A STUDY ON FACTORS INFLUENCING INVENTORY OPTIMIZATION DECISIONS AMONG THE MANUFACTURING COMPANIES

Transcription

1 A STUDY ON FACTORS INFLUENCING INVENTORY OPTIMIZATION DECISIONS AMONG THE MANUFACTURING COMPANIES A.SATHYA 1 N.BHUVANESH KUMAR 2 1 Student of PG Department of Commerce with International Business, NGM College Pollachi 2 Assistant Professor of PG Department of Commerce with International Business, NGM College Pollachi ABSTRACT Inventory management is an effective way to keep track of exactly what products a company has. Well-organized inventory management can help save a business by reducing unnecessary costs, while delivering products and services to customers more quickly and efficiently. The research mainly discuss to find out the factors influencing inventory optimization decisions among the Manufacturing Companies. Sample of 75 respondents were taken into study, and their data were collected. Samples for the purpose of the study are selected systematically. The study makes use of statistical techniques such as T-Test, ANOVA and Correlation in analyzing the data for finding the result. Through this study we concluded that companies can follow economic order quantity for optimum purchase and can maintain safety stock for components in order to avoid stock out conditions and helps in continuous production flow. This will reduce the cost and will increase the profit. If we could properly execute and follow the all the techniques of inventory management, we will be able to enhance the profit with minimum cost. Keywords: Inventory, Management, Companies and Cost INTRODUCTION Inventory management is an important element in project planning and control. Materials represent a major expense in production of any product, so minimizing procurement or purchase costs presents important opportunities for reducing costs. Poor inventory management can also result in large and avoidable costs during operation. First, if materials are purchased early, capital may be tied up and interest charges incurred on the excess inventory of materials. Even worse, materials may deteriorate during storage or be stolen unless special care is taken. Second, delays and extra expenses may be incurred if materials required for particular activities are not available. The control and maintenance of inventory is a problem common to all organizations in any sector of the economy. The problems of inventory do not confine themselves of profit making institutions. The same type of problems is encountered even by social and nonprofit institutions. Inventories are common to agriculture, manufacturers, wholesalers, retailers, hospitals, churches, prisons, zoos, universities, and national, state, and local Governments. Indeed, inventories are also relevant to the family unit in relation to food, clothing, medicines, toiletries, and so forth. On an aggregate national basis, the total investment in inventory represents a sizable portion of the gross national product. STATEMENT OF THE PROBLEM Inventory costs have lot of impact on the profitability of the firm and its success. Inventory management and its optimized decisions are depending on the identification of key success factors and right decisions at right moment. In a dynamic market environment, it is necessary to focus on the decision making and the factors influencing decision making in order to optimize the results of 30 editor@researchscripts.org

2 inventory function. The survey approach can bring a light on the variables and these have lot of biased information. Testing of the factors influence on inventory decisions by using scientific methods can help to improve the reliability of the factors taken as key variables in decision making. Hence, the present research is focused on two dimensions namely identification of Factors influencing inventory optimization among Manufacturing companies through a structured questionnaire and grouping them into two sets as internal variables and external variables. Establishing the relationship between grouped factors and the type of organization structure followed and their influence on inventory optimization decisions, Grouped external factor s sensitivity on the inventory optimization decision. Based on this the study is titled as A study on Inventory Management of Manufacturing Companies in Coimbatore District. OBJECTIVES OF THE STUDY To examine the inventory control techniques being adopted by the selected units. To find out the factors influencing inventory optimization decisions among the Manufacturing Companies. To provide suggestions to improve the inventory optimization discuss with suitable parameters. SCOPE OF THE STUDY Inventory optimization decisions are based on the accuracy of the information, and the reliability of the key variables identification. The present study is focused in identifying the factors influencing the optimization decisions in inventory management. The classification of items as internal and external and measuring the sensitivity of it is tested in the second level of analysis. The primary level of analysis is done by using descriptive and inferential statistics and specially by grouping the items influencing inventory optimization decisions among Manufacturing Companies. The survey technique is used to collect the data on the reliability of the factors identified as the influencing items in the optimization of inventory decisions in Manufacturing Companies. The factors are rerotated and tested under two categories namely internal factors and external factors. Internal factors are more related and based on the organization structure. External factors are more related to external environment conditions. RESEARCH METHODOLOGY The validity of any research depends on the systematic method of collecting the data and analyzing the same in a logical and sequential order. In the present study, an extensive use of both primary and secondary data was made. RESEARCH DESIGN Research design is generally a pure and simplified framework and certain plan for a study that will guide the collection and analysis of data where information needed. The function of the research design is to ensure that the required data is obtained and collected accurately and economically. Research design is basic framework, which provides guideline for the best of research purpose editor@researchscripts.org

