Effects of Inventory Management Systems on Performance of Manufacturing Companies in Eldoret Town, Kenya

Table of Contents

1.0 Introduction

2.0 Methodology
2.1 Research Design
2.2 Study Area
2.3 Target Population
2.4 Sample and the Sampling Design
2.4.1 Sample
2.4.2 Sampling Design
2.5 Instruments of Data Collection
2.5.1 Data Collection Procedures
2.5.2 Validity of Research Instrument
2.5.3 Reliability of Research Instrument
2.6 Data Analysis
2.6.1 Descriptive Statistics
2.6.2 Inferential Statistics
2.7 Ethical Considerations

3.0 Results and Discussions
3.1 Descriptive Analysis of Material Requirement Planning (MRP)
3.2 Multiple Regression Analysis
3.3 Hypotheses Testing

4.0 Summary and Conclusions

References

Abstract

Manufacturing companies’ managers are familiar how inventory plays in running of organization operations. In most manufacturing companies, direct materials help in the production process hence affecting company performance. Poor inventory management systems lead most of finished goods to stay in the store before being delivered to its final consumption hence affecting organization performance. It is against this background that the study examined effect of inventory management systems on performance of manufacturing companies in Eldoret Town, Kenya. The specific objectives of the research were: to examine effect of Material Requirement Planning (MRP) on manufacturing companies performance in Eldoret Town, Kenya; to assess effect of Distribution Resource Planning (DRP) System on performance of manufacturing companies in Eldoret Town, Kenya; to determine effect of Vendor Managed Inventory (VMI) system on performance of manufacturing companies in Eldoret Town, Kenya; and to evaluate effect of Just in Time (JIT) System on performance of manufacturing companies in Eldoret Town, Kenya. The study was guided by Adaptive Structuration Theory (AST), Theory of inventory and production and Lean theory. A cross sectional research design was used. The target population comprised of 47 top level management, 45 middle level management (stores, production, supply chain, finance, ICT, material handling department, sales and marketing, transport, total quality management) and 1032 employees working in the departments of the manufacturing companies in Eldoret Town totaling to 1124 respondents. The study selected 383 respondents using simple random sampling technique while both structured and unstructured questionnaire were used as research instrument. Quantitative analysis was used in analyzing and interpretation of data with the help of inferential and descriptive statistics. Manufacturing companies’ staff benefited to know the contributions of inventory management systems and how it enhances effectiveness and efficiency of inventory in manufacturing companies. It also benefited them to know the challenges faced in the adoption of inventory management systems and how to improve the performance of the manufacturing company. The study found out that Material Requirement Planning (MRP) affects manufacturing companies performance in Eldoret Town, Kenya with p=0.048. It was reported that Distribution Resource Planning (DRP) System affects performance of manufacturing companies in Eldoret Town, Kenya with p=0.000. It was found that Vendor Managed Inventory (VMI) system affects performance of manufacturing companies in Eldoret Town, Kenya with p=0.000. In addition the study revealed that Just in Time (JIT) System affects performance of manufacturing companies in Eldoret Town, Kenya with p=0.000. The study concluded that manufacturing companies benefit from adoption of inventory management systems. The study recommends that the management of manufacturing companies need to adopt proper inventory management systems in order to reduce operation costs such as holding costs, ordering costs among others hence increasing company performance. The management of manufacturing companies need to train employees on how to use the inventory management systems in order for them to understand how it operates efficiently and effectively hence increasing firm performance; and the supply chain department needs to provide policies and rules to govern the inventory management systems in order to protect the system.

KEYWORDS: Inventory Management Systems; Performance of Manufacturing Companies; Material Requirement Planning (MRP); Distribution Resource Planning (DRP) System; Vendor Manage Inventory (VMI) System; and Just in Time (JIT) System.

