# Optimal product quantity linear programming model for profit maximization in plastic small medium manufacturing firms

Nkemakonam Igbokwe

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Nkemakonam Igbokwe. Optimal product quantity linear programming model for profit maximization in plastic small medium manufacturing firms. World Journal of Advanced Research and Reviews, 2024, 22, pp.850 - 856. 10.30574/wjarr.2024.22.1.1164. hal-04563596

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# Optimal product quantity linear programming model for profit maximization in plastic small medium manufacturing firms

Nkemakonam Igbokwe

► To cite this version:

Nkemakonam Igbokwe. Optimal product quantity linear programming model for profit maximization in plastic small medium manufacturing firms. World Journal of Advanced Research and Reviews, 2024, 22, pp.850 - 856. 10.30574/wjarr.2024.22.1.1164. hal-04563596

HAL Id: hal-04563596

https://hal.science/hal-04563596v1

Submitted on 29 Apr 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

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Distributed under a Creative Commons Attribution 4.0 International License
(RESEARCH ARTICLE)

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Optimal product quantity linear programming model for profit maximization in plastic small medium manufacturing firms

Igbokwe Nkemakonam Chidiebube *, Ibekwe Chukwuemeka John, Umunna Daniel and Okoli Loveth

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

World Journal of Advanced Research and Reviews, 2024, 22(01), 850–856

Publication history: Received on 06 March 2024; revised on 15 April 2024; accepted on 18 April 2024

Article DOI: https://doi.org/10.30574/wjarr.2024.22.1.1164

**Abstract**

Planning production and maximizing profits can be difficult tasks for small and medium-sized manufacturing businesses (SMEs). Owing to their difficulties implementing the newest and most advanced Industry 4.0 technologies, small and medium-sized enterprises (SMEs) generally depend more on expert guidance. Thus this study focuses on the development of a Linear programming model for determining the optimum quantities of product in order to maximize profit profits in manufacturing Small medium enterprises. Data spanning over a period of forty five months was obtained from a manufacturing small and medium-sized enterprise. An optimization was developed in the study to determine the optimal product quantity for profit maximization. Based on the developed model and its optimization, it was determined that the optimal production quantity for the plastic factory to be 12000cups resulting in a maximum profit of ₦456000. This optimization approach provides valuable insights for the plastic factory's production planning.

**Keywords:** Linear programming; Linear optimization; Manufacturing SME; Optimal product quantity; Plastic industry

**1. Introduction**

Product and service providers are under tremendous pressure to improve and modernize their processes and procedures due to increased global competition and corporate growth (Chukwutoo and Nkemakonam, 2018). Manufacturing small medium enterprises (SMEs) must now establish industrial strategies in order to compete effectively, as the industry is seeing exponential growth in competition due to customers' increasing demands and the increasing unpredictability of supply (Chikwendu et al., 2020; Igbokwe and Godwin, 2021; N. Igbokwe and Mba, 2019).

Production planning and profit maximization is often a challenging prospect for manufacturing small medium enterprises (SMEs).

Due to their challenges in adopting the newest and most sophisticated Industry 4.0 technologies, small and medium-sized firms (SMEs) typically rely more on professional advice (Skéré et al., 2023). The majority of manufacturing firms aim to maximize profits or decrease costs with limited resources, despite the fact that management issues are always changing. For this reason, manufacturing organizations must manufacture the correct amount of product and fulfill anticipated demand in order to maximize profit.

Production planning can be defined as a method of anticipating each step in the drawn-out production process, taking them at the appropriate time and degree, and attempting to finish activities with the highest possible level of efficiency (Alexopoulos et al., 2022). Due to their reliance on a variety of raw materials and complex manufacturing processes, small and medium-size enterprises in the plastics industry need to plan and optimize their operations carefully in order to maximize output, minimize waste, and assure streamlined operations. These are primarily mass-production, made-

<footnote>* Corresponding author: Igbokwe Nkemakonam Chidiebube</footnote>
(RESEARCH ARTICLE)

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# Optimal product quantity linear programming model for profit maximization in plastic small medium manufacturing firms

Igbokwe Nkemakonam Chidiebube *, Ibekwe Chukwuemeka John, Umunna Daniel and Okoli Loveth

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

World Journal of Advanced Research and Reviews, 2024, 22(01), 850–856

Publication history: Received on 06 March 2024; revised on 15 April 2024; accepted on 18 April 2024

