# OPTIMIZATION OF OVERALL EQUIPMENT EFFECTIVENESS FACTORS IN A FOOD MANUFACTURING SMALL AND MEDIUM ENTERPRISE

¹Igbokwe Nkemakonam Chidiebube, ¹Charles Onyeka Nwamekwe, ¹Godwin Harold Chukwuemeka and ²Mba Wilfred

¹Department of Industrial and Production Engineering, Nnamdi Azikiwe University, Awka, Nigeria.

²Department of Mechanical and Automobile Technology Education Federal College of Education (Technical), Umunze, Nigeria.

Email: co.nwamekwe@unizik.edu.ng

## Abstract

This study investigates the optimization of Overall Equipment Effectiveness (OEE) in a small and medium food manufacturing enterprise, specifically Tummy-Tummy Foods Industries Limited. The primary objective is to enhance production efficiency and profitability by improving key OEE factors: availability, performance, and quality. Data on equipment failure over 36 months were analyzed using Response Surface Methodology (RSM) and Central Composite Design (CCD) to develop an optimization model. Descriptive statistics revealed mean values of 70.98% for availability, 77.42% for performance, and 89.16% for quality, resulting in a mean OEE of 49.36%. Pearson correlation analysis indicated a strong positive correlation between the OEE and both the availability (0.928) and the performance (0.878), while the correlation with quality was weakly negative (-0.257). This highlights the critical role of availability and performance in driving OEE improvements. The Response Surface Methodology (RSM) analysis provided a model with a high degree of fit (R-sq. = 99.93%), demonstrating the significant impact of availability, performance, and quality on OEE. Contour and surface plots further illustrated the relationships between these factors, emphasizing that increases in availability lead to substantial improvements in OEE, with performance also playing a crucial role. However, quality, while important, showed a less significant impact. The study concludes that focusing on availability, alongside improving performance and quality, can significantly enhance OEE, thereby boosting overall manufacturing efficiency and profitability in the food production sector.

Key Words - Response Surface Methodology (RSM); Overall Equipment Effectiveness (OEE); Small and Medium Food Manufacturing Enterprise; Optimization

## 1. Introduction

Industries and companies are under greater pressure than ever to operate profitably and efficiently due to globalization [1]. This trend encourages businesses to focus more on their core activities, outsourcing less profitable departments and services to cut costs [2]. Competition in manufacturing is rising rapidly as customer expectations grow and demand becomes increasingly unpredictable. As a result, the development of industrial strategies, such as maintenance and production, has become essential for manufacturing firms to reduce costs.

For food manufacturers, the primary goal is to enhance efficiency and profitability by minimizing total

manufacturing costs through optimizing operations and maintenance activities, which is achieved by continuously improving machine reliability and implementing effective maintenance strategies [3]. Several studies have explored the economic impact of maintenance in food manufacturing industries, demonstrating how effective maintenance policies positively influence the productivity and profitability of manufacturing processes [2],[1],[3]

In this sector, asset maintenance is crucial for efficient manufacturing, as it faces ongoing challenges that make asset upkeep critical. Due to the nature of manufacturing, companies must frequently update or modify these assets to maintain efficiency and improve production. Consequently, food manufacturers are investing more in manufacturing assets than any other sector [4]. The deterioration of
equipment in manufacturing operations is nearly unavoidable due to age, use, and other factors. Without proper maintenance, equipment failure or significant malfunctions can occur. Operating with deteriorating machinery typically leads to higher production costs and reduced product quality [5]. The growing availability of data is transforming decision-making processes in areas such as scheduling, maintenance management, and quality improvement [2],[6],[3],[7],[8]. This study aims to enhance profitability by improving Overall Equipment Effectiveness (OEE) in a small and medium enterprise within the food manufacturing industry.

## 2. Literature Review

In assessing manufacturing performance capability, overall equipment effectiveness (OEE) is a key performance indicator. It is determined by the availability rate, performance rate, and quality rate in a manufacturing system and is expressed as:

$$ \text{OEE} = \text{Availability} \times \text{Performance Rate} \times \text{Quality Rate} $$

Availability reflects losses due to equipment failure, setup, and adjustments, and is calculated as the ratio of operating time to loading time, as shown below:

$$ \text{Availability} = \frac{\text{Planned runtime} - \text{Planned downtime}}{\text{Planned runtime}} \times 100 \quad (2.2) $$

The performance rate accounts for losses from idle time and minor stoppages, which is calculated as the ratio of net operating time to operating time:

Performance rate

$$ = \frac{\text{Total Actual amount of product}}{\text{Targeted amount of product}} \times 100 \quad (2.3) $$

The quality rate considers defects in the process and reduced yield, and is defined as the ratio of valuable operating time to net operating time:

$$ \text{Quality rate} = \frac{\text{Processed quantity} - \text{defective quantity}}{\text{Processed quantity}} \times 100 \quad (2.4) $$

Generally, an OEE of 85% is regarded as a world-class standard and serves as a benchmark for manufacturing firms, as illustrated in table 1.