3 SAMPLING METHOD The sampling technique involved is Convenient Sampling. The study depends on primary data. A pilot study is conducted to validate the questionnaire and to confirm the feasibility of the study. Based on the pilot study, the questionnaire is modified suitably to elicit response from the sample group. SAMPLING SIZE Sample of 75 respondents were taken into study, and their data were collected. Samples for the purpose of the study are selected systematically. METHOD OF DATA COLLECTION The data for this study are of two types: - Primary data Secondary data PRIMARY DATA Primary data is the data is collected from the respondent for the first time, it is original in nature. For the purpose of collection of primary data, a well structured questionnaire was framed and filled by the respondents. The questionnaire comprises of close ended as well as open ended questions. In close ended questions, checklist questions and multiple choice questions are used. SECONDARY DATA Secondary data are collected from books, magazines, web sites etc, and both open ended & close-ended questions are incorporated in the questionnaire for the collection of data. STATISTICAL TOOLS The following statistical tools are used in the study T- Test Anova Correlation PERIOD OF THE STUDY The period of the study is 5 months (from December 2016 to April 2017). LIMITATIONS OF THE STUDY The primary data are being collected using questionnaire. Hence, this retains its own limitation. The sample size was limited to 75. The study was completed in short period. Lack of time for study is another drawback. REVIEW OF LITERATURE Cynthia Mito Mukopi and Dr. Amuhaya Mike Iravo (2015), An Analysis of the Effects of Inventory Management on the Performance of the Procurement Function of Sugar Manufacturing Companies in the Western Kenya Sugar Belt. The study examined the effect of inventory management on performance of the procurement function of sugar manufacturing companies in the western sugar belt. The sample size is 200 respondents. Descriptive research design, specifically a survey study was employed in carrying out the research. Data was analyzed using SPSS and presented in tables and charts. ANOVA and Chi-square test have been used to analyse the data. The study found 32 editor@researchscripts.org

4 that inventory management affects the performance of the procurement function of sugar manufacturing companies in the western sugar belt. Macharia Ngombo Wilson and Dr. Mike A Iravo (2015), Effects of Information Technology on Performance Effects of Logistics Firms in Nairobi County. The general objective of this study was to establish the effects of Information Technology on the performance of logistics firms in Nairobi County, Kenya. Data was collected from 10 firms in the logistic industry suppliers in Nairobi. Out of 34 logistic firms in Nairobi, the researcher will take 30% of the total population. Using Statistical techniques like percentage method and chi-square test analysis. The study found that the level of information usage among logistics firms in Nairobi County contributed to the performance. Kwame Owusu Kwateng and Kwame Nkrumah (2014), Outbound Logistics Management in Manufacturing Companies in Ghana. The purpose of this study is to assess outbound logistics of a manufacturing company (Guinness Ghana Breweries Limited) using the services of a third party logistics provider (DHL). The results will serve as a basis and initial benchmark of reference for any manufacturing company in their attempt to assess the outbound logistics operations which will improve supply chain performance. ANALYSIS AND S I) T- TEST TABLE 1 T TEST TABLE SHOWING THE DIFFERENCE IN THE MEAN SCORES BETWEEN TECHNOLOGY ADVANTAGE IN COST CONTROL & GENDER Gender N Std. Deviation Std. Error Male Female T Df Sig. (2-tailed) The above table depicts that the P value (0.260) is more than So there is no significant difference in the mean scores of the respondents based on the gender. It is inferred that gender does not influence the level of technology advantage in cost control. TABLE 2 T TEST TABLE SHOWING THE DIFFERENCE IN THE MEAN SCORES BETWEEN TECHNOLOGY ADVANTAGE IN COST CONTROL & TYPE OF THE FAMILY Type of the family N Std. Deviation Std. Error Nuclear Joint T Df Sig. (2-tailed) editor@researchscripts.org