Article DOI: https://doi.org/10.30574/wjarr.2024.22.1.1164

## Abstract

Planning production and maximizing profits can be difficult tasks for small and medium-sized manufacturing businesses (SMEs). Owing to their difficulties implementing the newest and most advanced Industry 4.0 technologies, small and medium-sized enterprises (SMEs) generally depend more on expert guidance. Thus this study focuses on the development of a Linear programming model for determining the optimum quantities of product in order to maximize profit profits in manufacturing Small medium enterprises. Data spanning over a period of forty five months was obtained from a manufacturing small and medium-sized enterprise. An optimization was developed in the study to determine the optimal product quantity for profit maximization. Based on the developed model and its optimization, it was determined that the optimal production quantity for the plastic factory to be 12000cups resulting in a maximum profit of ₦456000. This optimization approach provides valuable insights for the plastic factory's production planning.

**Keywords:** Linear programming; Linear optimization; Manufacturing SME; Optimal product quantity; Plastic industry

## 1. Introduction

Product and service providers are under tremendous pressure to improve and modernize their processes and procedures due to increased global competition and corporate growth (Chukwutoo and Nkemakonam, 2018). Manufacturing small medium enterprises (SMEs) must now establish industrial strategies in order to compete effectively, as the industry is seeing exponential growth in competition due to customers' increasing demands and the increasing unpredictability of supply (Chikwendu et al., 2020; Igbokwe and Godwin, 2021; N. Igbokwe and Mba, 2019).

Production planning and profit maximization is often a challenging prospect for manufacturing small medium enterprises (SMEs).

Due to their challenges in adopting the newest and most sophisticated Industry 4.0 technologies, small and medium-sized firms (SMEs) typically rely more on professional advice (Skéré et al., 2023). The majority of manufacturing firms aim to maximize profits or decrease costs with limited resources, despite the fact that management issues are always changing. For this reason, manufacturing organizations must manufacture the correct amount of product and fulfill anticipated demand in order to maximize profit.

Production planning can be defined as a method of anticipating each step in the drawn-out production process, taking them at the appropriate time and degree, and attempting to finish activities with the highest possible level of efficiency (Alexopoulos et al., 2022). Due to their reliance on a variety of raw materials and complex manufacturing processes, small and medium-size enterprises in the plastics industry need to plan and optimize their operations carefully in order to maximize output, minimize waste, and assure streamlined operations. These are primarily mass-production, made-

<footnote>* Corresponding author: Igbokwe Nkemakonam Chidiebube</footnote>
to-stock businesses. In practice, they base their production level on anticipated demand. Optimizing production quantities to maximize profit is a fundamental problem for various manufacturing firm. Understanding the complex interplay between production costs, selling prices, and demand becomes crucial for achieving sustainable success. Thus this study focuses on the development of a Linear programming model for determining the optimum quantities of product in order to maximize profit profits in manufacturing Small medium enterprises. The integration of this model ensures adaptability to changing market conditions, thereby contributing to the overall competitiveness and sustainability of the enterprise.

## 2. Methodology

The first phase of the research will involve collecting and analyzing data from the Nnamdi Azikiwe University Plastic Factory. The case study is a medium-sized plastic manufacturing company that operates a one injection molding machine that produces plastic cups. The factory operates from 8am to 5pm every weekday, since the company is a made-to-stock company; production is based on forecasted demand.

The data were gathered through direct observation of the production processes, interview with some key personnel of the factory. The observation of the production processes was carried out so as to understand the stages of production from raw-material to finished product. Historical data over the period of forty five months were also obtained, this data includes production data, resource availability, processing and setup times, product information, machine capacity and sales data.

### 2.1. Development of Linear Programming Model

In this study Linear Programming (LP) technique is used in the production planning of plastic cups production on a single injection molding machine. The aim here is to develop a plan that will give the optimal quantities of these parts. The materials involved in the production of the products is the same as they are, Recycled propylene plastic and Master Batch.

