Scenario-Based Strategic Capacity Planning for Powder Mixing Plant Using Discrete Event Modelling

Sales volume during the 2022–2024 period showed a positive upward trend, driving the company to increase machine capacity in order to enhance production and minimize opportunity losses. This study explores optimal strategies for increasing machine capacity using simulation method. Sales volume forecast data available from 2025 to 2035 is used as input for production simulations conducted using the Discrete Event Simulation (DES) method to determine the number of machines that need to be added, capacity increases, and the timing of machine additions. Scenario strategy variations are created based on the time horizon and method of adding machine capacity. The time horizon is medium term (five years) and long term (10 years). The method of adding machine capacity is through gradual and simultaneous additions. Machine utilization rates are used as a basis for capacity expansion. The research results indicate that the most effective scenario is Scenario 1 strategy of gradually adding machines over a period of 5 years, which involves adding two machines in 2026, increasing capacity by 325 million pcs/year and adding one machine in 2028 increases capacity by 162 million pcs/year. This scenario’s utilization pattern shows the best stability, ranging between 73%–78% throughout the 5-year period, without experiencing drastic fluctuations or significant periods of underutilization. By implementing the strategic recommendations, the company can optimize production processes in response to external uncertainties such as sales volume fluctuations.

Introduction

The company in this case is a powder mixing plant that produces powdered seasonings for instant noodles. Consumption of instant noodles in Indonesia is increasing, leading to an increase in the production of powdered seasonings. This is because every instant noodle contains powdered seasonings. In view of the growth in instant noodle consumption, which will impact seasoning production, and based on data showing that instant noodle consumption continues to rise alongside Indonesia’s rapid population growth reaching a total of 14 billion servings in 2023 (World Instant Noodle Association, 2023).

At the present time, the business sector is still fighting to develop a wide range of client wants that are getting higher and more sophisticated in their selection. To keep ahead of the competition, any business must optimize its production, efficiency, quick service, ease of use, and creation of new information. A corporation needs to plan its capacity to fit with its business strategy in order to remain competitive (Nurcahyo & Dhewanto, 2016). Population growth can be an opportunity for companies to enhance production. There are five types of products: A, B, C, D, and E. Historically, sales volume from 2022 to 2024 has grown by around 5%. The performance of existing machine capacity in the full year 2024 is shown in Table I, product D recorded the highest sales volume in 2024 of 654,876,990 pcs as well as the highest utilization (80.67%), which is already close to the optimal utilization limit of 85%. Similar pattern is seen in product B with 80% utilization, both of which are expected to reach maximum capacity in next years if sales growth remains consistent. Meanwhile, product A and E despite having relatively low utilization (65% and 61%), show steady annual growth which will eventually require capacity expansion. The high utilization on several main production lines (especially product B and D) which almost touched 85% indicates greater operational pressure. This condition has the potential to cause operational risks.

Table I. Existing Machine Performance in 2024 Full Year

Product	Output machine (pcs)	Capacity (pcs)	Utilization
A	450,338,740	695,822,400	65%
B	517,227,630	649,434,240	80%
C	486,752,360	649,434,240	75%
D	654,876,990	811,792,800	81%
E	343,909,820	569,630,880	60%

$$\begin{array}{r}Utilization=\frac{OutputMachine}{Capacity}\times 100\mathrm{\%}\end{array}$$

Consideration of capacity expansion becomes even more crucial given not only the high utilization factor, but also the positive market growth momentum as reflected by the consistent increase in sales volume. The capacity expansion approach needs to consider uneven utilization patterns between products. By considering historical trends and available capacity, the company needs to immediately formulate a measurable capacity expansion plan to ensure operational sustainability and optimally utilize market growth opportunities.

DES may evaluate production loads and facility outputs based on master production schedules, allowing for better planning in contexts where product volume is not predetermined. Simulation models can greatly enhance capacity in manufacturing settings by allowing for the examination of alternative operating situations and their effects on throughput and resource utilization (Kanget al., 2013). The utilization of DES in manufacturing extends ordinary capacity planning, it involves a comprehensive strategy for system design and optimization. DES can be utilized to assess production performance, layout, and material flow in a digital industrial setting, thus optimizing operational efficiency (Centobelliet al., 2016).