5 The above table depicts that the P value (0.495) is less than So there is a significant difference in the mean scores of the respondents based on the type of the family. It is inferred that type of family its influence the level technology advantage in cost control. II) ANOVA TABLE 3 - ANOVA TABLE SHOWING THE DIFFERENCE IN MEAN SCORES BETWEEN CONSTRAINTS IN TECHNOLOGY ADAPTATION & AGE Age F Sig. Between Groups Within Groups Total The above table shows that the P value (0.830) is greater than So, there is no significant respect to age of the respondents. It is inferred that age of the respondents does not influence the constraint in technology adaptation. TABLE 4 - ANOVA TABLE SHOWING THE DIFFERENCE IN MEAN SCORES BETWEEN CONSTRAINTS IN TECHNOLOGY ADAPTATION & GENDER Gender F Sig. Between Groups Within Groups Total The above table shows that the P value (0.359) is greater than So, there is no significant respect to gender of the respondents. It is inferred that gender of the respondents does not influence the constraint in technology adaptation. TABLE 5 CONSTRAINTS IN TECHNOLOGY ADAPTATION & PLACE OF RESIDENCE Place of Residence Between Groups Within Groups Total editor@researchscripts.org F Sig.

6 The above table shows that the P value (0.965) is greater than So, there is no significant respect to place of residence of the respondents. It is inferred that place of residence of the respondents does not influence the constraint in technology adaptation. TABLE 6 CONSTRAINTS IN TECHNOLOGY ADAPTATION & EDUCATIONAL QUALIFICATION Educational Qualification F Sig. Between Groups Within Groups Total The above table shows that the P value (0.525) is greater than So, there is no significant respect to educational qualification of the respondents. It is inferred that educational qualification of the respondents does not influence the constraint in technology adaptation. TABLE 7 CONSTRAINTS IN TECHNOLOGY ADAPTATION & TYPE OF THE FAMILY Type of the family Between Groups Within Groups Total F Sig. The above table shows that the P value (0.024) is less than So, there is a significant respect to type of family of the respondents. It is inferred that type of family of the respondents its influence the constraint in technology adaptation editor@researchscripts.org

7 TABLE 8 CONSTRAINTS IN TECHNOLOGY ADAPTATION & NATURE OF JOB Nature of job The above table shows that the P value (0.377) is greater than So, there is no significant respect to nature of job of the respondents. It is inferred that nature of job of the respondents does not influence the constraint in technology adaptation. TABLE 9 CONSTRAINTS IN TECHNOLOGY ADAPTATION & MONTHLY INCOME Monthly income Between Groups Within Groups Total The above table shows that the P value (0.194) is greater than So, there is no significant respect to monthly income of the respondents. It is inferred that monthly income of the respondents does not influence the constraint in technology adaptation. TABLE 10 CONSTRAINTS IN TECHNOLOGY ADAPTATION & SIZE OF THE FAMILY Size of the family Between Groups Within Groups Total Between Groups Within Groups Total F F F Sig. Sig. Sig editor@researchscripts.org

8 The above table shows that the P value (0.101) is greater than So, there is no significant respect to size of family of the respondents. It is inferred that size of family of the respondents does not influence the constraint in technology adaptation. TABLE 11 CONSTRAINTS IN TECHNOLOGY ADAPTATION & EARNING MEMBERS IN THE FAMILY Earning members in the family F Sig. Between Groups Within Groups Total The above table shows that the P value (0.651) is greater than So, there is no significant respect to earning members in the family of the respondents. It is inferred that earning members in the family of the respondents does not influence the constraint in technology adaptation. III) CORRELATION ANALYSIS A measure of the strength of linear association between two variables. Correlation will always between -1.0 and If the correlation is positive, we have a positive relationship. If it is negative, the relationship is negative. The formula for Correlation is given below: Correlation(r) = [ NΣXY - (ΣX)(ΣY) / Sqrt ([NΣX 2 - (ΣX) 2 ][NΣY 2 - (ΣY) 2 ])] Where, N = Number of values or elements X = First Score Y = Second Score ΣXY = the product of first and Second Scores ΣX = First Scores ΣY = Second Scores ΣX 2 = square First Scores ΣY 2 = square Second Scores 37 editor@researchscripts.org

