimes are present (Psarommatis, 2021).

Trunk Assembly seems to be nearly unused, which also implies lack of synchronizing between production phases and ineffective work in progress inventory control. Such state of affairs generates costly and ineffective resource utilization and flow rates that hamper production throughput.

This makes the total of the findings to be $31,493.00; the highest contribution to making up the cost is the Press001 at $4,608.00 and the employees whose total cost is $10,643.80. Fixed expenses were higher, hence occupying the higher percentage of 71%, amounting to $29,545.00, while variable expenses were relatively low, amounting to 29%, being $1,948.00; therefore, pointing towards the potential increase in the operational efficiency to boost up profitability.

Using data contained within the model, it has been confirmed that all the elements of the process are connected correctly from the woodselection level till the final packaging level, and the material passes through the machines, buffers, and conveyors as planned (Li et al., 2021). Nevertheless, such blocking percentage signals that this flow cannot be regarded as optimized.

The following production flow should be pursued to increase manufacturing effectiveness:

• (1) Restoration of workload balance among workstations,
• (2) modification of buffering stock levels between operations,
• (3) reallocation of workforce, and
• (4) adoption of pull systems.

These results show that, although the basic model of the process is established and correct, operational improvements have to be made to meet the production targets set within two 8-week periods.

Conclusion

In conclusion the approach of simulation has been covered with a wider range of timing for the considerable high amount of the product. The consideration of the skateboard has some number of the defects which is also considered for the resume. The method of the reuse should be applied with the various considerations of the recycling and re-modification of the wooden systems. The Consideration. The approach is to consider the nature of the proper consideration of the volume of manufacturing. This consideration should be applied with the extended volume for the approach.

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Reference List

Journals

• Afifi, M., Fotouh, A., Al-Hussein, M. and Abourizk, S., 2022. Integrated lean concepts and continuous/discrete-event simulation to examine productivity improvement in door assembly-line for residential buildings. International Journal of Construction Management, 22(13), pp.2423-2434.
• Eibeck, A., Shaocong, Z., Mei Qi, L. and Kraft, M., 2024. Research data supporting" A Simple and Efficient Approach to Unsupervised Instance Matching and its Application to Linked Data of Power Plants".
• Gejo-García, J., Reschke, J., Gallego-García, S. and García-García, M., 2022. Development of a system dynamics simulation for assessing manufacturing systems based on the digital twin concept. Applied Sciences, 12(4), p.2095.
• Koulouris, A., Misailidis, N. and Petrides, D., 2021. Applications of process and digital twin models for production simulation and scheduling in the manufacturing of food ingredients and products. Food and Bioproducts Processing, 126, pp.317-333.
• Krynke, M., 2021. Personnel management on the production line using the FlexSim simulation environment. Manufacturing Technology, 21(5), pp.657-667.
• Kurbanzade, A.K., Baig, A.M. and Mehrotra, S., 2023. Elevating Industries with Unmanned Aerial Vehicles: Integrating Sustainability and Operational Innovation. arXiv preprint arXiv:2312.13551.
• Li, W., Liang, Y. and Wang, S. eds., 2021. Data driven smart manufacturing technologies and applications. Berlin/Heidelberg, Germany: Springer.
• Magnanini, M.C. and Tolio, T.A., 2021. A model-based Digital Twin to support responsive manufacturing systems. CIRP Annals, 70(1), pp.353-356.
• Psarommatis, F., 2021. A generic methodology and a digital twin for zero defect manufacturing (ZDM) performance mapping towards design for ZDM. Journal of Manufacturing Systems, 59, pp.507-521.