The variables and parameters of the model are as follows:

Decision Variables:

$X_1$ = Number of cups produced per month

Model Variables and Parameters:

$P_T$ = Total Profit

$P_1$ = Profit of Unit of $X_1$

$D_1$ = Forecasted demand of $X_1$ per month

$IMC_1$ = Injection Molding Capacity per Month

$T_1$ = Total Available Machine Hours

$M_a$ = Total Quantity (Kg) of raw materials available for Recycled Propylene Plastics

$M_b$ = Total Quantity (Kg) of raw materials available for Master Batch

$r_a$ = Required raw material needed to produce $X_1$

$r_b$ = Required raw material needed to produce $X_1$

$r_c$ = Required raw material needed to produce $X_1$

$t_1$ = Production time required to produced $X_1$

Objective Function:

$$ \text{Max } P_T = P_1 X_1 \dots\dots\dots\dots\dots\dots(1) $$

Subject to:

$$ X_1 \geq D_1 \dots\dots\dots\dots\dots\dots\dots(2) $$

$$ X_1 \leq IMC_1 \dots\dots\dots\dots\dots\dots\dots(3) $$

$$ r_a X_1 \leq M_a \dots\dots\dots\dots\dots\dots\dots(4) $$
to-stock businesses. In practice, they base their production level on anticipated demand. Optimizing production quantities to maximize profit is a fundamental problem for various manufacturing firm. Understanding the complex interplay between production costs, selling prices, and demand becomes crucial for achieving sustainable success. Thus this study focuses on the development of a Linear programming model for determining the optimum quantities of product in order to maximize profit profits in manufacturing Small medium enterprises. The integration of this model ensures adaptability to changing market conditions, thereby contributing to the overall competitiveness and sustainability of the enterprise.

## 2. Methodology

The first phase of the research will involve collecting and analyzing data from the Nnamdi Azikiwe University Plastic Factory. The case study is a medium-sized plastic manufacturing company that operates a one injection molding machine that produces plastic cups. The factory operates from 8am to 5pm every weekday, since the company is a made-to-stock company; production is based on forecasted demand.

The data were gathered through direct observation of the production processes, interview with some key personnel of the factory. The observation of the production processes was carried out so as to understand the stages of production from raw-material to finished product. Historical data over the period of forty five months were also obtained, this data includes production data, resource availability, processing and setup times, product information, machine capacity and sales data.

### 2.1. Development of Linear Programming Model

In this study Linear Programming (LP) technique is used in the production planning of plastic cups production on a single injection molding machine. The aim here is to develop a plan that will give the optimal quantities of these parts. The materials involved in the production of the products is the same as they are, Recycled propylene plastic and Master Batch.

The variables and parameters of the model are as follows:

**Decision Variables:**

$X_1$ = Number of cups produced per month

**Model Variables and Parameters:**

$P_T$ = Total Profit

$P_1$ = Profit of Unit of $X_1$

$D_1$ = Forecasted demand of $X_1$ per month

$IMC_1$ = Injection Molding Capacity per Month

$T_1$ = Total Available Machine Hours

$M_a$ = Total Quantity (Kg) of raw materials available for Recycled Propylene Plastics