1.0 Introduction

Inventory management systems play a vital role in minimizing costs and maximizing profits, also meeting customer demands by making sure there is enough stock at the right quantity, quality and available at the right time and the right place. To make sure inventory is managed properly, there needs to be adoption of inventory management systems. Inventory management systems refers to control and set of policies that manage the level of inventory, assess the inventory which will be maintained, raw materials will be used for production and the finished goods will be delivered (Jonsson & Mattsson, 2016). Performance of manufacturing companies is where a business is giving high returns in terms of customer loyalty, reduction of operations costs incurred in inventory and increasing of service delivery (Stanton, 2016).

Inventory management systems have played a role in the business operations for many years in the global arena. Inventory management systems play a vital role in enhancing performance in controlling inventory in manufacturing companies. Companies in developed countries such as China, USA have continually tried to maintain in the competitive market through firm operations. It is high time for companies in developing countries such in Africa and India to implement effective inventory management systems in order enhance competitive advantage (Rajeev, 2016). Handling of inventories such as raw materials, work in progress, and finished goods are stored as buffer stock in order to manage running out of goods (Salawati, Tinggi & Kadri, 2015). Too much of handling of stock especially finished goods occupy a lot of space hence increasing inventory costs such as handling costs and also negatively affects business operations (Dimitrios, 2016).

Inventory management systems are important to business since it helps in minimizing costs incurred in the stores and increasing profits in making sure customer demands are met by delivering goods at the right quantity, quality, right time and at the right place (Wood, 2015). According to Lysons (2016), inventory management systems have been implemented in USA companies which have supported in maintaining raw material supplied from suppliers, work in progress and finished goods are stored in increasing service delivery and also reduces costs. They help in maintaining supply and demand where it controls and monitors all orders done by the procurement department in manufacturing company so as to help to maintain an uninterrupted flow of goods coming in and out of the manufacturing section, store or warehouses (Jonsson & Mattsson, 2016).

Inventory management systems curb the challenge of productive inventory management to support an upward trend in sales while keeping the investment cost at the lowest level consistent with adequate customer service (Ellram, 2016). Management of inventory typically represents 45-79% of all business operations and ensures the company has enough raw materials it order to avoid inefficiency of production process hence reducing operation costs (Khan, 2014). According to Gerald (2016), inventory management systems are processes of managing inventory in order to meet customers’ needs by maintaining the lowest possible cost of investment. Reducing these costs through appropriate inventory management systems can enhance any organization’s competitiveness. Many companies in different parts of the world have been revamping the ways in which they manage their inventories. Most are developing long-term partnerships with suppliers and collaborating with them in inventory control (Chan, Qi, Chan, Lau & Ralph, 2016).

Literature shows many companies globally have implemented inventory management systems in running their business operations. Barby (2016) research on role of inventory management techniques on performance of manufacturing companies in USA where he found out that Just in Time (JIT) System has help the manufacturing cycle by helping management in meeting their customers demand without incurring the costs and burdens that come from stocking excess inventory. Features such as effective forecasting, vendor management and data management control make it possible for organizations to achieve a much higher rate of efficiency.

Inventory management systems help in the enhancement of performance and competitiveness of business process (Dimitrios, 2016). As Rajeev (2016) argues, there is need for manufacturing companies in Africa to embrace efficiency inventory management systems as an approach to enhance their competitiveness. Control and holding of inventory is a challenge to many organizations especially manufacturing companies in developing countries in Africa such as Ghana, Kenya, Uganda, Nigeria among others. Stock problems in companies to be solved need effective inventory management systems to reduce costs incurred by inventory. Management is therefore becoming increasingly aware that the overall performance of company’s operation is directly related to inventory situation existing within the company. The real problem therefore has been in the determination of inventory level at which many invested in the inventory will produce a rate of return higher than it would if it had been invested in some other areas of business (Stanton, 2016).