One of the fundamental strategies in DES is event scheduling, where events are organized in a timeline. When an event occurs, it triggers subsequent events, and the simulation clock advances to the next scheduled event. This method is frequently used to improve performance and decision-making in industries like manufacturing, logistics, healthcare, telecommunications, and traffic systems (Bankset al., 2004). DES can be employed to improve productivity by simulating various scenarios and identifying bottlenecks in the production line by analyzing these simulations, manufacturers can make informed decisions about resource allocation, scheduling, and process improvements. DES allows for the evaluation of system performance metrics, such as throughput, utilization, and times. This capability is crucial for organizations aiming to enhance efficiency and reduce operational costs (Al Hazzaet al., 2018).

Materials

Forecast Sales Volume

The sales volume projection in Table II serves as a critical foundation for machine capacity expansion planning for several reasons. This forecast also indicates that capacity expansion cannot be implemented uniformly across all products but must be tailored to the growth patterns of each product, particularly for Products B and D, which exhibit the highest absolute growth rates.

Table II. Sales Volume Forecast for all Products 2025–2035

Year	Sales volume products (pcs)
	A	B	C	D	E	Total target per-year
2025	470,147,164	533,998,579	496,180,316	677,456,697	352,590,677	2,530,373,432
2026	491,657,890	553,932,705	513,054,350	704,941,677	366,161,248	2,629,747,869
2027	513,168,616	573,866,830	529,928,383	732,426,658	379,731,820	2,729,122,307
2028	534,679,342	593,800,956	546,802,417	759,911,638	393,302,391	2,828,496,744
2029	556,190,067	613,735,082	563,676,451	787,396,619	406,872,963	2,927,871,182
2030	577,700,793	633,669,208	580,550,485	814,881,599	420,443,534	3,027,245,619
2031	599,211,519	653,603,334	597,424,518	842,366,580	434,014,106	3,126,620,057
2032	620,722,245	673,537,460	614,298,552	869,851,560	447,584,677	3,225,994,494
2033	642,232,970	693,471,586	631,172,586	897,336,540	461,155,249	3,325,368,932
2034	663,743,696	713,405,712	648,046,620	924,821,521	474,725,820	3,424,743,369
2035	685,254,422	733,339,838	664,920,654	952,306,501	488,296,392	3,524,117,806

This growth pattern indicates market demand stability with an average annual increase of around 4%–5% for all products. Product D remains the product with the highest volume, projected to reach 952 million units in 2035, while Product E has the lowest volume with a projection of 488 million units in the same year. The consistency of this growth trend provides a strong foundation for long-term capacity planning, while also serving as an early warning that without additional capacity interventions, production facility utilization will increasingly approach or even exceed existing design capacity

In the context of discrete event simulation for production operations, this forecast data provides important input for several aspects. The basis for building a material flow model that will simulate how volume increases affect the throughput of the production system. Then identify potential bottlenecks in the system as capacity increases, particularly at critical points such as machines with high utilization or processes with long cycle times. Evaluate various production scheduling and resource allocation scenarios under different demand levels. Simulation will help answer critical questions such as when exactly additional capacity should be added and how to allocate machines for different products.

Aligning supply and demand (sales volume) is operations management’s main objective. Determining how much capacity or supply will be required to meet demand requires a demand prediction. For example, budgets must be set, purchasing requires information to place orders with suppliers, supply chain partners must plan, and operations must know how much capacity will be required to make decisions about staffing and equipment (Stevenson, 2021).

Scenario Strategy

The strategy scenario was proposed by the author through observation and experience of the company’s condition. The four proposed strategies were related to increasing production capacity by adding machines.

The scenario dimension diagram in Fig. 1 presents a strategic framework that maps out four scenarios for machine capacity expansion based on two critical dimensions: time horizon (medium term five years vs. long term 10 years) and implementation approach (gradual vs. simultaneous). This quadrant provides a comprehensive perspective for evaluating the optimal strategy options according to the company’s needs and capabilities.

Fig. 1. Scenario dimension.

Scenario 1 offers a more conservative alternative with gradual capacity expansion over a 5-year period. This approach reduces financial risk by spreading investments over several stages, while allowing the company to make adjustments based on actual market developments. This flexibility is particularly valuable in markets with high uncertainty or for companies with limited access to financing. The main challenge is the potential loss of market opportunities (opportunity cost) during the transition period and higher unit costs due to not achieving optimal economies of scale.