$M_b$ = Total Quantity (Kg) of raw materials available for Master Batch

$r_a$ = Required raw material needed to produce $X_1$

$r_b$ = Required raw material needed to produce $X_1$

$r_c$ = Required raw material needed to produce $X_1$

$t_1$ = Production time required to produced $X_1$

**Objective Function:**

$$ \text{Max } P_T = P_1 X_1 \dots\dots\dots\dots\dots(1) $$

**Subject to:**

$$ X_1 \geq D_1 \dots\dots\dots\dots\dots\dots(2) $$

$$ X_1 \leq IMC_1 \dots\dots\dots\dots\dots\dots(3) $$

$$ r_a X_1 \leq M_a \dots\dots\dots\dots\dots\dots(4) $$
<table><caption>**Table 2** Data on Selling Price, Cost Price, Profit, Time and Machine Capacity</caption><tbody><tr><td></td><td>June</td><td>17259</td><td>18306</td></tr><tr><td></td><td>July</td><td>15377</td><td>15025</td></tr><tr><td></td><td>August</td><td>16590</td><td>8860</td></tr><tr><td></td><td>September</td><td>11882</td><td>19104</td></tr><tr><td></td><td>October</td><td>15380</td><td>7219</td></tr><tr><td></td><td>November</td><td>13813</td><td>21557</td></tr><tr><td></td><td>December</td><td>24867</td><td>25619</td></tr><tr><td rowspan="10">2022</td><td>January</td><td>20314</td><td>24361</td></tr><tr><td>February</td><td>17115</td><td>15558</td></tr><tr><td>March</td><td>23165</td><td>13740</td></tr><tr><td>April</td><td>13623</td><td>18360</td></tr><tr><td>May</td><td>16395</td><td>18960</td></tr><tr><td>June</td><td>12763</td><td>8910</td></tr><tr><td>July</td><td>15211</td><td>16333</td></tr><tr><td>August</td><td>23104</td><td>27458</td></tr><tr><td>September</td><td>24803</td><td>24228</td></tr><tr><td>October</td><td>16324</td><td>8886</td></tr><tr><td>November</td><td>15451</td><td>19391</td></tr><tr><td>December</td><td>15226</td><td>19121</td></tr><tr><td rowspan="10">2023</td><td>January</td><td>17127</td><td>12032</td></tr><tr><td>February</td><td>17527</td><td>12012</td></tr><tr><td>March</td><td>15487</td><td>23046</td></tr><tr><td>April</td><td>18505</td><td>19518</td></tr><tr><td>May</td><td>22581</td><td>16655</td></tr><tr><td>June</td><td>13291</td><td>336</td></tr><tr><td>July</td><td>18222</td><td>12643</td></tr><tr><td>August</td><td>23499</td><td>17844</td></tr><tr><td>September</td><td>21916</td><td>12301</td></tr></tbody></table>

<table><thead><tr><td>Selling Price per Unit (Naira)</td><td>Cost Price per Unit (Naira)</td><td>Profit Per Unit (Naira)</td><td>Machine Capacity</td><td>Production time required for a Unit (Hours)</td><td>Total Available Machine (Hours)</td></tr></thead><tbody><tr><td>168</td><td>127</td><td>38</td><td>27840</td><td>0.006</td><td>176</td></tr></tbody></table>
<table><caption>**Table 2** Data on Selling Price, Cost Price, Profit, Time and Machine Capacity</caption><tbody><tr><td></td><td>June</td><td>17259</td><td>18306</td></tr><tr><td></td><td>July</td><td>15377</td><td>15025</td></tr><tr><td></td><td>August</td><td>16590</td><td>8860</td></tr><tr><td></td><td>September</td><td>11882</td><td>19104</td></tr><tr><td></td><td>October</td><td>15380</td><td>7219</td></tr><tr><td></td><td>November</td><td>13813</td><td>21557</td></tr><tr><td></td><td>December</td><td>24867</td><td>25619</td></tr><tr><td rowspan="10">2022</td><td>January</td><td>20314</td><td>24361</td></tr><tr><td>February</td><td>17115</td><td>15558</td></tr><tr><td>March</td><td>23165</td><td>13740</td></tr><tr><td>April</td><td>13623</td><td>18360</td></tr><tr><td>May</td><td>16395</td><td>18960</td></tr><tr><td>June</td><td>12763</td><td>8910</td></tr><tr><td>July</td><td>15211</td><td>16333</td></tr><tr><td>August</td><td>23104</td><td>27458</td></tr><tr><td>September</td><td>24803</td><td>24228</td></tr><tr><td>October</td><td>16324</td><td>8886</td></tr><tr><td>November</td><td>15451</td><td>19391</td></tr><tr><td>December</td><td>15226</td><td>19121</td></tr><tr><td rowspan="10">2023</td><td>January</td><td>17127</td><td>12032</td></tr><tr><td>February</td><td>17527</td><td>12012</td></tr><tr><td>March</td><td>15487</td><td>23046</td></tr><tr><td>April</td><td>18505</td><td>19518</td></tr><tr><td>May</td><td>22581</td><td>16655</td></tr><tr><td>June</td><td>13291</td><td>336</td></tr><tr><td>July</td><td>18222</td><td>12643</td></tr><tr><td>August</td><td>23499</td><td>17844</td></tr><tr><td>September</td><td>21916</td><td>12301</td></tr></tbody></table>

<table><thead><tr><td>Selling Price per Unit (Naira)</td><td>Cost Price per Unit (Naira)</td><td>Profit Per Unit (Naira)</td><td>Machine Capacity</td><td>Production time required for a Unit (Hours)</td><td>Total Available Machine (Hours)</td></tr></thead><tbody><tr><td>168</td><td>127</td><td>38</td><td>27840</td><td>0.006</td><td>176</td></tr></tbody></table>
<table><caption>**Table 3** Data on Raw Materials</caption><thead><tr><td>Raw Material</td><td>Raw Material Usage (kg)</td><td>Raw Material Available (kg)</td></tr></thead><tbody><tr><td>Recycled Propylene Plastic</td><td>0.08</td><td>1173</td></tr><tr><td>Master Batch</td><td>0.002</td><td>24</td></tr></tbody></table>

### 3.1. Solution of the Linear Programming Model

The objective function of the model is to maximize profit subject the constraints. Data from Table 1.2 was inputted into the model and solved using Pulp in Python.