<table><caption>Table 1: World class goals for OEE</caption><thead><tr><th>OEE Factor</th><th>WORLD CLASS RATE (%)</th></tr></thead><tbody><tr><td>Availability</td><td>>90.0%</td></tr><tr><td>Performance Rate (2.1)</td><td>>95%</td></tr><tr><td>Quality Rate</td><td>>99%</td></tr><tr><td>OEE</td><td>85%</td></tr></tbody></table>

Several studies suggested that TPM implementation positively influenced the OEE factors, leading to enhanced equipment maintenance and operational efficiency [9],[10]. However, conducting a comparative analysis with other industries or companies implementing TPM could provide a broader perspective on the effectiveness of TPM strategies in improving OEE and equipment maintenance practices [11], this is the basis of this study.

## 3. Materials and Method
The study was conducted at a food and beverage manufacturing company, “Tummy Tummy Foods Industries Limited,” located on Chicason Drive, Umudim, Nnewi, Anambra State, Nigeria. The company was incorporated in December 2008 (with RC No: 793032) as a producer and distributor of Tummy-Tummy Instant Noodles for human consumption, with production commencing in October 2009. The data used in the study were collected over a thirty-six-month period, providing records of equipment failure times for the duration. This data was essential for determining the Overall Equipment

Effectiveness (OEE) values. Following this, the three OEE rates—availability, performance, and quality—were calculated individually, as shown in table 2.

