Computer Application and Inventory In A Supermarket

Abstract

The objectives of SCM are to achieve the desired customer service level in the targeted market segment and to optimize the total cost of the supply chain investment. The general objective of the study was to investigate the influence of information and communication technology tools on inventory management. In this investigation, the focus is on computers, but it is hard to talk about computers within the context of inventory management without looking at the other information and communication technologies that make computers effective managers of inventory. Therefore, the work looks at ICT in general. Findings show that computers, as supported by other ICT, are crucial in the management of inventory in supermarkets because it helps to bridge the supply gap and, in essence, helps the supermarket to attain a competitive edge. This was secondary research, and the direction for further studies is that primary data should be gathered to validate the findings in this research.

Keywords: inventory, management, supermarket, computer, ICT

Introduction

Supply Chain Management (SCM) has become a major focus point for many organizations. The concept of SCM has therefore gained prominence in recent years as a way of gaining competitive advantage in the market (Kurien & Qureshi, 2011). On a basic level, the objectives of SCM are to achieve the desired customer service level in the targeted market segment and to optimize the total cost of the supply chain investment. In order to achieve the optimal level of service efficiency and ensure cost minimization in SCM, there is a need to eliminate unnecessary activities. Information and communication technologies (ICT) offer the promise of fundamentally changing the lives of much of the world’s population. According to Dobler and Starling (2006), inventory is the total amount of goods and/or materials contained in a store or factory at any given time. The term inventory is used to indicate raw materials, work in progress (this is input that has entered the production process but not yet fully processed) and finished goods still held up in the industry but not yet released into the market.

According to Thummalapalli (2010), inventory management is the means by which material of the correct quality and quantity is made available as required with due regard to the economy in storage and ordering costs, purchasing, price, and working capital. Inventory management is the function of planning, controlling, and maintaining the right quantity of materials using the minimum level of resources. Inventory management aims at efficient purchasing, storage, and use of materials. In business, the general definition of ICT is the application of technology to coordinate various players where knowledge is created and shared seamlessly, obviating or reducing transactional costs (Carugati & Rossignoli, 2011). Lindgren (2011) noted that the utilisation of ICT technologies can help a firm to achieve competitive advantage due to the high speed of development, the ability to visualize business performance and the reduction in cost of doing business. In its various forms, ICT affects many of the processes of business and government, how individuals live, work and interact, and the quality of the natural and built environment.

Tomar (2009) observed that information technology is the most effective tool in the decision-making process in operations management. Organizations can use ICT solutions in the management of supplier networks, facilitating traceability and managing distribution networks. Nowadays, competition is no longer company to company but rather supply chain to supply chain (Christopher, 2011). Uncertainty in supply is a major reason why most supply chain organizations keep inventories.Inventory management is necessary at different locations within an organization or within multiple locations of a supply chain to protect the production from running out of materials or goods.

Inventory management can be defined as the "management of materials in motion and at rest" (Coyle, 2003). The following activities fall within the range of inventory management: control of lead times; carrying costs of inventory; asset management; inventory forecasting; inventory valuation; inventory visibility; future inventory price forecasting; physical inventory; available physical space for inventory; quality management; replenishment; returns and defective goods; and demand forecasting (Reid & Sanders, 2007).

The information and communications technology (ICT) function is responsible for designing, implementing, and maintaining many of the controls over an organization’s business processes. IT has a critical role in collecting, processing, and storing data that is summarized and reported in financial statements (Cannon & Crowe, 2004). Use of telecommunication technologies enables suppliers, customers, manufacturers, retailers, employees, and managers in the supply chain to communicate effectively, enhancing the reduction of lead time and paperwork and, at the same time, improving customer-supplier relationships. Business organizations are looking forward to reducing their costs and lead time in order to improve service levels (Humphrey, 2001). To achieve the above and create value in the supply chain, communication tools like the internet and web sites are used (Tim, 2007). For companies to perform well in terms of cost management and offer high customer service in supply chain management, they need to incorporate top-line information technologies. According to Li (2001), companies that do not use information and communication systems face challenges in handling costs, offering superior customer service, and inventory management. Li (2001) further identified information technology tools like electronic data interchange, enterprise resource planning, the internet, and extranet among many tools. ICT and inventory management have drawn a lot of attention on the global stage. According to Lancioni (2003), an American distributor (Do It Best) reported a savings of US $4.5 million after instituting an order processing and purchasing system using ICT. Equally, according to NAPA Auto Parts (a US leading auto parts supplier), after adopting internet ordering processing, they captured a big share of the market. Manufacturers embrace the benefits brought by ICT, and thus there is great improvement in supply chain agility, higher efficiency, and timely product delivery to customers (Fasanghari, 2008).