Obemikel and Obiye (2016) did a study on impact of stock control systems on performance of coffee factories in Ghana where they revealed that the coffee factories have implemented stock control systems which have helped them in reducing operational costs, enhancing business efficiency, reduces lead time and decreases inventory hence increasing company performance.

In Kenya, more organizations such as large business enterprises have implemented inventory management systems in achieving firm performance and competitive advantage (Nyabwanga & Ojera, 2012). Nyabwanga and Ojera (2012) did a study on 71 manufacturing firms and 129 business companies in Kisii County where they found out around 70% of the firms and companies selected had adopted inventory management systems in firm operations hence increasing competitive advantage. Irungu and Wanjau (2014) asserted that new upcoming supermarkets are continuing adopting inventory management systems which improves customer service, business efficiency and retail firm performance. This recommends that companies, retail business among others continue to adopt inventory management systems around the world.

Kenya manufacturing firms such as in Eldoret are facing competition from other manufacturing companies where they need to adopt efficient techniques of controlling and assessing the inventory is managed by eliminating waste in the production process, reducing holding costs, ordering costs and many others. Many companies in developing countries like Kenya have adopted inventory management systems in improving their business operations but still they experience challenges in managing of inventory and increasing operation costs. For the companies to survive in the market they need to implement advance stock management systems (Kenya Association of Manufactures, 2016). Therefore, it is upon this background information the study determined how inventory management systems affect performance of manufacturing companies in Eldoret Town, Kenya.

2.0 Methodology

2.1 Research Design

A cross sectional research design was used. The cross sectional research design was adopted since it is suitable in finding out the prevalence of a problem or situation in a study by selecting a cross section of a population. The design is helpful in obtaining an overall of the time of carrying out the study (Ranjit, 2011). The design provided information on effects of inventory management systems on performance of manufacturing companies in Eldoret Town, Kenya.

2.2 Study Area

The study was carried out in Eldoret Town, Uasin Gishui County. The Ministry of Industrialization have enhance improvement of manufacturing industries where there has been an increase of new manufacturing industries being open in Eldoret where there are small and large industries in the area which has improve the economy of the town to increase. The residents of Uasin Gishu County do not need to purchase products from different counties but can access the products in the same town. Manufacturing industries in Eldoret include: Rift Valley Bottlers, Rai Plywoods (K) Ltd, Unga Group Millers, Rivatex Limited, and Eldoret Grains Limited. The companies in Eldoret Town had been experience challenges in managing their inventory hence affecting the performance of the company.

2.3 Target Population

The target population comprised of 47 top level management, 45 middle level management (stores, production, supply chain, finance, ICT, material handling department, sales and marketing, transport, total quality management) and 1032 employees working in the departments of the 5 manufacturing companies in Eldoret Town (Ministry of Industrialization Report 2017, Uasin Gishu County). The target population was 1124 respondents.

Table 1: Target Population

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Source: (Ministry of Industrialization Report 2017, Uasin Gishu County)

2.4 Sample and the Sampling Design

2.4.1 Sample

The sample size of the study was 383 respondents. To calculate the sample size, the research used Nassiuma’s (2000) method as shown below.

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Table 2: Sample Size

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2.4.2 Sampling Design

The respondents were selected through simple random sampling technique. The research selected 383 respondents using simple random sampling technique from a target population of 1124 employees. The technique was used where every person in the group had a chance to be selected. Simple random sampling technique was done through lottery bowl method where respondents are selected in a random manner.

2.5 Instruments of Data Collection

Questionnaire was used as data collection instrument. The instrument was preferred since it takes little time to construct. Also it can be given to a large number of respondents. The questionnaire had both structured and unstructured questions. The questionnaires were administered to the employees working in manufacturing companies within Uasin Gishu County.

