1 Introduction

Productivity is a crucial measure of efficiency in manufacturing processes, representing the ratio of aggregate output to input. Various definitions and approaches exist, depending on the measurement goal and data availability [1]. Simultaneously, the design and analysis of manufacturing lines have been extensively studied, especially considering the inherent variability and processing durations at different stations [2]. Simulation plays a vital role in modeling and understanding complex systems. A model is a representation of a real system, allowing analysts to predict the impact of changes. An effective model strikes a balance between realism and simplicity, capturing essential attributes without becoming overly complex [3]. The validity of a model is ensured through validation, which involves comparing model output to the actual system's output under known input conditions. Simulation-based studies have focused on modeling and optimizing production systems. For example, researchers simulated factory layouts to assess machine utilization and efficiency [4]. Workflow studies and simulation models identified bottlenecks and proposed improvements for UPS production systems [5]. Simulation also serves as a decision support tool for evaluating different scenarios and their outcomes to determine optimal solutions [6]. Several research papers have demonstrated the effectiveness of simulation in manufacturing line optimization. In one study, simulation was utilized to evaluate different scenarios and determine the optimal system that met target demands without modifying the production line layout [6]. Another study focused on implementing agility concepts within manufacturing facilities, including layout selection and efficient scheduling techniques [7]. The Flexsim simulation software aided in determining the optimal combination of layout and scheduling. The aim of this research is to study Atrakta ampoules Production line in an attempt to increase productivity. Improvements to the production line are recommended and simulated. Simulation of the production line after incorporating the recommended improvements shows that the suggested improvements increase the production line productivity.

Macro Group Pharmaceuticals is a pioneer and market leader in Egypt's cosmeceutical space, having been present for nearly two decades. By catering to multiple demographic segments with a well-developed portfolio of products diversified across seven high-growth therapeutic areas within cosmeceuticals and 1 newly added therapeutic area within nutraceuticals, the company has steadily grown in size to lead the highly fragmented market in terms of sales [8]. Egypt's vast and constantly growing population, rising income, greater urbanization, expanded internet and smartphone penetration, and rapidly rising private consumption levels have all spurred local demand for premium, innovative cosmeceutical products. Macro Group leads Egypt's dynamic cosmeceutical sector, commanding a market share of 31% in terms of sales in FY22 based on the therapeutic areas in which it operates. This is a testament to the company's unique and innovative consumer-centric business model, which has enabled the company to leverage its in-house market intelligence and innovation initiatives to continuously launch new products at attractive price points for customers in various demographic segments of the Egyptian market. Macro Group has a highly appealing portfolio of market-leading products in skin care, hair care, female care, anti-scar, oral care, antiseptics, and topical muscle relaxants, as well as nutraceuticals, all of which have experienced significant growth in Egypt in recent years. As of the end of 2022, the company has the greatest market share in Egypt among cosmeceutical companies in the therapeutic areas of skin care, hair care, oral care, female intimate care, and cosmeceutical antiseptics, and the second-largest market share in the anti-scar therapeutic area. The majority of Macro Group's products are produced in-house at its modern manufacturing facility in Badr City. The facility, which is built in accordance with Good Manufacturing Practices ("GMP") and Good Laboratory Practices ("GLP"), comprises ten cutting-edge manufacturing lines as well as supporting packaging machinery. Each production line follows precisely established internal criteria that are carried out and overseen by Macro Group's trained personnel [8].