<table><caption>Table 2: OEE Variables</caption><thead><tr><th rowspan="2">S/No</th><th rowspan="2">Category</th><th rowspan="2">January</th><th rowspan="2">February</th><th rowspan="2">March</th><th rowspan="2">April</th><th rowspan="2">May</th><th rowspan="2">June</th><th colspan="2">Months (1<sup>st</sup> Year)</th><th rowspan="2">September</th><th rowspan="2">October</th><th rowspan="2">November</th><th rowspan="2">December</th></tr><tr><th>July</th><th>August</th></tr></thead><tbody><tr><td>1</td><td>Availability (A) %</td><td>73.10</td><td>72.70</td><td>76.60</td><td>75.60</td><td>73.50</td><td>45.20</td><td>26.70</td><td>70.40</td><td>70.30</td><td>68.60</td><td>52.30</td><td>67.90</td></tr><tr><td>2</td><td>Performance rate (P) %</td><td>73.40</td><td>60.40</td><td>75.52</td><td>79.70</td><td>80.90</td><td>66.30</td><td>49.30</td><td>84.40</td><td>83.40</td><td>73.20</td><td>72.90</td><td>73.60</td></tr><tr><td>3</td><td>Quality rate (Q) %</td><td>93.7</td><td>93.40</td><td>94.80</td><td>92.00</td><td>91.90</td><td>70.60</td><td>80.70</td><td>91.20</td><td>90.20</td><td>88.90</td><td>72.50</td><td>83.40</td></tr><tr><td>4</td><td>QEE</td><td>50.32</td><td>40.01</td><td>53.05</td><td>55.43</td><td>54.64</td><td>21.15</td><td>10.62</td><td>54.18</td><td>52.88</td><td>44.64</td><td>27.64</td><td>41.67</td></tr></tbody><thead><tr><th rowspan="2">S/No</th><th rowspan="2">Category</th><th rowspan="2">January</th><th rowspan="2">February</th><th rowspan="2">March</th><th rowspan="2">April</th><th colspan="2">Months (2<sup>nd</sup> Year)</th><th rowspan="2">August</th><th rowspan="2">September</th><th rowspan="2">October</th><th rowspan="2">November</th><th rowspan="2">December</th></tr><tr><th>May</th><th>June</th></tr></thead><tbody><tr><td>1</td><td>Availability (A) %</td><td>67.90</td><td>75.78</td><td>77.20</td><td>76.60</td><td>70.80</td><td>66.87</td><td>77.87</td><td>72.46</td><td>72.76</td><td>78.43</td><td>77.56</td><td>78.60</td></tr><tr><td>2</td><td>Performance rate (P) %</td><td>73.60</td><td>79.40</td><td>80.10</td><td>78.80</td><td>83.90</td><td>73.30</td><td>79.30</td><td>74.70</td><td>73.47</td><td>79.80</td><td>76.22</td><td>79.60</td></tr><tr><td>3</td><td>Quality rate (Q) %</td><td>83.70</td><td>95.70</td><td>95.44</td><td>93.31</td><td>91.05</td><td>90.66</td><td>90.70</td><td>90.10</td><td>90.00</td><td>94.05</td><td>92.47</td><td>91.32</td></tr><tr><td>4</td><td>QEE %</td><td>41.82</td><td>57.58</td><td>59.01</td><td>56.32</td><td>54.08</td><td>44.43</td><td>56.00</td><td>48.76</td><td>48.11</td><td>53.99</td><td>47.26</td><td>52.82</td></tr></tbody><thead><tr><th rowspan="2">S/No</th><th rowspan="2">Category</th><th rowspan="2">January</th><th rowspan="2">February</th><th rowspan="2">March</th><th rowspan="2">April</th><th colspan="3">Months (3<sup>rd</sup> Year)</th><th rowspan="2">August</th><th rowspan="2">September</th><th rowspan="2">October</th><th rowspan="2">November</th><th rowspan="2">December</th></tr><tr><th>May</th><th>June</th><th>July</th></tr></thead><tbody><tr><td>1</td><td>Availability (A) %</td><td>75.86</td><td>73.08</td><td>70.88</td><td>72.46</td><td>74.70</td><td>78.00</td><td>76.40</td><td>69.60</td><td>69.80</td><td>72.90</td><td>77.37</td><td>78.50</td></tr><tr><td>2</td><td>Performance rate (P) %</td><td>84.30</td><td>81.78</td><td>80.00</td><td>81.23</td><td>83.66</td><td>85.30</td><td>84.63</td><td>74.84</td><td>74.92</td><td>81.73</td><td>84.00</td><td>85.52</td></tr><tr><td>3</td><td>Quality rate (Q) %</td><td>90.73</td><td>88.39</td><td>86.00</td><td>87.88</td><td>89.42</td><td>92.07</td><td>91.42</td><td>85.25</td><td>85.73</td><td>87.22</td><td>91.49</td><td>92.36</td></tr><tr><td>4</td><td>QEE %</td><td>58.02</td><td>53.34</td><td>48.77</td><td>51.72</td><td>55.88</td><td>61.25</td><td>59.11</td><td>44.40</td><td>44.83</td><td>51.97</td><td>59.44</td><td>62.00</td></tr></tbody></table>

## 4. Results and Discussion

Based on the data in table 2, a basic descriptive analysis and optimization using response surface methodology with a Central Composite design are performed. The results are discussed as follows.

### 4.1 Descriptive Statistics

This analysis aims to summarize the characteristics of the collected data, including measures such as the mean, standard deviation, and the minimum and maximum of a variable, offering a statistical overview of the parameters of the manufacturing system.

<caption>Table 3: Descriptive Statistics</caption>
<table><thead><tr><td>Variable</td><td>Mean</td><td>StDev.</td><td>Minimum</td><td>Maximum</td></tr></thead><tbody><tr><td>Availability</td><td>70.98</td><td>10.1611</td><td>26.7</td><td>78.6</td></tr><tr><td>Performance</td><td>77.4197</td><td>7.33714</td><td>49.3</td><td>85.51</td></tr><tr><td>Quality</td><td>89.16</td><td>5.53284</td><td>70.6</td><td>95.7</td></tr><tr><td>OEE</td><td>49.365</td><td>10.8726</td><td>10.62</td><td>62</td></tr></tbody></table>

From table 3 the mean Availability, Performance, and Quality values are 70.98%, 77.41%, and 89.16%, respectively, yielding a mean OEE of 49.36%, resulting in

an OEE range of 10.62% and 62.00%. This show the statistical parameters of the production system with the quality attribute having the highest mean parameter.