Scenario 2 represents simultaneous capacity expansion in the medium term (five years). This approach is suitable for companies facing rapidly growing market demand and having sufficient financial resources for large investments at once. Its main advantage lies in the ability to achieve economies of scale faster and avoid incremental costs that may arise in a phased approach. However, this strategy carries the risk of low-capacity utilization in the early stages if demand does not match projections, as well as significant liquidity pressure due to large capital outlays in a short period of time. For the 10-year long term, Scenario 3 (gradual) and Scenario 4 (simultaneous) offer different strategy variations with varying considerations. Scenario 3, which is gradual, provides room for technological adaptation and response to unpredictable market changes in the long term while Scenario 4, with large upfront investments at the beginning of the 10-year period, may only be suitable high demand predictability.

Methodology

This research applies Discrete Event Simulation (DES) to analyze the dynamic mixing powder production capacity planning strategy. DES modeling was chosen due to its ability to represent a production system as a series of discrete events (e.g., raw material arrival, mixing process, packaging) with time and resource dependencies. This method allows evaluation of system performance under various scenarios, such as demand fluctuations, machine additions, or predicting plant throughput.

Scheduling

Schedule optimization of manufacturing systems is critical to improve operational efficiency. DES may evaluate production loads and facility outputs based on master production schedules, allowing for better planning in contexts where product volume is not predetermined. Simulation models can greatly enhance capacity in manufacturing settings by allowing for the examination of alternative operating situations and their effects on throughput and resource utilization (Kanget al., 2013).

At this stage, the process of scheduling operational hours and holidays as well as the number of production batches to be executed each day. The operational day depends on the year that has been calculated. For example, product A has simulation in year of 2027, so operations will be scheduled in 2027 including working days, regular holidays, and public holidays. The date used as a reference is the date of the Indonesian state. Then, simulation model time range can be made. Find for run configuration input model time from January 1^st at 7 AM to December 31^st at 12 midnight. Find for simulation experiment and input model time January 1^st at 7 AM to December 31^st at 12 midnight.

Next, schedule is made for batch mixing in one day. The average mixing time is determined with a certain number of batches so that the target batch is achieved. The amount of mixing batch 1 batch/hour–3 batch/hour in 21 hour/day.

Parameter Input

Production has four lines with each respective products according to Table III. In this DES, each agent represents one production batch. Some of the parameters entered include:

Table III. DES Parameter Input in One Day Operational

	Time process parameter					Capacity parameter						Schedule parameter
Product	Weighing (minutes)	Mixing (minutes)			Packing (hour)	Silo	Weighing	Mixer	Sender hopper	Receiver hopper	Packing	Dumping
Avg	Min	Avg	Max	Avg	batch	batch	batch	batch	batch	batch
A	7	21	28	34	1	10	1	1	2	2	1–3	Operational from Monday till Saturday, Sunday is holiday and public holiday
B	5	16	19	22	1	10	1	1	2	2	1–3
C	5	22	25	30	1	10	1	1	2	2	1–3
D	6	23	24	26	1	10	1	1	2	2	1–3
E	6	23	24	26	1	10	1	1	2	2	1–3

1. Process time parameter, as an indication of how long a stage takes.

2. Capacity parameter, as an indication of how many batches each stage can accommodate.

3. Schedule parameter, as an indication of the appearance of the batch to be processed. Represents the schedule in reality where Sundays or public holidays are days off and there is no production process.

The three parameters above are taken from the production report database and applied in discrete event simulation to each production line and each product.

Production Process

The manufacturing process as reflect on DES block process is the stage of processing raw materials into finished goods in the form of powdered seasoning.

These are the process as shows Fig. 2, start from dumping process is the pouring of raw materials according to the batch formulation to be stirred. This dumping schedule is adjusted to the order set by the planning department. Silo is a place of storage after the dumping process and a place to wait for the next weighing process. The next process is weighing each material according to the products formulation. Weighing is done to ensure that each product to be produced has a consistent and uniform taste quality. Mixing process all the raw materials are mixed together, and the results are ready to be sent to the packing section. After the raw material is mixed homogeneously, WIP (work in progress) is formed and stored temporarily in a storage called the sender hopper. This place is used to wait for WIP to be transferred to the packing section. WIP is sent using a transfer system to a temporary place called a receiver hopper, which is where WIP is received before entering the packing machine. In this machine, WIP which was initially still in the form of bulky powder is packed into a pack or pcs.

Fig. 2. DES schematic.