Atrakta hair ampoules are one of the Macro premium products. Atrakta hair ampoules have nine packaging components as follows: 12 ampoules, 12 ampoule stoppers, 12 aluminum Cap, 12 batches no. and manufacturing and expiration date labels, four droppers, two ampoule plastic holders, two safety labels, three sponge and packing box. The layout of the ampoules production line contains two semi-manual machines, a transportation line, and a table for manual packaging. The productivity of this line is 720 finished products per day of Atrakta hair ampoules. On the other hand, the production line needs 15 laborers to operate filling process, ampoules sealing off process that have two sub process as allocating stopper in ampoules and allocating aluminum caps. In addition to transfer ampoules to packing table, ampoules packing process that have six sub process as label disassembly, label assembly, allocating ampoules in plastic holder, allocating dropper in plastic holder, packing box assembly and allocating plastic holder in packing box. The following table shows the required labor for each sub process,

The forecast of Atrakta hair ampoules in 2023 is 60,375 finished products (12 ampoules/product) and according to the current productivity 84 days are needed to produce this quantity. Moreover, using linear regression method using excel with formula \(Y=a+bX\) where, Y is dependent variable (Forecast), a is Y-intercept of the line, b is slope of the line, X is independent variable (period/year), \(Y=5580.5+11158X\), R2 = 0.8049. The forecast may reach 89,965 finished products (12 ampoules/product) in 2027 which means 125 days needed to produce the market needs. On the other hand, the required working days to manufacture the forecasted units for the three ampoules products are 188 days in 2023 and 365 days in 2027.

The numbers in the forecast show that the production line needs to work every day by the year 2027 to meet market needs. This called the company to revisit the production line operations techniques. By observing the processes in the production line, it has been noticed that some non-expensive improvements could enhance the production line operation to increase productivity, leading to a significant reduction in the number of working days needed to meet the forecasted market demand. Accordingly, this research suggests such improvements. To examine the impact of these improvements on the rate of production, simulation is utilized. Simulation has been utilized in several similar situations in order to improve productivity, as in the work of [4:7]. The production line under study is simulated with the suggested improvements and the new higher production rate is recorded, leading to a decision of implementation in the actual production line.

The rest of this paper is organized as follows: Sect. 2 explains the methodology adopted in the current research, Sect. 3 highlights results and discussion in light of the simulation and statistical analysis, and finally, Sect. 4 concludes the paper (Table 1).

Table 1 Atrakta production labor force for each process
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2 Methodology

2.1 Data collecting and analysis

This paper collected 21 records for each process for each laborer through live recording on the production line according to direct time study (DTS). The internal and external work elements were determined, and the personal fatigue and delay (PFD) allowance is included in all process data values. On the other hand, using Arena software 16.10 student version, the collected data were analyzed to determine the probability distribution for each probabilistic input [9:12].

3 Questionnaire:

A questionnaire was created using the stated categories for difficulty level of insertion/allocation and a subcategories list for difficulty level in terms of time consumption. The stakeholders in the factory were asked whether the questionnaire was legitimate and whether to make any necessary changes to the categories, credits, and structure. Table 2 depicts a paired scale that represents the score of difficulty in terms of time consumption in each process and spans from 1 to 5. The results of the questionary will clarify the difficulty level of primary pack items insertion through a comparison between questionnaire results and simulation results.

Table 2 Difficulty level of insertion/allocation of primary pack components
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4 Production line simulation and analysis

This research worked on 2 scenarios to simulate the current layout and production process of Atrakta hair ampoules to determine each process time, where are the bottleneck points on the production line, and the level of the bottleneck. Also, the same 2 scenarios were used for the production line productivity after the recommended optimization to compare the results of before and after optimization to measure the percentage of change and efficiency. Each scenario has 8000 runs to achieve a high percentage of results accuracy. Scenario one this work assumed after analyzing and determining the probabilistic and controllable inputs that all probabilistic inputs have followed uniform distributions. Using Excel to generate random numbers for each Probabilistic input from 0 to 1 through the rand function to calculate each process time using the uniform distribution equation “min*r(max–min)” [9:11]. The arrival time of each process is equal to the compellation time of the previous process. Scenario two of this work followed the probabilistic distribution results of analyzing process data to simulate the layout and production process. Using Excel to generate random numbers for each Probabilistic input from 0 to 1 through the rand function to calculate each process time according to its probabilistic distribution function. The arrival time of each process is equal to the compellation time of the previous process [11:16][21].