According to Sweeney (2005), inventory management is a key competitive advantage tool and outlines the role of information and communication technology as an enabler in the value chain process. Ashok (2013) carried out a study on the relationship between inventory management and profitability: An Empirical Analysis of Indian Cement Companies The dependent variable, gross operating profit, is used as a measure of profitability and the relationship between inventory management and profitability is investigated for a sample of five top Indian cement companies over a period of ten years from 2001–2010. This study employed regression analysis to determine the impact of the inventory conversion period over gross operating profit, taking current ratio, size of the firm, and financial debt ratio as control variables.

Ashok (2013) found that there is a significant negative linear relationship between inventory conversion period and profitability. The study found that the inventory conversion period has an inverse relationship with firms’ profitability, i.e., when the ICP days increase, the profitability of firms decreases and vice versa. It was found that the firms' profitability as measured by GOP has a negative relationship with their financial debt ratio. This implies that profitability increases with a decrease in financial debt ratio. Furthermore, in this study, the relationship between firm size and GOP was positive, which indicates that profitability increases with an increase in firm size. The relationship between the current ratio and the GOP was negative.

Regionally, several studies have also been conducted with regard to ICT and inventory management in firms. A study done by Gono, Harindranath, and Ozcan (20015) on the challenges of ICT adoption by South African SME’s where quantitative and qualitative research techniques were used on 130 firms surveyed, found that supply chain drives growth by using ICT. Bob Morgan Enterprises in South Africa use ICT. ICT is playing a big role in even small and medium enterprises in Johannesburg (Modimogale & Kroeze, 2011).

Mazanai (2012) studied the impact of a just-in-time (JIT) inventory system on efficiency, quality, and flexibility among the manufacturing sector and small and medium enterprises (SMEs) in South Africa. Self-administered questionnaires were distributed to a sample of manufacturing sector SMEs in the food, wood, telecommunication, metals, non-metals, and other industries. The study revealed that the majority of SMEs in the manufacturing sector were not applying the JIT inventory management principles. It was also revealed that there are obstacles impeding the implementation of JIT principles in SMEs in the manufacturing sector.These challenges include a lack of reliable supplier networks, a lack of capital, and a lack of knowledge of immediate financial gains, among others.

Research Problem

In the dynamically competitive environment, many logistics companies have adopted ICT in their supply chain trends to improve inventory management. Effective integration of ICT with inventory functions plays a major influence in supporting inventory management in organizations (Nelson, 2009). The use of ICT to automate an organization inventory processes optimize efficiencies and improve access to information across every aspect of an organization. According to Govindaraian (2007), many organizations fear embracing ICT due to the heavy financial implications involved. While each organization is distinct, with its own set of purchasing, inventory, and order fulfillment, tracking systems, and pick-and-pack procedures, the role of ICT in streamlining the inventory function within each organization cannot be overstated.

Objectives

The general objective of this research is to investigate the influence of information and communication technology (with a special focus on computer) tools on inventory management in supermarkets.

Theoretical Review

A theory includes a set of basic assumptions and axioms as the foundation, and the body of the theory is composed of logically interrelated, empirically verifiable prepositions (Camp, 2001). Different theories have been employed to help bring clarity to the study of the influence of information and communication technology on inventory management. This study will be based on the following theories: theory of constraints; adaptive structural theory; dynamic capabilities theory; scientific management approach; and concepts of inventory management, information systems, and technology.

The Theory of Constraints

Goldratt (1984) came up with the theory of constraints, which states that an organization is a system and every system has at least one constraint limiting it from attaining its goal. According to Goldratt, these constraints must be identified and corrective measures must be taken for improved performance. A system can be evaluated through inventory, throughput, and operating expenses. Throughput is the rate at which a firm generates revenue through sales; inventory is all the investments of a firm in terms of purchased stocks (raw materials and assemblies); and operating expenses refer to all the money spent to change inventory into throughput. A theorist defines a constraint as any barrier to a firm’s achieving its goals. Goldratt (1984) identified internal constraints, which exist when a system cannot produce enough for the market, and external constraints, which exist when a system produces more than the market needs. In order to improve the organizational output, throughput must be increased while inventory and operating expenses must be reduced. This study is concerned with inventory as a constraint where parameters must be put in place to ensure optimal quantities are held, leading to efficiency. In inventory management, there are constraints like uncertain demand, long lead times, and high production costs (Gunus and Guneri, 2007). In this research, it is believed that inventory management faces challenges like high inventory costs, untrained personnel, inaccurate data, and demand variability. This theory is therefore linked to the vendor managed inventory system in that the achievement of delivery on time is a standard procurement objective. If goods and materials arrive late or work is not completed at the right time, sales may be lost, production halted, and damages may be invoked by dissatisfied customers.

Theorem of Adaptive Structural Theory

The proponent of this theory was Anthony Giddens in 1984, where he tried to reconcile social systems and the micro perspective of organization structures. Desanctis and Poole (1994) made use of it to propose an Adaptive Structuration Theory (AST). This theory explains the interaction between advancing information technology, social structures, and human interaction. This theory goes further to describe the interaction between rules and resources provided by information technology as the basis for human activities.

