THE IMPACT OF FORECASTING ON ORGANIZATIONAL EFFICIENCY IN MANUFACTURING FIRMS IN PORT HARCOURT

THE IMPACT OF FORECASTING ON ORGANIZATIONAL EFFICIENCY IN MANUFACTURING FIRMS IN PORT HARCOURT

ABSTRACT: This study presents an empirical investigation on the impact of forecasting and organizational efficiency of manufacturing firms in Port Harcourt, our curiosity led to the research problem of this study; on how forecasting affects the organizational efficiency of manufacturing firms in Port Harcourt. Using the quasi-experimental research design, quantitative data were collected through a 5-point Likert type scale. A total of 100 copies of questionnaires were completed by managers and supervisors of the ten selected manufacturing firms in Port Harcourt. We adopted the spearman’s rank correlation coefficient to measure the strength of the relationship between forecasting and organizational efficiency of manufacturing firms in Port Harcourt. The entire hypothesis that was stated in the null form was rejected and the alternate hypothesis was all accepted. Our findings from quantitative data analysis support the argument that forecasting affects organizational efficiency through dimensions such as time series analysis, causal method, and Delphi method. We, therefore, concluded that forecasting and its dimensions play a significant role in influencing the organizational efficiency of manufacturing firms in Port Harcourt and recommend among others that effort should be made at the organization to ensure accuracy of the forecast as possible since inaccurate forecast can lead to misleading results which might affect efficiency.

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CHAPTER ONE: INTRODUCTION

1.1 Background of the Study

Organizations desire enhanced performance because they are an efficiency-seeking entity (Pfeffer, 1982). This is because better performance translates into increased output, enhanced patronage, and growth in sales and growth in profit. Consequent on the attendance benefit that streams from enhanced performance, lot of scholarly works have been undertaken to make the efficiency of the organization better: innovation (Tabatabae, 2000), administrative approach (Henri Fayol, 1930), behavioral approach (Mayo, 1953), understanding personality trait and type (McGregor, 1950), motivation (Maslow, 1960; Herzberg 1987; Skinner 1971; Vroom 1964; Adams 1960) work-life-balance of employee (Armstrong, 2001; Bartoli & Blatrix, 2015), information compliance and integration (Block & Hire, 1978), employees voice and participation (Blundell et al., 1995). Yet other scholars like Leonard and Milton (1963), Bunn and Taylor (2001), and Chann (2000) suggested increased use of automation, and significant organizational changes in response to new manufacturing (Corradi & Swanson, 2006). Moreover, Michael and Mogga (2018) defined organization efficiency as an organizational ability to meet demand using the smallest possible amount of resources.

The manufacturing sector is a critical growth driver for any economy. The sector is regarded as a basis for determining a nation’s economic efficiency (Amakom, 2012). However, despite the country’s wealth and relative improvement in GDP growth rate, poverty is widespread. This is a result of gross underperformance of the real sector, particularly manufacturing.

Every organization or manager seeks to know the nature of future events to plan and take necessary action before time. The effectiveness of this plan depends upon the level of accuracy with which future events are known. Nevertheless, every organization plans whether or not future events are exactly known. That implies that the organization does make forecast and calculate future events to the best of their Abilities, Judgment, and Experience (Armstrong, 1985). Virtually all management decisions depend on forecasts. Managers make a forecast on sales, working capital needs, the size of the workforce, inventory levels, the scheduling of production runs, the location of facilities, the amount of advertising and sales promotion, the need to change prices, etc. It is equally required in all situations (Armstrong, 1985; Makridakis & Hibon, 2000).

Forecasting is required in many situations because it is an important aid in effective and efficient planning. Some things are easier to forecast than others. Good forecasts are major inputs in all aspects of manufacturing operations decisions (Fildes & Makridakis, 1995). David (2013) assert that forecasting is the number one area of applications in corporations. DeSanctis (1984) positioned forecasting as the driving force behind all planning activities in firms. Accurate forecasts help companies prepare for short and long term changes in market conditions and improve operating performance (Gardner, 1990). When the accuracy of forecasts declines, decisions based on the forecasts lead to operational miss-steps (Aviv, 2003; Gardner, 1990).

The adoption of structured forecasting techniques has been studied by several authors (Sanders & Manrodt, 2003; Sanders & Ritzman, 2001). Quantitative (such as exponential smoothing or regression) and/or qualitative approaches (such as the Delphi method, panel of experts, etc.), can be used to elaborate sales forecasts. Despite the plethora of studies on this issue, the debate is still open on whether the adoption of structured forecasting techniques is always beneficial in improving forecast accuracy and effectiveness of the organization. In particular, during the last decade, several authors have challenged the assumption that: ‘the greater the adoption of complex forecasting techniques – the better the forecast accuracy’. For instance, many authors attempted to demonstrate that the efficacy of forecasting techniques in improving forecast accuracy depends on the fit between the type of technique adopted and the context (Makridakis et al., 2000; Sanders and Manrodt, 2003).