5 Results validation and verification

The t-test was created using Microsoft Excel Add-in data analysis functions for the 1100 observations after and before the process improvement to compare the two scenarios performance and check improvements signification in the two scenarios.

6 Results and discussions

6.1 Data analysis before improvements

The result of Arena software analysis shows in Fig. 1a, b, c, d and e a five-process having beta distribution with six interval of process time for each process.

Fig. 1
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a, b, c, d and e Beta distribution

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On the other hand, in Chi-square test filling process has three intervals, the label assembly process has five intervals, allocating droppers in plastic holders process has five number of intervals, allocating plastic holder in packaging box process has five number of intervals, packaging box assembly process has six number of intervals.

Moreover, Fig. 2a, b shows a two-process having Weibull distribution with six process time intervals for both. In addition, the Chi-square test for the Stopper allocating process has 4 intervals and for allocating ampoules in plastic holders process has 4 number of intervals.

Fig. 2
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a and b Weibull distribution

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Figure 3 shows that the allocating aluminum cap process has gamma distribution with six intervals of process time and four intervals as results of Chi-square test. On the other hand, Fig. 4 shows that the labeling disassembly process has lognormal distribution with 6 intervals of process time and three intervals as results of Chi-square test.

Fig. 3
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Labeling disassembly process time lognormal distribution

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Fig. 4
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Allocating aluminum cap process time gamma distribution

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The result of Arena software analysis shows in Table 3 the minimum and maximum value of each process in seconds with the mean and standard deviation. Also, the probability distribution of each process. five of the processes follows Beta distribution with 1.7 S as minimum process time and 14.93 S as high process time, two processes follow Weibull distribution with 1.25 S as minimum process time and 5.98 S as high process time, one of process follows Gama distribution with 1.99 S as minimum process time and 7.55 S as high process time and one of process follows Lognormal distribution with 3.45 S as minimum process time and 23.1 S as high process time.

Table 3 Atrakta hair ampoules production process time
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7 Questionnaire

Table 4 shows the difficulty of each process with time consumption according to the scale in Table 1, the labeling disassembly process has the highest score that presents a high value of time consumption, and a high level of focus and skill needed to disassemble four labels at one time. Also, the labeling process has the second-highest score, it's back to a required specific place for labeling on the ampoule. In addition, packaging box assembly time has a four on a scale score, it’s back to the difficulty of assembly product box chamfer that required a high level of focus and specific skill to assemble correctly without deforming the packaging box. Moreover, allocating plastic holders in the packaging box process has a less high score on a scale, it’s back to the difficulty of controlling it with ampoules because of the elasticity of plastic.

Table 4 Atrakta hair ampoules production process time consumption levels
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8 Production line simulation and analysis before improvements

In Tables 5 and 6 the simulation of 8640 runs shows that several suboptimal processes such as the labeling disassembly and assembly process because the number of ampoules outed after allocating the aluminum cap process per minute is 24 ampoules and the time required to label these ampoules is 2.3 min.

Table 5 Atrakta hair ampoules production line simulation results before improvements (Scenario 1)
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Table 6 Atrakta hair ampoules production line simulation results before improvements (Scenario 2)
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Moreover, shows a medium level of bottleneck in allocating plastic holders in packaging boxes. In addition, a low level of bottleneck in the packaging box assembly process. On the other hand, these results show compatibility with questionnaire results.

Table 7 shows the analysis of the simulation in terms of the number of waiting ampoules and their probabilities. Moreover, the average waiting time, maximum waiting time, and maximum number in the queue.

Table 7 Atrakta hair ampoules production line simulation analysis before improvements
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Table 8 shows the bottlenecks process in terms of the highest value of total waiting time for each process in minutes. The aluminum cap allocating process has the highest waiting time at 90.45 min and the plastic cap allocating process has a waiting time of 33.04 min while the remaining process has zero waiting time.