As it examines the change from various perspectives, AST is a viable method for studying the effects of using IT in inventory management. Adaptive structuration theory, according to Ramakrishna (2005), provides specific advances in information technologies that enable organizations to cope with changes in business alignment, resulting in effective resource management. This study will use AST theory to investigate how the complexity inventory management is influenced by information and communication technology (Ramakrishna, 2005).

This theory could help in evaluating the use and penetration of advancements in ICT technologies in manufacturing entities. The theory posits that lowering inventory levels would give organizations a competitive advantage due to the production of quality products at lower prices and the ability to respond faster to customer needs. Through the distribution resource planning system, accurate demand and sales forecasts would help a firm out of stock-out situations and allow a business firm to provide a high level of customer service.

Conceptual framework

Computer and Inventory Management

Inventory management procedures streamline the number of levels in a system and can help shorten the distance/gap between the source and the end user and thus improve efficiencies by controlling stock levels and stock redundancies. Information and communication technologies (ICT) are one of the most important enablers of effective supply chain management. Jack and colleagues (2006).The availability of information and the methods for analyzing this information to produce meaningful results are driving a lot of interest in supply chain management.As electronic commerce grows in importance, new opportunities emerge, and widespread internet use increases interest in information technologies (Haag & Stephen, 2010).

ICT tools are a source of competitive power for many companies. Information technologies have become increasingly important in many organizations, particularly in service industries such as large retailers, transportation companies such as DHL, and airline companies (Lysons & Farrington, 2006).In supply chain management, time and opportunities to get information on time are very important. Accurate and timely information will enable the organization to increase service levels and, as a result, decrease costs and lead times (Bottani, 2008). In forecasting, one has to determine what has to be forecast, that is the items' quantity levels and the period in question, followed by data availability and the forecast model to be used. Examples of forecast models include the Delphi method, market research and executive opinion (Ried 2007). According to Lysons (2006), inventories pervade the business world and maintaining inventories is essential for any company that deals with physical products, including retailers, wholesalers, and manufacturers. The total value of all inventory—including finished goods, partially finished goods, and raw materials—in the United States is more than a trillion dollars, which is more than $4,000 for every man, woman, and child in that country. The carrying costs of the inventory are also very high, perhaps a quarter of the value of the inventory. Therefore, the costs incurred for the storage of inventory in the United States may run into hundreds of billions of dollars annually.

In today's business environment, many smaller businesses have come to rely on computerized inventory management systems. Given such developments, it is little wonder that business experts commonly cite inventory management as a vital element that can spell the difference between success and failure in today's keenly competitive business world. Good inventory management is responsible for the availability of goods, and this enhances smooth operations as materials required are present at the right quantities, quality, and at the right time in order to deliver a specific level of service. According to Goor and Weijera (1998), inventories are responsible for about one third of the working capital. Inventory costs also represent a significant component of total logistics costs (Coyle 2003). The above costs can be minimized by using information and communication technologies such as Enterprise Resource Planning (ERP). Working capital invested in stocks/inventory could be a very useful resource when it could be used otherwise (Fawcett 2007). Happenings like inventories catching fire, being stolen or damaged pose a great risk, thus influencing production process stoppage and order delivery delays (Visser and Goor 2004). When handling supply chain challenges (Johnson & Pyke, 2001), inventory costs are some of the easiest to identify and reduce when handling supply chain challenges (Johnson & Pyke, 2001).

VMI stands for Vendor Managed Inventory.

In other words, vendor managed inventory (VMI) is where the manufacturer is given responsibility for manufacturing and controlling inventory levels at the retailer’s distribution center and, in some instances, at the retail store level as well (Baily et al., 2008). Irungu & Wanjau (2011) explain that VMI is a process that falls under the "push" stock management processes. This is a process that can be triggered by a firm’s interpretation of an expected demand in inventory, and supply is scheduled to meet this demand. A well designed and developed approach to VMI can lead not only to reductions in inventory levels in the supply chain but also to secondary savings arising from the simplification of systems and procedures (Rushton, Croucher, & Baker, 2011). This is because there is potential for great improvement in the operational performance of firms. This is due to the elimination of delays in both information and material flow for the firms. The achievement of delivery on time is a standard procurement objective. If goods and materials arrive late or work is not completed at the right time, sales may be lost, production halted, and damages may be invoked by dissatisfied customers. Failure to achieve supply on time may slow down the cash to cash cycle, thus reducing the organization’s efficiency or profitability (Baily et al., 2008). VMI provides the opportunity to develop a much closer and more binding relationship between the retailers and the manufacturers, as well as give a much better view of the real demand. The supplier takes the responsibility for operational management of the inventory within a mutually agreed framework of performance targets, which are constantly monitored and updated to create an environment of continuous improvement (Baily et al., 2008). Users receive improved service levels and cash flows, and vendors enjoy better visibility of changing demand and greater customer loyalty (Emmett & Granville, 2007). Reduced administrative costs due to the elimination of the need to monitor levels, paper to computer entries, and reduced reordering costs (Farrington & Lysons, 2006). This can lead to significant reductions in inventory held right through the supply chain (Rushton, Croucher, & Baker, 2011).