Moreover, several researchers suggested that forecasting technique adoption is not enough to guarantee good forecast accuracy (Armstrong, 1987; Mentzer and Bienstock, 1998; Moon et al., 2003). Forecasting management is a complex issue, that includes decisions on information-gathering processes and tools (e.g., what information should be collected, how it should be collected), organizational approaches to be adopted (e.g., who should be in charge of forecasting, and what roles should be designed), inter-functional and intercompany collaboration for developing a shared forecast (e.g., using different sources of information within the company or supply network, joint elaboration of forecasts, etc.), and measurement of accuracy (e.g., using the proper metric and defining proper incentive mechanisms). Adopting a structured forecasting technique could lead to no improvements in forecast accuracy if information gathering or forecasting organizational approaches are not properly designed and managed.

Several types of research have been carried out on the impact of forecasting on organizational efficiency thus, Michael and Mogga (2018) conducted a study on the impact of effective forecasting on business growth. The result of their findings showed that there is a strong correlation between forecasting and business growth in a given market and most business forecasting is based on the length of experience and subjective manager’s judgments. Chindia (2016), carried out a study on forecasting techniques and the accuracy of performance forecasting. The result of the study showed that there was evidence that the accuracy of performance forecasting (APF) is influenced by each of the forecasting methods in different ways. Needorn (2019) did a study on forecasting and organizational performance. The result of his study revealed that a significant relationship exists between all measures of forecasting and dimension of organizational performance. From the studies above, it appears that the impact of forecasting on organizational efficiency was not examined by the authors mentioned above. This has created a point of departure that needs to be filled. This is what has informed the researcher to embark on an empirical study to look at the association between the impact of forecasting on the organizational efficiency of manufacturing firms in Port Harcourt.

1.2 Statement of Problem

The business environments in most developing countries have become very complex, with increased difficulty in predicting the activities of firms in the market. Notwithstanding, firms need to have a good knowledge of the present and future outlooks of their respective environments to be able to cope with the growing challenges. To do this, the old method of relying on subjective and intuitive managerial and board views is no longer desirable. Instead, there is now a stronger need to apply scientific and systematic approaches in major areas of decision making.

The need to have a good knowledge of the future revenue of a firm is the greatest challenge in this respect. This is so because every other aspect of the firm depends almost entirely on the ability of the marketing management to evolve sound strategies capable of increasing the revenue and facilitating efficient utilization of the scarce resources of the firm. In a harsh business environment laced with stiff competition and unstable policies, a lot of scientific approaches are required to make realizable future performance projections as well as the efficiency of operations.

This study, therefore, is directed to show the relationship that exists on the impact of forecasting on organizational efficiency with the view of closing the existing knowledge gap which seemed to be obvious in the management literature and proffering Lasting solution to the adverse effect of organizational efficiency to manufacturing firms under view.

1.3 Operational Framework

Operational Framework on forecasting and organizational efficiency.

Figure 1.1 [missing]

Source: Conceptualized by the Researcher (2019)

The figure above is an operational framework of forecasting and organizational efficiency of manufacturing firms in Port Harcourt. Forecasting here is the independent variable while organizational efficiency is the dependent variable. The dimensions of forecasting in this study are time series analysis, casual method, and Delphi method while that of organizational efficiency was measure as market share and profitability. This was drawn from the work of Needorn (2019) and Chindia (2016).

1.4     Aim and Objective of the Study

The study aims to examine the relationship between the impact of forecasting and organizational efficiency of manufacturing firms in Port Harcourt. The specific objectives of the study include:

1. To ascertain the relationship between time series analysis and market share of manufacturing firms in Port Harcourt.
2. To ascertain the relationship between time series analysis and profitability of manufacturing firms in Port Harcourt.
3. To identify the relationship between causal method and market share of manufacturing firms in Port Harcourt.
4. To identify the relationship between the causal method and profitability of manufacturing firms in Port Harcourt.
5. To identify the relationship between the Delphi method and market share of manufacturing firms in Port Harcourt.
6. To identify the relationship between the Delphi method and the profitability of manufacturing firms in Port Harcourt.

1.5     Research Questions

The following research questions are raised to help shape the direction of this study:

1. To what extent does time series analysis affect the market share of manufacturing firms in Port Harcourt?
2. To what extent does time series affect the profitability of manufacturing firms in Port Harcourt?
3. How does the causal method affect the market share of manufacturing firms in Port Harcourt?
4. How does the causal method affect the profitability of manufacturing firms in Port Harcourt?
5. To what extent does the Delphi method affect the market share of manufacturing firms in Port Harcourt?
6. To what extent does the Delphi method affect the profitability of manufacturing firms in Port Harcourt?