Table 8 Atrakta hair ampoules production line simulation analysis before improvements (bottlenecks process)
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9 processes analysis and packaging design

According to the simulation and Questionnaire results, this study found several suboptimal processes as follows: disassembly and assembly batch no. and manufacturing and expiration dates labels and Assembly product packaging box. Also, allocating plastic holders in a product packing box. On the other hand, the current process has a high amount of waste. Furthermore, packaging personnel on the Atrakta production line perform poorly due to the high level of concentration and divided attention required to conclude from packing one finished product through the following six sequences. So, this study re-designed the packing box of the Atrakta hair ampoule by removing the chamfer from the current packaging box, replacing the stopper and aluminum cap with an atomizer, replacing the plastic holders with a sponge, removing the safety label, replacing the labeling manual process with an ink-jet printer. To increase line productivity and improve ergonomics layout [18:20].

10 Data analysis after improvements

This research collected the processing time for the Atrakta hair ampoules production line after improvements with the same criteria of collecting data before improvements as shown in Table 9. Furthermore, simulated the layout and production process after improvements using the same scenarios. Figure 5a, b, c shows the histogram of process that have beta distribution, all histograms have 6 intervals of process time.

Table 9 Atrakta hair ampoules production process time after improvements
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Fig. 5
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a, b and c Beta distribution

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Figure 6a, b shows the histogram of process that have Weibull distribution, all histograms have 6 intervals of process time.

Fig. 6
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a and b Weibull distribution

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11 Production line simulation after improvements

Tables 10 and 11 shows a decrease in lead time per box from 4 min to 1.32 min which means increasing in daily capacity from 720 finished products per day to 2178 finished products per day.

Table 10 Atrakta hair ampoules production line simulation results after improvements (Scenario 1)
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Table 11 Atrakta hair ampoules production line simulation results after improvements (Scenario 2)
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Moreover, shows a decrease in the number of processes needed to produce Atrakta hair ampoules with simple packaging items without any changes in packaging function ability.

12 Results validation and verification:

The t-test was performed using Microsoft Excel Add-in data analysis functions for the 1100 observations after and before the process improvement. The parameters used in the t-test were as follows:

  1. 1.

    The reference level of confidence is 95%, i.e., p-value = 0.05

  2. 2.

    The two data sets are assumed to be unpaired, i.e., data sets are not relevant to each other.

  3. 3.

    The variance and standard deviation of each data set are different.

As shown in Tables 12, 13, 14 and 15, the p-value for both one tail and two tails are approximately (0), i.e., less than 0.05, which means a significant difference between the two data sets and the mean of both sets are significantly different as well.

Table 12 T-test of ampoules sealing off process scenario 1
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Table 13 T-Test of Ampoules Sealing off Process Scenario 2
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Table 14 T-Test of Ampoules Packing off Process Scenario 1
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Table 15 T-Test of Ampoules Packing off Process Scenario 2
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13 Conclusions

The focus of this study was on Macro's three hair ampoule products: Atrakta, Verdex, and Solodex. Looking ahead to 2023, the forecast indicates an expected production of 134,655 FG units, which may increase to 261,000 FG units by 2027. To meet these targets, the manufacturing process must be optimized for efficiency and responsiveness to market demands. Through interviews, production line observation, data collection, and modeling and simulation of the production line, this research aimed to uncover opportunities for enhancing the productivity of the Atrakta hair ampoules production line while ensuring it meets market requirements. The study proposed two scenarios with recommended improvements for the production lines, differing mainly in the probability distribution of probabilistic inputs. The simulation results demonstrated that both scenarios led to a substantial increase in productivity, with daily production rising from 720 FG units per day to 2176 FG units per day, representing a 300% improvement. Based on the positive outcomes observed in the Atrakta production line, Macro Group has made the strategic decision to implement the same recommended improvements in the production process of Verdex and Solodex ampoules. This move is expected to result in a production capacity of 2176 FG units per day for these products as well. As a result of these optimizations, the required working days to reach the forecasted production units in 2023 have decreased significantly from 188 to 67 days, while the timeline for 2027 has decreased from 365 to 132 days.