<table><caption>Table 4: OEE Variables Correlation</caption><thead><tr><td></td><td>Availability</td><td>Performance</td><td>Quality</td></tr></thead><tbody><tr><td>Performance</td><td>0.756</td><td></td><td></td></tr><tr><td>Quality</td><td>-0.170</td><td>-0.251</td><td></td></tr><tr><td>OEE</td><td>0.928</td><td>0.878</td><td>-0.257</td></tr></tbody></table>

<figure><img src="image_1.png" /><figcaption>Figure 1: Matrix Plot of OEE Variables</figcaption></figure>

The Pearson correlation as shown in figure 1 measures the strength of the linear relationship between two variables, ranging from -1 to 1. A value of -1 indicates a complete

negative correlation, 0 indicates no correlation, and +1 indicates a complete positive correlation. The results from Table 3 and Figure 1 reveal a strong positive correlation
(0.756) between performance and availability, indicating that as availability increases, the manufacturing system's performance tends to improve. Additionally, the findings show a weak negative correlation between performance and quality (-0.251), and between availability and quality (-0.170). Since these correlations are weak, it suggests that other factors in the manufacturing system may be influencing both variables more significantly.

The correlation between Overall Equipment Effectiveness (OEE) and availability is very strong (0.928), suggesting that availability plays a crucial role in OEE, with OEE significantly increasing as availability rises. Likewise, OEE and performance have a strong positive correlation (0.878), indicating that performance is another key factor influencing OEE. As performance improves, OEE also increases substantially.

There is a somewhat negative correlation between OEE and quality (-0.257), indicating that OEE slightly decreases as quality improves. While this negative association is small, it suggests that optimizing OEE might occasionally come at the expense of quality. These correlations highlight that while performance and availability are crucial for high OEE, quality may require additional attention and optimization to ensure it is not compromised during OEE improvements.

To further enhance OEE characteristics, response surface methodology was employed to design, evaluate, and optimize availability, quality, and performance. The input parameters are listed in Table 5, the model summary is presented in Table 7, and Table 6 provides the analysis of variance, leading to a final OEE equation that takes the actual components into account while disregarding insignificant values.

<table><caption>Table 5: Input parameters</caption><thead><tr><th>Factor</th><th>Name</th><th>Units</th><th>Type</th><th>subtype</th><th>minimum</th><th>maximum</th><th>Coded Low</th><th>Coded High</th><th>Mean</th><th>STD Dev.</th></tr></thead><tbody><tr><td>A</td><td>Availability</td><td>Numeric</td><td>Continuous</td><td>26.7</td><td>78.60</td><td>-1↔1.00</td><td>+1↔1.00</td><td>70.9800</td><td>10.1611</td></tr><tr><td>B</td><td>Performance</td><td>Numeric</td><td>Continuous</td><td>49.3</td><td>85.51</td><td>-1↔1.00</td><td>+1↔1.00</td><td>77.4197</td><td>7.3371</td></tr><tr><td>C</td><td>Quality</td><td>Numeric</td><td>Continuous</td><td>70.6</td><td>95.70</td><td>-1↔1.00</td><td>+1↔1.00</td><td>89.1600</td><td>5.5328</td></tr></tbody></table>

<table><caption>Table 6: Coded Coefficients</caption><thead><tr><th>Term</th><th>Coef</th><th>SE Coef</th><th>T-Value</th><th>P-Value</th><th>VIF</th></tr></thead><tbody><tr><td>Constant</td><td>29.506</td><td>0.215</td><td>137.40</td><td>0.000</td><td></td></tr><tr><td>Availability</td><td>14.545</td><td>0.142</td><td>102.09</td><td>0.000</td><td>1.00</td></tr><tr><td>Performance</td><td>7.928</td><td>0.142</td><td>55.64</td><td>0.000</td><td>1.00</td></tr><tr><td>Quality</td><td>4.453</td><td>0.142</td><td>31.26</td><td>0.000</td><td>1.00</td></tr><tr><td>Availability*Availability</td><td>0.001</td><td>0.139</td><td>0.01</td><td>0.994</td><td>1.02</td></tr><tr><td>Performance*Performance</td><td>-0.000</td><td>0.139</td><td>-0.00</td><td>0.998</td><td>1.02</td></tr><tr><td>Quality*Quality</td><td>0.001</td><td>0.139</td><td>0.01</td><td>0.994</td><td>1.02</td></tr><tr><td>Availability*Performance</td><td>3.906</td><td>0.186</td><td>20.98</td><td>0.000</td><td>1.00</td></tr><tr><td>Availability*Quality</td><td>2.196</td><td>0.186</td><td>11.80</td><td>0.000</td><td>1.00</td></tr><tr><td>Performance*Quality</td><td>1.197</td><td>0.186</td><td>6.43</td><td>0.000</td><td>1.00</td></tr></tbody></table>