2.5.1 Data Collection Procedures

Before collecting the data, the researcher sent a letter to the respondents requesting to be allowed to collect the data. This letter was obtained from the School of Business and Economics, Kisii University. In addition a permit was obtained from NACOSTI. These letters were sent three weeks before the actual day of data collection. It enabled the respondents to be prepared for participation in the research. The investigator carried out the study in the manufacturing industries and hand in the questionnaires to the respondents. Some of the questionnaires were filled on the same day when the researcher visits the study area while others were left to be filled. After a week the researcher then returned to the manufacturing companies to collect the questionnaires that had been left to be filled by the respondents on the first day visit in the manufacturing companies.

2.5.2 Validity of Research Instrument

The validity of the questionnaire was examined using both content and face validity. Content validity refers to the degree to which the questionnaire fully assesses or measures the construct of interest. The content validity was assessed by the experts in assessing the content in the questionnaire is according to the study objectives. Face validity refers to the checking and evaluating the content is related to the structured and unstructured questionnaire questions.

2.5.3 Reliability of Research Instrument

The test-retest study was used to evaluate the reliability of research instrument. The questionnaire was administered to the staff of Timsales Limited. The test-retest used 31 respondents to make sure the reliability was efficient and effective. The study used Cronbach’s Alpha Coefficient to measure the reliability coefficient of the questionnaire. The Conbach’s Alpha Coefficient rule where the value of α>0.7 is considered to be reliable.

Table 3: Reliability Measures

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Source: Kombo and Tromp, (2016).

Table 4: Reliability Analysis

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From the reliability analysis before and after factor analysis all the variables had a Cronbach’s alpha value 0.907 which was above 0.7, therefore all the variables were reliable. From the reliability analysis before and after factor analysis all the variables had a Cronbach’s alpha value 0.771 which was above 0.7, therefore the variables were reliable.

2.6 Data Analysis

Quantitative analysis was used in analyzing, presentation and interpretation of data with the help of inferential and descriptive statistics. Before the data is processed, data preparation was done on the filled questionnaires by editing, coding, entering and cleaning the data. This helped in assessing for the accuracy and completion of the responses. Data was analyzed and presented through descriptive and inferential statistics with the help of SPSS (Version 21) and Ms Excel.

2.6.1 Descriptive Statistics

The descriptive statistics method involves the use of tables, percentages, means, frequencies, graphs and charts to present the collected data. Data was presented in form of percentages, means, frequency, and standard deviations while the content was displayed in form of tables.

2.6.2 Inferential Statistics

Multiple regression analysis was used to show correlation of the variables. In statistics usually a model has assumptions where the multiple regression model assumptions included: there was a linear relationship between independent and dependent variables which were characterized by a straight line and the residuals are normally distributed. Also there was no multicollinearity of data where tolerance value was above 0.2 and the predictors are not highly correlated. In addition values of residuals were independent where the Durbin-Watson was close to 2; and the values of residuals were normally distributed.

Homoscedasticity was another assumption that the variation in the residuals is similar at each point across the model. The spread of the residuals should be fairly constant at each point of the predictor variables. Moreover the plot of the points generally follow the normal (diagonal) line with no strong deviations where the residuals were normally distributed. The dependent and independent variables were normally distributed suggesting multivariate as assumption of regression. The regression model is as follows:

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While β1, β2, β3 and β4 are coefficients of determination and ε is the random error term.

2.7 Ethical Considerations

Ethical considerations included the following: confidentiality, privacy, anonymity, voluntary participation and informed consent. The study treated the respondents with greatest discretion since it was for academic purpose only. Also the data in the questionnaire there was handled with high secrecy as shown in the letter from the institution showing the student was doing research. In addition the anonymity was created by the researcher informing the respondents that it was only for education purpose. Moreover the study selected the respondents randomly to participate in the study. Finally, the researcher visited manufacturing companies before doing the main research in order to be accepted to do the study in those companies.

3.0 Results and Discussions

The study involved descriptive and inferential statistics.

3.1 Descriptive Analysis of Material Requirement Planning (MRP)

The research sought to examine effects of Material Requirement Planning (MRP) on performance of manufacturing companies in Eldoret Town, Kenya.