The vendor is able to schedule deliveries efficiently, as it has better visibility of the client’s requirements and it can incorporate these requirements at an early stage into its product schedules (Rushton, Croucher, & Baker, 2011). VMI is likely to smooth demand. Companies that can react promptly and accurately to the needs of their customers are more likely to attract orders than those that cannot. If a company is seeking the competitive advantage of becoming better able to respond to customer needs as they arise, then it follows that the company will require a greater degree of responsiveness from its own suppliers (Baily, Farmer, Barry, Jessop, & David, 2008). VMI information improves forecasts of customers’ requirements, thereby enabling firms to plan production to meet customer demand (Farrington & Lysons, 2006). It enhances operational flexibility. It enables production times and quantities to be adjusted to suit the suppliers (Farrington & Lysons, 2006). While VMI has been voted best by retail managers, it has its shortcomings associated with trust, turnover of suppliers and small-scale suppliers who lack financial capacity to implement the VMI system sustainably (Irungu & Wanjau, 2011). They further argue that sometimes this interferes with customer satisfaction as some goods on VMI become one-offs due to the high turnover of suppliers because some new suppliers are yet to develop credibility in their respective areas of supply. The VMI's effectiveness as a system is affected by inventory flow, the quality of ICT, and the quality of information and sharing but is not affected by the quality of relationships. Most of the empirical studies addressing the issue of VMI have focused on manufacturing firms and retailers (Vigtil, 2007; Kauremaa, Smares, & Holmstrom, 2009). Irungu & Wanjau (2011) argued that vendor managed inventory systems could be used to gain competitive advantage by leveraging on inventory supplier reliability and strong buyer/supplier relationships to grow revenue and reduce risk. Their findings suggest that vendor managed inventory has been effective in retail supermarkets by improving stock management, cash flow, and risk management. Irungu & Wanjau (2011) stress that vendor managed inventory systems have the goal of accomplishing deeper integration and collaboration between the members of the supply chain in order to cope with the ever decreasing time windows for product and service fulfilment and the requirement for operational improvement and operational efficiency. Classen, Weele, & Raaij (2008) testify empirically that implementation of the Vendor-Managed Inventory system leads to improved service levels rather than cost reductions. Vendors and clients have linked computer systems, often using Electronic Data Interchange (EDI). This allows the vendors to monitor levels of inventory. Further, the Vendor-Managed inventory initiative has found, as Stevenson (2006), that stocking level variability is caused by factors such as deficient information sharing and deficient forecasts. He found out that the variability of inventory is majorly caused by firms not applying the inventory control systems. He enumerated the effects of inventory variability as inaccurate forecasting leading to periods of not having enough capacity, leading to inadequate customer service and high inventory costs. Inventory control systems would enhance procurement efficiency and effectiveness of the purchasing function and hence improve the performance of the firm (industry-trade, 2008). Fawcett, Ogden, Magnan, & Cooper (2006) appreciate the fact that companies have struggled to invest in the technology of inventory control systems and organizational structures needed to achieve data and systems synchronization that enables coordinated inventory flows. This enhances timely inventory replenishment, hence ensuring the availability of supply as demand arises. According to Baily, Farmer, Barry, Jessop, and David (2008), it is vital to get the balance of cost and service right. They argue that specific inventory targets are agreed upon and that it is the responsibility of the manufacturer to ensure that suitable inventory is always available. They continue arguing that this depends on timely information and suitable computerized inventory control systems, which have become available in recent years. However, they fail to clarify whether the situation has improved since before or not. Therefore, the current study intends to clarify the same.

Distribution Resource Planning (DRP) System

Distribution Resource Planning is a system for forecasting or projecting requirements for finished products at the point of demand (Farrington & Lysons, 2006). DRP systems are designed to take forecast demand and reflect this through the distribution system on a time-phased requirement basis (Baily, Farmer, Barry, Jessop, & David, 2008). From these projections, aggregated, time-phased requirements schedules for each echelon in the distribution system can be derived (Rushton, Croucher, & Baker, 2011). The distribution resource planning system acts by pulling the product through the distribution system once demand has been identified. According to Rushton, Phil, and Baker (2011), the system works towards the elimination of inventory. The distribution resource planning system tries to combine the need for lower inventory investment with improved customer service. According to Hansen & Mowen (2007), lowering inventory levels would give organizations a competitive advantage due to the production of quality products at lower prices and it would enable them to respond faster to customer needs. Businesses seek ways to reduce the time to bring products and services to market to gain a competitive edge (Hanke & Wichern, 2009). It enables physical resource requirements to be planned together with production and purchasing control. It controls the entire logistics system (Baily, Farmer, Barry, Jessop, & David, 2008).