<caption>Table 7: Model Summary</caption>
<table><caption>Table 8: Analysis of Variance</caption><thead><tr><th>S</th><th>R-sq</th><th>R-sq(adj)</th><th>R-sq(pred)</th></tr></thead><tbody><tr><td>0.526512</td><td>99.93%</td><td>99.87%</td><td>99.39%</td></tr></tbody></table>

<table><thead><tr><th>Source</th><th>DF</th><th>Adj SS</th><th>Adj MS</th><th>F-Values</th><th>P-Values</th></tr></thead><tbody><tr><td>Model</td><td>9</td><td>4190.30</td><td>465.59</td><td>1679.53</td><td>0.000</td></tr><tr><td>Linear</td><td>3</td><td>4018.23</td><td>1339.41</td><td>4831.67</td><td>0.000</td></tr><tr><td>Availability</td><td>1</td><td>2889.09</td><td>2889.09</td><td>10421.87</td><td>0.000</td></tr><tr><td>Performance</td><td>1</td><td>858.30</td><td>858.30</td><td>3096.15</td><td>0.000</td></tr><tr><td>Quality</td><td>1</td><td>270.84</td><td>270.84</td><td>976.99</td><td>0.000</td></tr><tr><td>Square</td><td>3</td><td>0.00</td><td>0.00</td><td>0.00</td><td>1.000</td></tr><tr><td>Availability*Availability</td><td>1</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.994</td></tr><tr><td>Performance*Performance</td><td>1</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.998</td></tr><tr><td>Quality*Quality</td><td>1</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.994</td></tr><tr><td>2-Way Interaction</td><td>3</td><td>172.07</td><td>57.36</td><td>206.91</td><td>0.000</td></tr><tr><td>Availability*Performance</td><td>1</td><td>122.03</td><td>122.03</td><td>440.20</td><td>0.000</td></tr><tr><td>Availability*Quality</td><td>1</td><td>38.58</td><td>38.58</td><td>139.17</td><td>0.000</td></tr><tr><td>Performance*Quality</td><td>1</td><td>11.46</td><td>11.46</td><td>41.35</td><td>0.000</td></tr><tr><td>Error</td><td>10</td><td>2.77</td><td>0.28</td><td></td><td></td></tr><tr><td>Lack-of-Fit</td><td>5</td><td>2.77</td><td>0.55</td><td>26359.07</td><td>0.000</td></tr><tr><td>Pure Error</td><td>5</td><td>0.00</td><td>0.00</td><td></td><td></td></tr><tr><td>Total</td><td>19</td><td>4193.07</td><td></td><td></td><td></td></tr></tbody></table>

$$ \text{OEE} = 29.57 - 0.5607A - 0.4377P - 0.356Q + 0.000002A*A - 0.000001P*P + 0.000007Q*Q + 0.008313A*P + 0.006743A*Q + 0.005268P*Q \quad (1.3) $$

Availability is denoted by A, Performance by P, and Quality by Q.

In Table 6, the baseline OEE value is presented when all predictor variables (Availability, Performance, and Quality) are set at their coded zero levels. Availability, with a coefficient of 14.545, T-value of 102.09, and P-value of 0.000, has a strong positive impact on OEE and is highly significant (P-value < 0.05). Performance, with a coefficient of 7.928, T-value of 55.64, and P-value of 0.000, also

positively affects OEE and is highly significant (P-value < 0.05). Quality, with a coefficient of 4.453, T-value of 31.26, and P-value of 0.000, has a positive effect on OEE and is highly significant (P-value < 0.05).

All quadratic terms have very small coefficients and P-values, indicating they are not significant. The interaction effects reveal significant interactions between Availability and Performance, Availability and Quality, and Performance and Quality.

From Table 7, the values show an excellent fit of the model
in Equation 1.3 to the data, with R-squared values close to 100%, indicating that nearly all variability in OEE is explained by the model. Table 8 shows that the overall regression model is significant and linear.

Focusing on Availability while improving both Performance and Quality in the manufacturing system will greatly enhance OEE improvement.

The contour plots in Figures 2, 3, and 4 depict the influence of input parameters on the output parameter. In Figure 2, the contour lines represent varying OEE levels as a function of Quality and Availability, showing that increasing both generally leads to higher OEE. The region with the highest

OEE is toward the upper right corner, indicating maximum OEE with high Availability and Quality.