Table 5: Descriptive Results of Material Requirement Planning (MRP)

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The results indicate that 22.6% of the participants strongly disagreed, 29% disagreed, 13.3% were undecided, 15.1% agreed and 20.1% strongly agreed that use of MRP systems makes sure there is enough inventory to meet production demands hence reducing interruption of production. It had a mean of 2.81, Std=1.33, skweness=-0.28 and kurtosis=-1.33. The data was normally distributed and suitable for regression with skweness and kurtosis values ranging between -2.0 and +2.0.The data was normally distributed and suitable for regression. The standard deviation showed a lower uniformity of data and there was no homogeneity of data.

The findings indicate that 13.6% of respondents strongly disagreed, 22.9% disagreed, 10.8% were undecided, 36.6% agreed and 16.1% agreed that it plays a role in holding of inventory ensures uninterrupted business operations hence increasing efficiency and effectiveness of firm operations. It had a mean of 3.19, Std=1.33, skweness=-0.28 and kurtosis=-1.21. The data was normally distributed and suitable for regression with skweness and kurtosis values ranging between -2.0 and +2.0. The standard deviation showed a lower uniformity of data and there was no homogeneity of data.

According to table 4.6, 12.2% of employees strongly disagreed, 34.1% disagreed, 12.2% were undecided, 26.2% agreed and 15.4% strongly agreed that helps in speeding up production execution hence meeting customer demands. It had a mean of 2.99, Std=1.31, skweness=0.11 and kurtosis=-1.27. The data was normally distributed and suitable for regression with skweness and kurtosis values ranging between -2.0 and +2.0. The standard deviation showed a lower uniformity of data and there was no homogeneity of data.

From the results 27.2% of participants strongly disagreed, 17.6% disagreed, 20.4% were undecided, 30.5% agreed and 4.3% strongly agreed that it enhances capability and speed to providing products that meet customers’ demands hence increasing customer satisfaction. It had a mean of 2.67, Std=1.28, skweness=-0.03 and kurtosis=-1.36. The data was normally distributed and suitable for regression with skweness and kurtosis values ranging between -2.0 and +2.0. The standard deviation showed a lower uniformity of data and there was no homogeneity of data.

The findings indicate 11.8% of the respondents strongly disagreed, 3.6% were undecided, 33.7% agreed and 50.9% strongly agreed that helps in managing lead time hence minimizing ordering costs hence increasing profit volume. It had a mean of 4.12, Std=1.27, skweness=-1.64 and kurtosis=1.59. The data was normally distributed and suitable for regression with skweness and kurtosis values ranging between -2.0 and +2.0. The standard deviation showed a higher uniformity of data and there was homogeneity of data.

Moreover table 4.6 indicates 3.6% of the employees strongly disagreed, 6.1% were undecided, 35.1% agreed and 55.2% strongly agreed that there is enough inventory in order to meet the production demands hence meeting customer demands and production effectiveness of the company. It had a mean of 4.42, Std=0.76, skweness=-1.36 and kurtosis=1.65. The data was normally distributed and suitable for regression with skweness and kurtosis values ranging between -2.0 and +2.0. The standard deviation showed a higher uniformity of data and there was homogeneity of data.

From the results majority of respondents with 88.4% (mean=4.42) were of the opinion that there is enough inventory in order to meet the production demands hence meeting customer demands and production effectiveness of the company.

Sanghal (2015) supported the study where he found out that that long run price enhances too much of inventory to stay in the store hence increasing holding costs. This affects the performance of manufacturing companies. Also the results is agreed by Kwadwo (2016) who revealed that there is relationship between procurement management and profitability of manufacturing firms where it helps in controlling of inventory, improves lead time management, enhancing customer supplier relationship hence enhancing competitive advantage. In addition the findings is concurred by Mogere, Oloko and Okibo (2013) where they revealed that inventory management systems is very important since it helps in controlling of inventory, improves lead time management, enhancing customer supplier relationship hence enhancing competitive advantage.