Results and Discussions

In this discussion section, machine utilization rates will serve as a benchmark, as indicates machine productivity. Whenever utilization in a given year approaches the 85% threshold, the process of increasing capacity must be initiated immediately, with a one-year implementation timeline to cover administration, budgeting, and installation. The results of the capacity increase will then be reflected in the next year’s results.

Scenario 1

Scenario 1 is a strategy of gradually adding machines over a period of five years, this approach successfully maintains machine utilization at around the maximum threshold of 85% while meeting forecast demand with an average achievement rate of above 93%. This pattern of adding machines in response to increases in utilization demonstrates its effectiveness in production capacity requirements.

Product Machine Capacity Addition

In the initial years (2025–2026), before any machine additions, utilization rates on some production lines were already approaching critical thresholds 85%. Product B reached 85.78% and product D 85.96% in 2026, serving as the primary trigger for capacity expansion in the following year. A swift response by adding 1 machine unit to product B and one machine unit to product D in 2027 successfully reduced utilization to 72.48% and 74.48%, respectively, creating adequate capacity buffer for demand growth in subsequent years. The subsequent phased expansion on one unit machine to product C in 2029 demonstrates a precise approach in anticipating utilization increases before reaching critical levels. The total of machine addition is three units machines.

The utilization patterns revealed by this simulation highlight several key operational insights. Variability in achievement rates across years indicates the presence of stochastic factors in operations, necessitating consideration of safety capacity in planning. Meanwhile, differences in machine addition patterns across production lines (only occurring in product B, C, and D) demonstrate that a customized approach per product is more effective than uniform capacity expansion. Specifically for product E, which consistently shows relatively low utilization (average 65%), the simulation results provide opportunities for resource optimization through resource sharing strategies or production line consolidation to enhance capital efficiency.

Powder Mixing Plant Capacity Addition (All Product)

The result on plant view means the total capacity of all product, the data in Table IV illustrate the effectiveness of the gradual machine capacity expansion strategy over a five-year period (2025–2030). A consistent growth pattern is evident in both the forecast line and the simulation results, with overall utilization maintained within the optimal range of 73%–78%. In early 2025, the total capacity of 3.38 billion units was able to meet the forecast demand of 2.53 billion units with a utilization rate of 74.6%, indicating an adequate capacity buffer. As demand grows to 3.03 billion units by 2030, the phased machine addition strategy successfully increases total capacity to 3.86 billion units, keeping system utilization at 77.38%.

Table IV. DES Scenario 1 Plant Capacity Result

Year	Capacity (pcs)	DES output result (pcs)	Utilization
2025	3,376,114,560	2,518,605,902	74.60%
2026	3,376,114,560	2,615,799,848	77.48%
2027	3,700,831,680	2,714,468,340	73.35%
2028	3,700,831,680	2,784,724,616	75.25%
2029	3,863,190,240	2,916,727,470	75.50%
2030	3,863,190,240	2,989,200,382	77.38%

Fig. 3 shows that gradually adding capacity follows the result of simulation, means the utilization can be optimized.

Fig. 3. Scenario 1 in year 2025–2030.

Scenario 2

Scenario 2 is adding capacity simultaneously with time horizon mid-term (five years) shows interesting operational dynamics when compared to the phased approach. In Scenario 1 the addition of capacity till five years period gets three units machines and it had been added gradually, in Scenario 2 the 3 units machines will add simultaneously in one time.

Product Machine Capacity Addition

In the first year of implementation (2025), the initial conditions prior to the addition of machines showed relatively high utilization in several production lines, particularly product B with 81.27% utilization and product D reaching 82.86%. This condition serves as the rational basis for the large-scale capacity expansion carried out in the following year.

The simultaneous implementation of three machine additions in 2026 has a significant direct impact on the system’s capacity profile. There is an increase in total capacity from 3.38 billion units in 2025 to 3.86 billion units in 2026, a jump of 14.4% in one year. This surge in capacity successfully reduced the utilization of product B machine to 72.48% and product D to 73.95% in 2026, creating a large buffer capacity to absorb demand growth in the following years. However, this drastic decline in utilization also indicates a period of adaptation where the new capacity has not been fully utilized optimally. This pattern confirms that the significant capacity expansion at the beginning of the period provides sufficient growth room for the next five years, despite some notable underutilization in the early years.