In Figure 3, higher Performance and Availability levels correspond to higher OEE, with OEE increasing more rapidly with Availability than Performance, highlighting the greater influence of Availability on OEE. Figure 4 shows how OEE varies with Performance and Quality, indicating that both factors positively impact OEE, though the relationship is not as strong as with Availability.

<figure><img src="image_2.png" /><figcaption>Figure 2: The response surface methodology Contour plot of OEE vs Quality and availability</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>Figure 3: The response surface methodology Contour plot of OEE vs Performance and availability</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>Figure 4: The response surface methodology Contour plot of OEE vs Performance and quality</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Figure 5: The Surface Plot of OEE vs Performance and Availability</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Figure 6: The response surface methodology surface plot of OEE vs Quality and Availability</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Figure 7: The response surface methodology surface plot of OEE vs Quality and Performance</figcaption></figure>

Figures 5, 6 and 7, provide a more detailed view of the relationship between OEE, Performance, Availability and Quality. The steepness in figure 5 indicates that changes in Availability have a more significant effect on OEE compared to changes in Performance. In figure 6, the steepness is along the Availability axis, reinforcing the conclusion that Availability has a stronger impact on OEE than Quality. While in figure 7, the surface is relatively flat compared to the previous plots, suggesting a more balanced impact of both factors on OEE.

In summary, Availability is the most significant factor influencing OEE. All plots show that improvements in Availability lead to substantial increases in OEE. Performance is also a crucial factor, but its impact is generally less pronounced than that of Availability. While Quality positively affects OEE, but its impact is less significant compared to Availability and Performance. Increasing Availability is the main goal to optimizing OEE, along with enhancing Performance and Quality attributes as shown in the optimal solution in table 8.

<table><caption>Table 9: System Parameters and Optimal Solution</caption><thead><tr><th>Solution</th><th>Availability</th><th>Performance</th><th>Quality</th><th>OEE Fit</th><th>Composite Desirability</th></tr></thead><tbody><tr><td>1</td><td>78.6</td><td>85.51</td><td>98</td><td>65.98</td><td>1</td></tr><tr><th>Response</th><th>Goal</th><th>Lower</th><th>Upper</th><th>Weight</th><th>Importance</th></tr><tr><td>OEE</td><td>Maximum</td><td>5.04844</td><td>64.32</td><td>1</td><td>1</td></tr></tbody></table>

## 5. Conclusion

The study successfully identified key factors impacting the OEE in a food manufacturing SME and provided a clear direction for optimizing these factors. By focusing on improving Availability while maintaining a balance between Performance and Quality, the company can achieve significant improvements in OEE, leading to enhanced

efficiency and profitability. Implementing these findings through strategic maintenance and operational adjustments can help the company achieve world-class OEE standards. The study demonstrated that Availability is the most critical factor influencing OEE in the manufacturing company, with Performance and Quality also contributing significantly. Basic descriptive analysis and response surface methodology (RSM) with Central Composite Design-based
optimization was carried out to analyze the manufacturing
system parameters and optimize OEE. Descriptive statistics
highlighted that the mean values for Availability,
Performance, and Quality were 70.98%, 77.41%, and
89.16%, respectively, resulting in a mean OEE of 49.36%.
Pearson correlation analysis indicated a strong positive
correlation between OEE and both Availability (0.928) and
Performance (0.878), while a weak negative correlation was
observed between Quality and OEE (-0.257). RSM analysis
revealed that Availability had the most significant impact on
OEE, with a coefficient of 14.545, followed by Performance
(7.928) and Quality (4.453). Interaction effects among
Availability, Performance, and Quality were also

Acknowledgement

The authors acknowledge the efforts of the management
of the Nnamdi Azikiwe University Akwa in stimulating
this research.

References

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significant. The model demonstrated an excellent fit, with
R-square value of 99.93%, close to 100%. Contour and
surface plots further confirmed that increasing Availability
significantly enhances the OEE, with Performance and
Quality also contributing positively, albeit to a lesser extent.
The optimal solution suggested that maximum OEE of
65.98% could be achieved with an Availability of 78.6%,
Performance of 85.51%, and Quality of 98%. Therefore,
strategic focus on optimizing Availability, while also
addressing Performance and Quality, is crucial for
maximizing OEE and achieving higher efficiency in the
manufacturing company.

Policy for Inventory Control in a Small and Medium Enterprise,” *International Journal of Engineering Science and Computing*, 12(11), 2022, 29829–29833.

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