3.2 Multiple Regression Analysis

The research sought to determine the relationship between inventory management systems and performance of manufacturing companies using multiple liner regression.

Table 6: Multiple Regression Model Summary

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The R-Squared is the proportion of variance in the dependent variable which can be explained by the independent variables. The R-squared in this research was 0.702, which shows that the independent variables [Material Requirement Planning (MRP) System] can explain 70.2% of the change in dependent variable (Performance of manufacturing companies). This shows that the other factors not studied in this research explain 29.8% of the dependent variable (Performance of manufacturing companies).

Table 7: Multiple Regression Model Goodness of Fit

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The results indicated that the p=0.000<0.05 and hence the model can predict how the independent variables [Material Requirement Planning (MRP) System] affect performance of manufacturing companies. Also the F=161.172 was more than the F-critical (2.46) which shows that the model was fit in showing the effect of independent variables on performance of manufacturing companies.

Table 8: Multiple Regression Coefficients

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The regression equation was modeled as follows:

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The regression equation computed was:

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Y (Performance of manufacturing companies) = -0.293 – 0.079 (Material Requirement Planning (MRP) System) + 0.0.184 (Standard Error)

Table 8 indicated that holding all the other independent variables constant, a unit increase in Material Requirement Planning (MRP) System would lead to a -0.079 increase in performance of manufacturing companies.

3.3 Hypotheses Testing

H01. There is no statistically significant effect of Material Requirement Planning (MRP) on performance of manufacturing companies in Eldoret Town, Kenya.

The study findings indicated there was a statistically significant effect of Material Requirement Planning (MRP) on performance of manufacturing companies in Eldoret Town, Kenya (p=0.048<0.05). The study therefore rejected the null hypothesis.

This reveals that adoption of Material Requirement Planning (MRP) in production section helps to monitor on inventory such as raw materials, working in progress, spare parts among others it helps the production to work efficiently without any hindrance.

From the findings is concurred by Roumiantseva and Netessinet (2017) who found out inventory management systems affects financial performance of textile companies especially material requirement planning system where it enhance efficiency flow of materials, increases speed of production and improves execution of information along the production process with a p>0.016.

Also the findings from this hypothesis test supported with that of Owolabi and Ajidagba (2014) who revealed that stock management enhances manufacturing output. The result shows that effective inventory management account for 82.4% in the variation of manufacturing output, given the value of the R[2] (0.824). In addition Kung’u (2016) agreed with the hypothesis test where he found out that there is a significant between stock control practices and profitability was 0.601 at 0.01 significant levels. This implies stock control practices affect profitability of industrial and firms.

4.0 Summary and Conclusions

The findings indicated that Material Requirement Planning (MRP) affects performance of manufacturing companies in Eldoret Town, Kenya since use of MRP systems makes sure there is enough inventory to meet production demands hence reducing interruption of production; it plays a role in holding of inventory ensures uninterrupted business operations hence increasing efficiency and effectiveness of firm operations; helps in speeding up production execution hence meeting customer demands; it enhances capability and speed to providing products that meet customers’ demands hence increasing customer satisfaction; helps in managing lead time hence minimizing ordering costs hence increasing profit volume; and there is enough inventory in order to meet the production demands hence meeting customer demands and production effectiveness of the company.

The research revealed that Material Requirement Planning (MRP) affects performance of manufacturing companies in Eldoret Town, Kenya through Adaptive Structuration Theory (AST) where it evaluates the types of systems used in managing the inventory in the firm and the structures which govern the systems in order to perform effectively and efficiently. Therefore adoption of Material Requirement Planning (MRP) is important in production section since it helps to monitor on inventory such as raw materials, working in progress, spare parts among others it helps the production to work efficiently without any hindrance.

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