In-depth analysis reveals several challenges in this strategy. There is a period of underutilization in the early years following expansion, particularly evident on product C machine, where utilization dropped to 63.42% in 2027 after machine additions. The concentrated capital investment burden at the beginning of the period could lead to significant liquidity pressure. This strategy is slightly riskier in dealing with the challenges of real sales volume fluctuations.

Powder Mixing Plant Capacity Addition (All Product)

The results of Simulation Scenario 2 in Table V, which applies simultaneous machine capacity expansion at the beginning of the period. The significant capacity expansion implemented at the beginning of 2027 (as evidenced by the surge in capacity to 3.86 billion units) creates a significant change in the system utilization profile. There is a fairly dramatic decline in utilization from 77.48% in 2026 to 69.97% in 2027, indicating an adaptation period where the new capacity has not yet been fully optimized. This underutilization phenomenon is a characteristic of simultaneous expansion strategies, where available capacity far exceeds short-term needs to accommodate medium-term growth.

Table V. DES Scenario 2 Plant Capacity Result

Year	Capacity (pcs)	DES output result (pcs)	Utilization
2025	3,376,114,560	2,518,605,902	74.60%
2026	3,376,114,560	2,615,799,848	77.48%
2027	3,863,190,240	2,703,077,658	69.97%
2028	3,863,190,240	2,776,803,700	75.25%
2029	3,863,190,240	2,916,727,470	71.88%
2030	3,863,190,240	2,989,200,382	77.38%

Fig. 4 shows that simultaneously adding capacity and the capacity have a larger gap which indicate the machine have low utilization.

Fig. 4. Scenario 2 in year 2025–2030.

Scenario 3

Scenario 3 is adding gradual capacity expansion strategy over a 10-year period 2025–2035. Every DES result for product have reached utilization levels above 85%, serving as a strong indicator of the need for capacity expansion adding machines.

Product Machine Capacity Addition

During the initial period (2025–2026), machine utilization rates indicate that certain production lines, such as product B 85.78% and product D 85.96%, have reached utilization levels above 85%, In response to these utilization signals, additional machines were added 1 unit machine to product B and one unit machine to product D in 2026, effectively reducing utilization to 72.48% and 74.48% in 2027, respectively, creating sufficient room for production growth in subsequent years. Meanwhile, product C will experience utilization of 84.80% in 2028, requiring the addition of 1 unit of product C machine. Utilization then drops to 67.49% in 2029.

The main challenge identified from the simulation data is significant variability in utilization across production lines. For Product A in 2031, machine utilization is 84.72%, requiring the addition of one machine for Product A. For Product D in the same year, machine utilization is 85.03%, necessitating the addition of one machine for Product D. For Product D over a 10-year period, an increase in machine capacity of two units is required, with 1 unit added in 2026 and another in 2031. As a result, by 2032, machine utilization for Product A drops to 77% and for Product D to 76.57%. In 2033, while product B machine achieved 84.77% utilization, product C was only at 75.06%. Machine of product B need the addition one unit machine, then 2034 utilization of product B decrease at 72.28%. This variation highlights the complexity of managing multiple production lines with differing demand characteristics. However, the inherent flexibility of the phased strategy allows for more precise adjustments to each production line based on its specific needs. The total of machine addition is 6 units machines.

The machine addition pattern observed in this simulation follows a highly systematic logic, where expansion is only undertaken when utilization approaches or exceeds the optimal threshold of 85%. This approach results in a stable utilization profile, ranging between 65%–85% for most production lines throughout the 10-year period. What is particularly noteworthy is how this strategy successfully balances demand fulfillment (with an average achievement rate of 94.3%) and machine utilization efficiency, without significant overcapacity. In years when additional machines were added, such as 2027, 2029, 2032, 2034 there was a natural decline in utilization as a direct result of capacity expansion, followed by a stabilization period where utilization gradually increased in line with demand growth.

The total capacity growth from 3.38 billion units in 2025 to 4.30 billion units in 2035 demonstrates controlled yet consistent growth, with an average annual capacity increase of approximately 92 million units. This growth pattern is much flatter than the simultaneous expansion scenario, reflecting the “little but often” philosophy in capacity management. In terms of demand fulfillment, the production simulation results consistently reach 91%–97% of the annual forecast, indicating that this phased approach is capable of meeting market needs with high reliability.