Inventory control systems have the effect of smoothing the operations of the firm, hence reducing lead time (Cousens, Szweszewski, & Sweeney, 2009). According to Langfield-Smith et al. (2009), time delays in production affect operational performance negatively. They assert that cycle-time management can be used as a competitive advantage. However, Cousens, Szweszewski, & Sweeney (2009) are of the contrary opinion that reduction of lead time can only be beneficial if it does not negatively affect the operations of the firm. The control system may have an impact on the predictability of future demands and the speed of the firm’s production scheduling to meet customer demand. DRP serves a central role in coordinating the flow of goods inside the factory with the system modules that place the goods in the hands of the customer (Farrington & Lysons, 2006). Predictability of future demands, resource requirements, and consumer needs may contribute to flexible operational performance. Bowersox, Closs, & Cooper (2007) state that the competency of a firm can be measured by how well it is able to adapt to unpredictable situations. Accurate forecasting may have an effect on a firm’s inventory level. Chang & Lin (2010) state that the bullwhip effect is an example of predictive inaccuracy. Hanke & Wichern (2009) add that the capacity of the operational activities of the firm will be such that its output just matches demand. They say that excess capacity is wasteful and costly; too little capacity means dissatisfied customers and lost revenue. They argue that having the right capacity requires having accurate forecasts of the demand and the ability to translate forecasts into capacity requirements. The DRP system takes forecast demand and reflects this through the distribution system on a time-phased requirements basis (Rushton, Croucher, & Baker, 2011). According to Quesada, Gazo, & Sanchez (2012), accurate demand and sales forecasts help a firm out of stockout situations and allow business firms to provide a high level of customer service. The control system is oriented so as to facilitate accurate prediction of customer demand and, hence, timely response to their requirements. DRP provides the basis for integrating the manufacturing planning and control systems from the firm into the field (Farrington & Lysons, 2006).

The Impact of Computer Systems on the Management of Inventory in Supermarkets

An important factor in inventory management relates to production scheduling. Continuous process manufacturers often produce a mix of products, one at a time, using the same equipment and facilities. Each time a different product is to be produced, it is necessary to stop the production process and make adjustments before proceeding (Ritzman et al., 2003). The costs of shutdown and adjustments, which are referred to here as "changeover costs," can be rather high. Production time is lost while the facilities are closed down, and labor costs must be expended to make the necessary adjustments (Ritzman et al., 2003). As a consequence of the changeover costs, businesses try to find ways to minimize the number of changeovers. One of the principal ways of achieving this goal is through the use of inventory. Simply put, a company can choose to make many short production runs on each product in the mix, thereby incurring many changeovers and having smaller lots in inventory, or it can opt for long production runs with very few changeovers and increasing inventory lots. In 1880, there was a change in manufacturing practice from companies with relatively homogeneous lines of products to horizontally integrated companies with unprecedented diversity in processes and products. Those companies (especially in metalworking) attempted to achieve success through economies of scale—the gains of jointly producing two or more products in one facility. The managers now needed information on the effect of product-mix decisions on overall profits and therefore needed accurate product-cost information. A variety of attempts to achieve this were unsuccessful due to the huge overhead of the information processing of the time (Wagner, 2002). However, the rapidly increasing need for financial reporting after 1900 created unavoidable pressure for the financial accounting of stock, and the management's need to cost manage products became overshadowed.

In particular, it was the need for audited accounts that sealed the fate of managerial cost accounting. The dominance of financial reporting accounting over management accounting remains to this day with few exceptions, and the financial reporting definitions of 'cost' have distorted effective management 'cost' accounting since that time. This is particularly true of inventory (Saxena, 2009). Inventory management entails holding an appropriate amount of inventory. Too much inventory consumes physical space, creates a financial burden, and increases the possibility of damage, spoilage, and loss. On the other hand, too little inventory often disrupts business operations and increases the likelihood of poor customer service (Dimitrios, 2008). Inventory as an asset on the balance sheet of companies has taken on increased importance because many companies are applying the strategy of reducing their investment in fixed assets, like plants, warehouses, equipment, machinery, and so on, which even highlights the significance of reducing inventory (Coyle et al., 2003).

Changes in inventory levels affect return on assets (ROA), which is an important financial parameter from an internal and external perspective. Reducing inventory usually improves ROA, and vice versa if inventory goes up without offsetting increases in revenue (Coyle et al., 2003). Lean production and manufacturing is also an important consideration in improving the performance of the procurement function. Sanches and Ferez (2001) also investigated the link between lean production practices in manufacturing organizations and the resultant enhanced competitiveness. Through good housekeeping practices such as general waste reduction and minimizing hazardous waste, lean production is also expected to improve the performance of the firms.