Objective Function

$$ \text{Max}_Z = P \times X_1 \quad (1) $$

$$ X_1 \geq 5775 \quad (2) $$

$$ X_1 \leq 27840 \quad (3) $$

$$ 0.08 \times X_1 \leq 1173 \quad (4) $$

$$ 0.002 \times X_1 \leq 24 \quad (5) $$

$$ 0.006 \times X_1 \geq 176 \quad (7) $$

$$ X_1 \geq 0 \quad (8) $$

The programming code used to solve the model is showed in figure 1.0

<figure><img src="image_3.png" /><figcaption>**Figure 1** Solution Model Codes</figcaption></figure>

The original model gave room for inventory by allowing production to exceed demand, the optimum value of cups that should be produced gave 12000cups and a gross profit of ₦456.000 as shown in fig 1.2. When ≥ was changed to ≤ in equation (2) to prevent inventory the optimum value under the constraints shows that the factory should produce exactly the forecasted demand which is 5775 cups which will give a profit of ₦219, 412 as shown in fig 1.3. Since the objective is to find out the number of cups that will yield maximum profit the factory should produce 12000cups as this will give the maximum profit.
<table><caption>**Table 3** Data on Raw Materials</caption><thead><tr><td>Raw Material</td><td>Raw Material Usage (kg)</td><td>Raw Material Available (kg)</td></tr></thead><tbody><tr><td>Recycled Propylene Plastic</td><td>0.08</td><td>1173</td></tr><tr><td>Master Batch</td><td>0.002</td><td>24</td></tr></tbody></table>

### 3.1. Solution of the Linear Programming Model

The objective function of the model is to maximize profit subject the constraints. Data from Table 1.2 was inputted into the model and solved using Pulp in Python.

Objective Function

$$ \text{Max}_Z = P \times X_1 \quad (1) $$

$$ X_1 \geq 5775 \quad (2) $$

$$ X_1 \leq 27840 \quad (3) $$

$$ 0.08 \times X_1 \leq 1173 \quad (4) $$

$$ 0.002 \times X_1 \leq 24 \quad (5) $$

$$ 0.006 \times X_1 \geq 176 \quad (7) $$

$$ X_1 \geq 0 \quad (8) $$

The programming code used to solve the model is showed in figure 1.0

<figure><img src="image_3.png" /><figcaption>**Figure 1** Solution Model Codes</figcaption></figure>

The original model gave room for inventory by allowing production to exceed demand, the optimum value of cups that should be produced gave 12000cups and a gross profit of ₦456.000 as shown in fig 1.2. When ≥ was changed to ≤ in equation (2) to prevent inventory the optimum value under the constraints shows that the factory should produce exactly the forecasted demand which is 5775 cups which will give a profit of ₦219, 412 as shown in fig 1.3. Since the objective is to find out the number of cups that will yield maximum profit the factory should produce 12000cups as this will give the maximum profit.
<figure><img src="image_4.png" /><figcaption>**Figure 2** Solution of the solved model</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Figure 3** Sensitivity Analysis</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Figure 4** Solution of sensitivity analysis</figcaption></figure>

## 4. Conclusion

Based on the developed model and its optimization, it was determined that the optimal production quantity for the plastic factory to be 12000cups resulting in a maximum profit of ₦456000. This solution adheres to all the specified constraints, including demand, machine capacity and raw materials as limitations.

This optimization approach provides valuable insights for the plastic factory's production planning. By adhering to the recommended production quantity, the factory can maximize its profit while ensuring efficient resource utilization and avoiding potential production shortfalls.

## Compliance with ethical standards

### Acknowledgments

The Authors will like to acknowledge Nnamdi Azikiwe University Plastic Factory for providing data for this research.
<figure><img src="image_4.png" /><figcaption>**Figure 2** Solution of the solved model</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Figure 3** Sensitivity Analysis</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Figure 4** Solution of sensitivity analysis</figcaption></figure>

## 4. Conclusion

Based on the developed model and its optimization, it was determined that the optimal production quantity for the plastic factory to be 12000 cups resulting in a maximum profit of ₦456000. This solution adheres to all the specified constraints, including demand, machine capacity and raw materials as limitations.