Powder Mixing Plant Capacity Addition (All Product)

The results of Scenario 3 simulation in Table VI, which applies a gradual addition of machines over a 10-year period, show a highly measured approach to managing production capacity. Aggregate data for all products show interesting developments, starting from an initial utilization rate of 74.6% in 2025 with a capacity of 3.38 billion units, reaching a utilization rate of 80.89% in 2035 with a capacity of 4.30 billion units. A graph displaying the two main components (capacity and simulation results) shows nearly parallel lines with a harmonious growth pattern, indicating good synchronization between production planning and execution.

Table VI. DES Scenario 3 Plant Capacity Result

Year	Capacity design (pcs)	DES output result (pcs)	Utilization
2025	3,376,114,560	2,518,605,902	74.60%
2026	3,376,114,560	2,615,799,848	77.48%
2027	3,700,831,680	2,714,468,340	73.35%
2028	3,700,831,680	2,784,724,616	75.25%
2029	3,863,190,240	2,916,727,470	75.50%
2030	3,863,190,240	2,989,200,382	77.38%
2031	3,863,190,240	3,074,193,626	79.58%
2032	4,141,519,200	3,224,350,594	77.85%
2033	4,141,519,200	3,288,525,060	79.40%
2034	4,303,877,760	3,415,134,372	79.35%
2035	4,303,877,760	3,481,327,802	80.89%

Fig. 5 shows that gradually adding capacity follows the result of simulation, means the utilization can be optimized.

Fig. 5. Scenario 3 in year 2025–2035.

Scenario 4

Scenario 4 is adding capacity simultaneously with time horizon long term (10 years). In Scenario 3 the addition of capacity till 10 years period gets six units machines and it had been added gradually, in Scenario 4 the six units machines will add simultaneously in one time.

Product Machine Capacity Addition

According to the installation of machines, the first year of implementation (2025) revealed comparatively high usage in a number of production lines, especially product B (81.27% utilization) and product D (82.86% utilization).

The capacity profile of the system is directly and significantly impacted by the simultaneous installation of three machine expansions in 2026. The overall capacity increased by 27% in a single year, from 3.38 billion units in 2026 to 4.30 billion units in 2027. By successfully lowering the usage of the product B machine to 58.88% and the product D machine to 63.15% in 2026, while product A have drop utilization from 72.24% to 63.41%. The product C also drop utilization from 76.76% to 63.15%. This capacity increase created a sizable buffer capacity to handle future increases in demand. This sharp drop in usage, however, also points to an adaptation phase during which the extra capacity has not been used to its fullest potential. Despite some apparent underutilization in the early years, this pattern demonstrates that the substantial capacity expansion at the start of the era gives enough growth room for the following ten years.

An in-depth analysis identifies a number of issues with this approach. Early on after expansion, there is a period of underutilization. This is especially noticeable with the product A, B, C, D machine, whose utilization fell drastically in 2027 following machine additions. There may be severe liquidity pressure as a result of the period’s initial concentrated capital investment load. When it comes to handling the difficulties of actual sales volume variations, this approach is a high risk.

Powder Mixing Plant Capacity Addition (All Product)

In the early years (2025–2026), production capacity remains relatively low, with simulation results nearly touching the forecast line, indicating efficient utilization in the range of 74%–77% as shown in Table VII. Following the addition of new machines in 2026, there is a significant increase in capacity in 2027, creating a large “buffer” between the capacity line and the forecast. This leads to a temporary decline in utilization in 2027 (62.45%) as the new machines are not yet fully utilized.

Table VII. DES Scenario 4 Plant Capacity Result

Year	Capacity (pcs)	DES output result (pcs)	Utilization
2025	3,376,114,560	2,518,605,902	74.60%
2026	3,376,114,560	2,615,799,848	77.48%
2027	4,303,877,760	2,687,960,246	62.45%
2028	4,303,877,760	2,803,690,574	65.14%
2029	4,303,877,760	2,889,256,658	67.13%
2030	4,303,877,760	3,053,740,838	70.95%
2031	4,303,877,760	3,183,062,216	73.96%
2032	4,303,877,760	3,224,268,898	74.92%
2033	4,303,877,760	3,263,460,790	75.83%
2034	4,303,877,760	3,415,382,816	79.36%
2035	4,303,877,760	3,481,327,802	80.89%

From an operational perspective as shown Fig. 6, the initial underutilization period (2027–2029 with utilization of 62%–67%) offers several distinct advantages. This excess capacity allows flexibility in handling unexpected demand, performing machine maintenance without disrupting operations, and providing room for testing and implementing new production processes. However, on the other hand, this also has the potential to cause cost inefficiencies at the beginning of the period due to depreciation of machines that have not been fully utilized. The graph shows that simultaneously adding capacity and the capacity have a larger gap which indicate the machine have low utilization.