King and Lenox (2001) conclude that lean production is complementary to improvements in the performance of the procurement function and it often lowers the marginal cost of pollution reduction, thus enhancing competitiveness. A study carried out by Atieno (2014) on ICT and supply chain performance on logistic firms in Kenya, where a population of 30 logistics firms in Nairobi was considered, was also considered. The findings from this study indicate that ICT is used in information sharing, which supports planning of operations, distribution, and material requirements. The study indicates that intermodal logistics can be enhanced by connecting stakeholders with an information and communication system, which will minimize costs. The study found out that there is a positive relationship between information and communication technology and the supply chain performance of logistics firms. The study concludes that there is a need to combine ICT and other organizational resources like work place practices, organizational structures, and the financial condition of a firm to maximize its benefits. Because ICT technology alone cannot create value.Research has shown that many companies have begun to use the Internet to manage their inventory (Lancioni, 2000).

Due to Internet-enabled inventory management systems' ability to provide real-time stock information at all levels in the supply chain from customer stocks, field inventories, and plant inventory, Internet deployment of inventory management rose from 30.1% in 1999 to 48.5% in 2001. Philips Semiconductors, a leading international semiconductor supplier, managed their inventory by using the Internet to simplify inventory tracking, reduce costs associated with manual labor, and increase supply chain accuracy (Philips, 2006). Rapid advances in computer and software technologies combined with the explosive growth of the internet have led many companies to rethink their business practices, to put a greater emphasis on their use of IT, and to invest more in enterprise organization.

Research methodology

Research Sample and Data Collection

For this research, the sample consists of 220 respondents from different markets across Enugu State in Nigeria, surveyed in 2019. The survey started in June 2019 and ended in August 2019. A stratified sampling method was adopted for the samples selected in this case. In order to be selected as a sample, the firms had to meet the following criteria: they must be in the supermarket industry and they must provide evidence of computer adoption. These selected service firms were chosen because of the practical reason that the researchers could visit their offices and conduct the survey, and also because the researcher could personally study their adoption of computers in order to understand what they mainly use them for. The respondents (who are staff of the selected firm) were selected based on the notion that they have a vivid understanding of the implementation of computer technologies in the service delivery process of supermarkets.

Measurement of Variables

A closer look at the literature on ICT adoption does show that most of the past studies have utilized questionnaire measures in that context (Kazungu et al., 2017; Dodokh, 2017; Ahmad et al., 2018). In line with that, a questionnaire was also used in this case to measure the variables. For the purpose of this research, the researchers adopted a questionnaire to gather data from the samples. The questionnaire was divided into two sections, with the first gathering data about the demographic variables of the sample, and the second section gathering questions in relation to the research variables.

Results

Descriptive Analysis

Contained in table (1) below are the descriptive statistics of the respondents’ demographic profile. As can be seen from the table, the majority of the respondents are male (67.3%), Igbo (39.5%), and aged from 31 years to 40 years old (50%). When it comes to the supermarket size, most of these are medium (55.5%), followed by large (24.1%), small (12.7%), and finally micro (7.7%). The table also shows that the majority of the respondents are senior staff (75.9%), followed by supervisors (10%), managers (7.7%), and finally the owners (6.4%). 

Table 1: Respondent Demographic Profile

 

Gender	n	%	Race	n		%		Age	n	%
Male	148	67.3	Igbos	87		39.5		Below 20 years	3	1.4
Female	72	32.7	Hausa	46		20.9		21-30 years old	57	25.9
Total	220	100.0	Yoruba	40		18.2		31-40 years old	110	50.0
			Others	47		21.4		41-50 years old	41	18.6
Size of Supermarket	n	%	Total	220		100.0		Above 50 years	9	4.1
Larger	53	24.1	Employment	n		%		Total	220	100.0
Meum	122	55.5	Senior staff		167		75.9	Frequency of use	n	%
Small	28	12.7	Supervisor		22		10.0	Rarely	86	39.1
Micro	17	7.7	Manager		17		7.7	Sometimes	86	39.1
Total	220	100.0	Owner		14		6.4	Frequent	48	21.8
			Total		220		100.0	Total	220	100.0

 

Data Measurement Model

To test the direct and indirect relationships between the variables, this data analysis was based on Structural Equation Modelling (SEM). For the start, data were analyzed based on measurements of central tendency and dispersion. Secondly, the validity and reliability of the scale were computed as documented in the table (2) below. In order to evaluate the reliability, the test of Cronbach’s Alpha was adopted and all the items were found to be above the 0.70 threshold, making then acceptable (Nunnally, 1978). The third step is the analysis of the principle component on each of the item. This is followed by the convergent validity that was estimated at the average variance extract (AVE). For the items loaded in this study, they were all above 0.5, which is considered the recommended rate (Bagozzi, Yi, & Phillips, 1991) and as such, said to be satisfactory. The composite reliability of the constructs is also above 0.60, the suggested level based on the views of Bagozzi and Yi (1988).