This optimization approach provides valuable insights for the plastic factory's production planning. By adhering to the recommended production quantity, the factory can maximize its profit while ensuring efficient resource utilization and avoiding potential production shortfalls.

## Compliance with ethical standards

### Acknowledgments

The Authors will like to acknowledge Nnamdi Azikiwe University Plastic Factory for providing data for this research.
Disclosure of conflict of interest

No conflict of interest to be disclosed.

References

[1] Alexopoulos, K., Nikolakis, N., & Xanthakis, E. (2022). Digital Transformation of Production Planning and Control in Manufacturing SMEs-The Mold Shop Case. Applied Sciences (Switzerland), 12(21). https://doi.org/10.3390/app122110788

[2] Chikwendu, O. C., Faith Ifeyinwa, O., Chidiebube, I. N., & Utochukwu, O. E. (2020). The Implementation of Kaizen Manufacturing Technique: A Case of a Tissue Manufacturing Company. Ijesc, 10(5), 25938-25949. https://hal.science/hal-03391087/document

[3] Chukwutoo, C. I., & Nkemakonam, C. I. (2018). Total Productive Maintenance (TPM) as a Business Strategy in Manufacturing Small and Medium Enterprises in Nigeria. Advances in Research, 15(5), 1-9. https://doi.org/10.9734/AIR/2018/41330

[4] Igbokwe, N. C., & Godwin, H. C. (2021). Maintenance Performance Evaluation and Downtime Analysis of Manufacturing Equipment in a Food Manufacturing Company. Journal of Engineering Research and Reports, 20(11), 100-107. https://doi.org/10.9734/jerr/2021/v20i1117413

[5] Igbokwe, N., & Mba, W. (2019). Assessing the Nature of Risk Management Implementation in Manufacturing Small Medium Enterprises in Nigeria. Archives of Current Research International, 16(4), 1-11. https://doi.org/10.9734/ACRI/2019/v16i430098

[6] Skérè, S., Žvironienė, A., Juzėnas, K., & Petraitienė, S. (2023). Optimization Experiment of Production Processes Using a Dynamic Decision Support Method: A Solution to Complex Problems in Industrial Manufacturing for Small and Medium-Sized Enterprises. Sensors, 23(9), 1-18. https://doi.org/10.3390/s23094498
Disclosure of conflict of interest

No conflict of interest to be disclosed.

References

[1] Alexopoulos, K., Nikolakis, N., & Xanthakis, E. (2022). Digital Transformation of Production Planning and Control in Manufacturing SMEs-The Mold Shop Case. Applied Sciences (Switzerland), 12(21). https://doi.org/10.3390/app122110788

[2] Chikwendu, O. C., Faith Ifeyinwa, O., Chidiebube, I. N., & Utochukwu, O. E. (2020). The Implementation of Kaizen Manufacturing Technique: A Case of a Tissue Manufacturing Company. Ijesc, 10(5), 25938-25949. https://hal.science/hal-03391087/document

[3] Chukwutoo, C. I., & Nkemakonam, C. I. (2018). Total Productive Maintenance (TPM) as a Business Strategy in Manufacturing Small and Medium Enterprises in Nigeria. Advances in Research, 15(5), 1-9. https://doi.org/10.9734/AIR/2018/41330

[4] Igbokwe, N. C., & Godwin, H. C. (2021). Maintenance Performance Evaluation and Downtime Analysis of Manufacturing Equipment in a Food Manufacturing Company. Journal of Engineering Research and Reports, 20(11), 100-107. https://doi.org/10.9734/jerr/2021/v20i1117413

[5] Igbokwe, N., & Mba, W. (2019). Assessing the Nature of Risk Management Implementation in Manufacturing Small Medium Enterprises in Nigeria. Archives of Current Research International, 16(4), 1-11. https://doi.org/10.9734/ACRI/2019/v16i430098

[6] Skérè, S., Žvironienė, A., Juzėnas, K., & Petraitienė, S. (2023). Optimization Experiment of Production Processes Using a Dynamic Decision Support Method: A Solution to Complex Problems in Industrial Manufacturing for Small and Medium-Sized Enterprises. Sensors, 23(9), 1-18. https://doi.org/10.3390/s23094498