1.6     Research Hypotheses

The following null hypotheses have been formulated to help guide the study:

H₀₁: There is no significant relationship between time series analysis and market share of manufacturing firms in Port Harcourt.

H₀₂: There is no significant relationship between time series analysis and profitability of manufacturing firms in Port Harcourt.

H₀₃: There is no significant relationship between causal method and market share of manufacturing firms in Port Harcourt.

H₀₄: There is no significant relationship between the causal method and the profitability of manufacturing firms in Port Harcourt.

H₀₅: There is no significant relationship between the Delphi method and the market share of manufacturing firms in Port Harcourt.

H₀₆: There is no significant relationship between the Delphi method and the profitability of manufacturing firms in Port Harcourt.

1.7     Significance of the Study

The study is designed to examine the relationship between the impact of forecasting and organizational efficiency of manufacturing firms in Port Harcourt. Thus, the study will be significant to the following groups and personnel:

Organizations: First, it will provide a deeper understanding of how important forecasting is to a firm operation and if adopted by organizations it can influence operational efficiency. The study will reveal to organizations how the use of forecasting can place them in a better position in a competitive environment, promotes the effective functioning of the organization, and in improving their overall efficiency.

Management Practitioners: To the management practitioners, the findings of this study are expected to provide answers to the fundamental question of why some firms collapse or are bought by other firms and to help the organization to structure better forecasting technique to increase the performance and efficiency of their firms.

Management: Management of manufacturing firms will learn how best to assess and apply forecasting techniques, that is, the study becomes necessary because it is going to point out ways of how management will maintain and predict the future of its organization.

Human Resource Teams: Human resource teams will use the findings of this study to formulate viable policy documents that will effectively boost employee, customers, stakeholder’s confidence and organizational performance as well as operational effectiveness and efficiency.

Policy Maker: The study will be of great benefit to policymakers as it will re-emphasize the importance of forecasting and the need to make appropriate policies that will help improve the efficiency of the manufacturing sector in general.

Researchers and Students: Researchers and students who may wish to carry out similar studies will benefit using this study as it offers a reference for future research that might examine the relationship between forecasting and organizational efficiency or related topics.

1.8    Scope of the Study

The scope considered in this study includes content scope, geographical scope, and unit of analysis.

Content Scope: The elements covered in the independent variable forecasting having its dimension as time series analysis, causal method, and Delphi method, while that of the dependent variable organizational efficiency has its measures as market share and profitability.

Geographical Scope: The study is centered on manufacturing firms in Port       Harcourt.

Study Unit Scope: The unit of analysis for this study is at the micro-level. Due to the type of primary data needed for this research, respondents will comprise of employees and managers of the manufacturing firms in Port Harcourt.

1.9     Limitations of the Study

Every research work comes with several limitations. To achieve the stated objectives of the study, the following limitations are inevitable:

This study is limited to selected manufacturing firms in Port Harcourt. This is because not all manufacturing firms in Port Harcourt can be covered given the short duration of the study.

The study is also limited to primary data which involves the design and administration of the questionnaire.

The time required to carry out this work is inadequate. Since this study is one of the many courses being offered by the researcher, the researcher is constrained by time to carry out in-depth research on the topic.

Lastly, this study is limited to manufacturing firms. This excludes other sectors such as the hospitality, banking sector, health sector, insurance industry, and service sector.

However, these limitations will not in any way invalidate the results of the study.

1.10 Definition of Terms

Causal Method: attempt to predict outcomes based on changes in factors that are known or believed to impact those outcomes.

Delphi Method: is a forecasting technique that cannot describe in detail the activity of forecasting and, generally, one or more persons are involved in preparing the forecasts.

Forecasting: attempt to predict the future by using quantitative and qualitative means to predict the future.

Manufacturer: large-scale users of raw material or parts that assemble or convert to finished goods for distribution to a retailer.

Market Share: the actual sales (either in quantity sold or expressed in monetary terms) for a product in a given period and a given geographical area.

Organizational Efficiency: the ability to implement its plans using the smallest possible expenditure of resources.

profitability: the degree to which a business or activity yields profit or financial gain.

Time Series Analysis: is a sequential set of data points, measured typically over successive times.

1.11     Organization of the study

The study is structured into five chapters as follows Chapter one covers the overview of the corporate governance and firm performance, the statement of the problems, Research questions, and hypothesis, significance of the study, limitations of the work, and operational definition of terms. Chapter two gives reviews of related literature of some of the work of scholars, practitioners, and others who have contributed to the study, it, therefore, creates a link to this current study. Chapter three describes the research methodology used in the description of the population, research design, methods of data collection, and sampling techniques. Chapter four shows the presentation and analysis of data. Chapter five provides the discussion, conclusion, and recommendation of research findings.