Table 2: Measurement Model

Construct	Indicators	Factor Loading*	Composite Reliability**	R2***	AVE****
Enhanced efficiency	UI4	0.555	0.88	.982	0.85
	UI3	0.586		-.393
	UI2	0.610		.979
	UI1	0.501		-.455
Enhanced effectiveness	PI4	0.557	0.71	.988	0.82
	PI3	0.596		-.695
	PI2	0.713		.984
	PI1	0.550		-.545
Computer application adoption	SC2	0.789	0.89	-.688	0.85
	SC4	0.771		-.487

Notes:* it is significant when it is above 0.5; ** Scale reliability is satisfactory when it is above 0.6;*** R-Square; ****Convergent validity is satisfactory when it is above 0.50

 

Confirmatory Factor Analysis

The confirmatory factor analysis (CFA) is a tool recommended for testing the fitness of the data to the model research (Kline, 1998). Validate the research scales was used in this research, and there are numerous indices that illustrate the fitness model, but the adopted indices in this case is the absolute fit as suggested by (Kline, 1998), which is indicated by Chi-squared test, GFI, AGFI and RMSEA. The results of the confirmatory factor analysis are presented in the table (3) below. In this result, what is illustrated is that the scales are unidimensional with high levels of reliability and validity.

In order to test the reliability and validity of the measures, CFI was adopted for each of the constructs (Joreskog & Sorbom, 1996). What is shown in the measurement model is that there is a high level of reliability and validity of the scales, with the table (4) also showing that the Cronbach’s Alpha than the recommended level of 0.70 (Hair et al., 2012). Based on the same table, further illustrations are the appropriate indexes of goodness-fit: CFI, GFI, non-significant x², and IFI that are all above 0.9, with an RMSEA having value below 0.8.

Table 3: Confirmatory Factor Analysis (CFA)

Variables	M	SD	Items	X2	GFI	AGFI	RMSEA	Cronbach Alpha	SCRa
Enhanced efficiency	3.8591	0.95715	4	60.78	0.91	0.89	0.0001	0.801	0.84
Enhanced effectiveness	4.3364	1.10025	4	65.90	0.92	0.89	0.0013	0.860	0.88
Computer application adoption	3.8500	1.18215	4	80.79	0.94	0.88	0.0011	0.888	0.69

a Scale composite reliability.

Estimation and Model Results

Simultaneous assessment of the relationship between variables can be conducted with SEM. Based on the findings so far, the fit indices does illustrate a good fit for the model: Chi-square = 18.32, p = 0.00, (Goodness of Fit Index) GEI = 0.97, Incremental Fit Index (IFI) = 0.98, Comparative Fit Index (CFI) = 0.98, Standardized PMR = 0.094, RMSEA = 0.38. Based on the analysis, the following hypotheses are adopted.

HI: adoption of computer application Þ enhanced efficiency. With a b (0.72), the relationship is positively significant.

H2: adoption of computer application Þ enhanced effectiveness. With a b (0.68), the relationship is positively significant.

Conclusion

The study concludes that computer is useful in attaining efficiency and effectiveness in the inventory management of Supermarkets. Computer applications such as VMI and DRP were found in the Business because they are easily understood, easy to implement and cost-effective. Computer systems help to make early decisions about inventory control in case there is any interruption in the supply and that IT better monitor demand for certain products and place orders to prevent an out-of-stock situation; the use information technology assists in inventory monitoring and asset visibility. This implied that not all ICT factors contributed to efficiency of inventory.

Limitation

This study is limited in the sense of its scope. It was based on only supermarkets in Enugu, which limits the application of the findings in the context of the entire Nigeria because, what is obtainable in Enugu might not be the case for other countries. On the same note, micro supermarkets (road side stores) were considered and they rarely use computer applications.

Direction for Further Studies

Based on the limitations above, it is suggested that further studies within this setting should seek to gather primary data from more states in Nigeria, in order to ensure the development of findings that can be used as actual representation of the entire country.

References

Abhyankar, H.S. & Graves, S. (2001). Creating an inventory hedge for Markov-modulated Poisson demand: Application and model. Manufacturing & Service Operations Management 3 (4), 306 - 320.

Alexander, K., Gilliam, T., Gramling, K., Grubelic, C., Kleinberger, H., Leng, S., Moogimane, D., & Sheedy, C. (2002). Applying auto-ID to reduce losses associated with shrink. IBM Business Consulting Services, MIT Auto-ID Center White Paper, November.