9 TABLE 12 CORRELATION RELATIONSHIP BETWEEN INVENTORY MANAGEMENT OF MANUFACTURING COMPANIES Cost Element Type of Technologies Technology advantage in cost control Constraints in technology adaptation Suggestions to optimize inventory costs Correlations **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). Cost Eleme nt Type of Technolo gies Technolo gy advantag e in cost control Constrai nts in technolo gy adaptati on Suggestions to optimize inventory costs Pearson Correlation ** ** Sig. (2-tailed) N Pearson Correlation.329 ** **.271 *.755 ** Sig. (2-tailed) N Pearson Correlation ** **.203 Sig. (2-tailed) N Pearson Correlation and.421 ** Sig. (2-tailed) N Pearson Correlation.585 **.755 ** Sig. (2-tailed) N From the above table is Correlation relationship between Inventory Management of Manufacturing Companies. The Coefficient of Correlation shows that there is a significant exists between cost element with type of technologies. The Coefficient of Correlation shows that there is a significant exists between cost element and suggestion to optimize inventory costs. technologies and cost element. technologies and technology advantage in cost control editor@researchscripts.org

10 technologies and constraints in technology adaptation. technologies and suggestion to optimize inventory costs. The Coefficient of Correlation shows that there is a significant exists between technology advantage in cost control and type of technologies. The Coefficient of Correlation shows that there is a significant exists between constraints in technology adaptation. The Coefficient of Correlation shows that there is a significant exists between constraints in technology adaptation and technology advantage in cost control. The Coefficient of Correlation shows that there is a significant exists between suggestion to optimize inventory control and cost element. The Coefficient of Correlation shows that there is a significant exists between suggestion to optimize inventory control and type of technologies. FINDINGS T-Test The P value (0.260) is more than So there is no significant difference in the mean scores of the respondents based on the gender. It is inferred that gender does not influence the level of technology advantage in cost control. The P value (0.495) is less than So there is a significant difference in the mean scores of the respondents based on the type of the family. It is inferred that type of family its influence the level technology advantage in cost control. Anova The P value (0.830) is greater than So, there is no significant difference in the mean scores of the respondents based on constraint in technology adaptation with respect to age of the respondents. It is inferred that age of the respondents does not influence the constraint in technology adaptation. The P value (0.359) is greater than So, there is no significant difference in the mean scores of the respondents based on constraint in technology adaptation with respect to gender of the respondents. It is inferred that gender of the respondents does not influence the constraint in technology adaptation. The P value (0.965) is greater than So, there is no significant difference in the mean scores of the respondents based on constraint in technology adaptation with respect to place of residence of the respondents. It is inferred that place of residence of the respondents does not influence the constraint in technology adaptation. The P value (0.525) is greater than So, there is no significant difference in the mean scores of the respondents based on constraint in technology adaptation with respect to educational qualification of the respondents. It is inferred that educational qualification of the respondents does not influence the constraint in technology adaptation. The P value (0.024) is less than So, there is a significant difference in the mean scores of the respondents based on constraint in technology adaptation with respect to type of family of the respondents. It is inferred that type of family of the respondents its influence the constraint in technology adaptation editor@researchscripts.org