Fig. 6. Scenario 4 in year 2025–2035.

Comparing between Gradual and Simultaneous Adding Capacity

Based on the utilization data presented in Fig. 7, Scenario 1 (5-year gradual adding capacity) emerges as the most optimal option that balances risk minimization and efficient utilization. This scenario’s utilization pattern shows the best stability, ranging between 73%–78% throughout the 5-year period, without experiencing drastic fluctuations or significant periods of underutilization. This stability is achieved through an incremental machine addition mechanism when utilization approaches 85%, thereby maintaining a sufficient 7%–12% capacity buffer to anticipate demand fluctuations without causing excessive idle capacity.

Fig. 7. Comparing utilization result between gradual and simultaneous adding capacity.

In terms of minimizing operational risk, Scenario 1 excels in three critical aspects:

• Financial risk is better controlled due to phased capital investment aligned with demand realization.

• Demand uncertainty risk is minimized through the ability to adjust expansion based on actual utilization rather than long-term projections.

• Operational risk such as production disruptions caused by the simultaneous installation of a large number of machines can be avoided.

Scenario 3 (10-year phased gradual adding capacity) actually shows a similar utilization pattern (74%–81%), but the longer timeframe increases the risk of technological change where machines added in the later years may become less competitive. Scenario 1 with a 5-year horizon is more adaptive to technological changes and market preferences. Strategic Implications like companies with limited access to capital will benefit most from Scenario 1 because cash outflows are more distributed. For industries with short technology cycles (<5 years), Scenario 1 allows for more flexible machine upgrades than a 10-year capacity commitment. In an uncertain business environment, Scenario 1’s ability to adjust expansion based on actual utilization (rather than projections) acts as a natural hedge against market volatility.

Compared to Scenario 2 (simultaneous five years), which saw utilization drop to 69.97% in 2027, or Scenario 4 (simultaneous ten years) with utilization of only 62.45% in the same year, Scenario 1 successfully avoided the risk of acute underutilization that could burden finances through the depreciation of unproductive machinery. On the other hand, the final utilization rate of 77.38% in 2030 under Scenario 1 is only 3.5% different from Scenario 4 (80.89% in 2035), indicating that the phased approach remains capable of achieving long-term efficiency without initial capacity shocks.

The simultaneous scenario creates excess capacity at the beginning of the period, providing flexibility to handle unexpected demand, but with the cost of depreciation on underutilized machinery. In the long term (2025–2035), both approaches ultimately achieve relatively similar utilization rates (around 80%), but through very different paths the phased scenario reaches equilibrium gradually, while the simultaneous scenario must go through a prolonged period of underutilization before finally achieving optimal utilization. The utilization gap phenomenon mentioned the difference between available capacity and actual output does indeed indicate different levels of uncertainty risk. Scenarios with large gaps (especially the simultaneous scenario in the early years) face higher risks of utilization fluctuations in the event of changes in sales volume. However, these large gaps can also serve as valuable buffers when facing faster-than-expected demand growth. Conversely, the phased scenario with small utilization gaps is more protected from underutilization risks but has limited capacity to respond to exponential demand growth.

Conclusion

Capacity Machine Addition

Based on the DES simulation, the number of machines and the year in which the addition of machines must begin were obtained (Table VIII).

Table VIII. Summary Capacity Machine Addition

Scenario	Start year for addition machine	Quantity of machine	Product need to add machine
1	2026	2	B & D
	2028	1	C
2	2026	3	B, C, D
3	2026	2	B & D
	2028	1	C
	2031	2	A & D
	2033	1	B
4	2026	6	A, B, C, D

Recommendation Scenario

The research results indicate that the most effective scenario is Scenario 1 strategy of gradually adding machines over a period of five years, which involves adding two machines in 2026, increasing capacity by 325 million pcs/year and adding one machine in 2028 increases capacity by 162 million pcs/year. This scenario’s utilization pattern shows the best stability, ranging between 73%–78% throughout the 5-year period, without experiencing drastic fluctuations or significant periods of underutilization. This scenario can minimize operational risk, sales volume uncertainty and more controlled financial risk.

Conflict of Interest

Conflict of Interest: The authors declare that they do not have any conflict of interest.