Anupindi, R., Morton, T. & Pentico, D., (1996). The non-stationary stochastic lead time inventory problem: Nearmyopic bounds, heuristics, testing. Management Science, 42 (1), 124-

Atali, A. & Ozer, O. (2005) Multi-Item inventory systems with Markov-modulated demands and production quantity requirements. Working Paper, Stanford University.

Boyacı, T. & Ozer, O. (2004). Information acquisition via pricing and advance sales for Capacity Planning: When to stop and act? Working paper, Stanford University.

Buzacott, A., & Shantikumar, G. (1994). Safety stock versus safety time in MRP controlled production systems. Management Science, 40, 1678-1689.

Cachon, G. & Lariviere, M. (2000). Supply chain coordination with revenue-sharing contracts: Strengths and limitations. Forthcoming Management Science.

Cachon, G. & Lariviere, M. (2001). Contracting to assure supply: How to share demand forecasts in a supply chain. Management Science 47, 629-646.

Cachon, G. (2003). Supply chain coordination with contracts. Handbook of Operations Research and Management Science Vol. 11, Elsevier, Amsterdam.

Caglar, D., Li & Simchi-Levi. D. (2004). Two-echelon spare parts inventory system subject to a service constraint. IIE Transactions 36, 655-666.

Chen, F. & Song, J. (2001). Optimal policies for multi-echelon inventory problems with Markov modulated demand. Operations Research, 49, 226-234.

Chen, F. & Yu, B. (2005). Quantifying the value of leadtime information in a single-location inventory system. Manufacturing & Service Operations Management, 7,144-151

Chen, F. (2001). Market segmentation, advanced demand information, and supply chain performance. Manufacturing & Service Operations Management, 3, 53-67.

Chen, F. (2003). Information sharing and supply chain coordination. Handbooks in Operations Research and Management Science Vol. 11, Eds.

Cheng, T.; Podolsky, S. (1996). Just-In-Time Manufacturing: An Introduction, 2ed. London: Chapman & Hall.

Corbett, C., DeCroix, G. & Ha, Y. (2001). Optimal shared savings contracts in supply chains: Linear Contracts and Double Moral Hazard. Working Paper.

Daniel, E.M. (2004) Exploring the Role of Third Parties in Inter-Organizational Web Service Adoption. The journal of Enterprise information management, Volume 15, number 5, pp. 351-360.

Donald, W., Dobbler, P. & David, N., (1996). Purchasing and Supply Management, 6th Edition, Pitman Publishing

Franke, P. (2010). Vendor-Managed Inventory for High Value Parts—Results from a survey among leading international manufacturing firms. ISBN 978-3-7983-2211-0

Government of Kenya (2005) Gazette Supplement No.12 Public procurement and disposal bill (2005) Government printer Kenya

Kimutai, S. (2010). Factors affecting inventory management in Kenya’s Public sector, focusing on KISE in Nairobi. MBA Project.

Lancioni, R. (2003). Internet impacts on supply chain management. Industrial Marketing Management 32, pp. 173-175.

Lancioni, R., Schau, H. J., & Smith, M.F., (2003) Internet impacts on supply chain management. Industrial Marketing Management, Volume 32, Issue 3, Pages 173-175

Lazarus M., (2000), Operations Management Techniques in Maintenance Management. New

Lemoine, W., & Dagnaes, L. (2003). Globalization strategies and business organization of a network of Logistics service providers. International Journal of Physical Distribution & Logistics Management, Vol. 33, No. 3, pp. 209-228.

London. Mcivor, R., & Humphreys, P. (2004). The implications of electronic B2B intermediaries for the buyer-supplier interface. International Journal of Operations & Production management, Volume 24, Number 3, pp. 241-269

Lysons, K. (2003) Purchasing and Supply Chain Management. Sixth Edition, pitman publishers

Momanyi, E., & Sanewu, E., (2014) Impact of Information Communication Technology on Inventory Control Systems in Transport Organization: A Case Study of Kenya Ferry Services. MBA Project, University of Nairobi

Morrison, A. (1991). Storage and Supply of Materials. Pitman Publishers London.

Mugenda, O. & Mugenda, A. (1999), Research Methods, Acts Press, Nairobi. Reck, R. & Long, B (1988) Purchasing a competitive weapon. Journal of Purchasing and Management, Vol.1 Issue (5), 31-46

Simchi-Levi, D. Xin, C. & Julien, B (2004). The Logic of Logistics: Theory, Algorithms, and Applications for Logistics Management, 2 nd Ed. New York: Springer

Tempelmeier, H.E. (2011) Inventory Management in Supply Networks, 3 rd . Edition, Norderstedt, ISBN 3-8423- 4677-8

Wasike J. & Ogollah K. (2014). Role of Information Systems on Supply chain Agility in Service Industry, Technical University of Kenya. The strategic Journal of business and change management.

Waweru, S. (2010), The Effects of Inventory Levels and Stock outs on Procurement Performance, www.bidcoafrica.com, York Publishers.