11 The P value (0.377) is greater than So, there is no significant difference in the mean scores of the respondents based on constraint in technology adaptation with respect to nature of job of the respondents. It is inferred that nature of job of the respondents does not influence the constraint in technology adaptation. The P value (0.194) is greater than So, there is no significant difference in the mean scores of the respondents based on constraint in technology adaptation with respect to monthly income of the respondents. It is inferred that monthly income of the respondents does not influence the constraint in technology adaptation. The P value (0.101) is greater than So, there is no significant difference in the mean scores of the respondents based on constraint in technology adaptation with respect to size of family of the respondents. It is inferred that size of family of the respondents does not influence the constraint in technology adaptation. The P value (0.651) is greater than So, there is no significant difference in the mean scores of the respondents based on constraint in technology adaptation with respect to earning members in the family of the respondents. It is inferred that earning members in the family of the respondents does not influence the constraint in technology adaptation. Correlation The Coefficient of Correlation shows that there is a significant exists between cost element with type of technologies. The Coefficient of Correlation shows that there is a significant exists between cost element and suggestion to optimize inventory costs. technologies and cost element. technologies and technology advantage in cost control. technologies and constraints in technology adaptation. technologies and suggestion to optimize inventory costs. The Coefficient of Correlation shows that there is a significant exists between technology advantage in cost control and type of technologies. The Coefficient of Correlation shows that there is a significant exists between constraints in technology adaptation. The Coefficient of Correlation shows that there is a significant exists between constraints in technology adaptation and technology advantage in cost control. The Coefficient of Correlation shows that there is a significant exists between suggestion to optimize inventory control and cost element. The Coefficient of Correlation shows that there is a significant exists between suggestion to optimize inventory control and type of technologies. SUGGESTION A proper management of the human resources is very essential for the effective function of the inventory management. On the job training is essential for these personnel. The inventory personnel should be fully aware of the purchasing system, quality control and marketing. It will be advantageous if they are sent through these departments. So as to enable them to pick up sufficient knowledge of the functioning of these departments editor@researchscripts.org

12 Scientific management of inventory would be possible only through training of the executives at all levels. The training courses should enable the candidates to acquire general commercial and specialist knowledge complementary to the practical skills and to learn to apply the skills needed in the purchasing and inventory function efficiently. CONCLUSION Inventory Analysis and Control has become inevitable for a manufacturing industry. In order to refrain from having an inventory go dead it is of utmost importance to stay abreast with the number and condition of items in that particular inventory. Inventory management has to keep accurate records of goods. It is important for keeping cost down. The better inventory management will surely help in solving problems the company would be facing with respect to inventory and will help in reducing huge investment or blocking of money in inventory. Through this study we concluded that companies can follow economic order quantity for optimum purchase and can maintain safety stock for components in order to avoid stock out conditions and helps in continuous production flow. This will reduce the cost and will increase the profit. If we could properly execute and follow the all the techniques of inventory management, we will be able to enhance the profit with minimum cost. REFERENCE Cynthia Mito Mukopi and Dr. Amuhaya Mike Iravo (2015), An Analysis of the Effects of Inventory Management on the Performance of the Procurement Function of Sugar Manufacturing Companies in the Western Kenya Sugar Belt, International Journal of Scientific and Research Publications, Volume 5, Issue 5, pp Macharia Ngombo Wilson and Dr. Mike A Iravo (2015), Effects of Information Technology on Performance Effects of Logistics Firms in Nairobi County, International Journal of Scientific and Research Publications, Volume 5, Issue 4, pp Kwame Owusu Kwateng and Kwame Nkrumah (2014), Outbound Logistics Management in Manufacturing Companies in Ghana, Review of Business and Finance Studies, Volume 5, Number 1, pp Thogori M. and Dr. Jane Gathenya (2014), Role of Inventory Management on Customer Satisfaction among the Manufacturing Firms in Kenya: A Case Study of Delmonte Kenya, International Journal of Academic Research in Business and Social Sciences, Vol. 4, No. 1, pp Dr. Angel Raphella. S, Mr. Gomathi Nathan. S and Ms. Chitra. G (2014), Inventory Management- A Case Study, International Journal of Emerging Research in Management &Technology, Volume-3, Issue-3, pp Elema Boru Godana and Dr. Karanja Ngugi (2014), Determinants of Effective Inventory Management at Kenol Kobil Limited, European Journal of Business Management, Vol.1, Issue 11, pp Balakrishnan V. Selvaraj (2014), Inventory management of cement industries in Ariyalur district - a study, Asian Journal of Management Research, Volume 4 Issue 4, pp editor@researchscripts.org