3.6 PROCESAMIENTO DE RESULTADOS
3.6.3. DISCUSIÓN DE RESULTADOS
Many expect that BI is used to generate various aspects of business views for supporting and analysing accurate and timely information to increase the company’s performance
(Gangadharan & Swami 2004). Moreover, BI leads to improved financial, strategic
operational or information risk by enabling decision makers to see changes in the underlying business as fast as possible (Ericsson 2004). As a result, BI is capable of leveraging the company’s assets to optimize their value and provide a good return on investment (ROI) (Thierauf 2001). Initially, BI is an important strategic tool intended to help with planning and performance measurement rather than with purely operational decisions (Rouibah & Ould-ali 2002). Prior research reported that by utilising the model to assess BI performance, 24% improvement in effectiveness has been achieved in terms of system effectiveness and user satisfaction (Lin et al. 2008). Moreover, BI has the potential to offer decision makers a better perspective compared to analysis by conventional methods in many situations.
21 For example, as pointed out by Olszak (2002), BI also provides adequate and reliable up-to- date information on different aspects of enterprise activities. A variety of companies
including retailers, telecommunications providers, travel agencies, and manufacturers use BI for various activity purposes such as customer profiling, customer support, market research and segmentation, product profitability, statistical analysis, and inventory and distribution analysis (Olszak & Ziemba 2004).
Particularly in the retail industry, as researched by Ranjan & Khalil (2008), BI is becoming necessary at the present time for the retail sector in India and is widely accepted for modern and advanced decision support tools. As such, the retail giant Shopper’s Stop in India is trying to upgrade its business strategies using these particular systems. It could be believed that BI systems provide good impacts to companies in: 1) stabilising the decision-making process; and 2) increasing the visibility of company information to stakeholders. Regarding this, Cunningham, Song & Chen (2004) showed significantly that a BI (e.g. data warehouse) supports CRM analyses by providing various profitability analyses (e.g. customer profitability analysis, market profitability analysis, product profitability analysis, and channel profitability analysis).
Moreover, as described by Nguyen Manh, Schiefer & Tjoa (2005), Sahay & Ranjan (2008), and Viitanen & Pirttimaki (2006), the development of a decision support information system such as a BI decision support function is critical to successful decision-making in seeking to reduce data latency (near real-time). It is important because not only is it the analysis done on near real-time data, but also actions in response to analysis results can be performed in near
22 real-time, and instantaneously change parameters of business process (Azvine, Cui & Nauck 2005).
As ERP has grown fast in automating back office operations and become an important infrastructure for many organisations (Hannula & Pirttimaki 2003), it is clear that today’s companies are more process-oriented than in the past and process-driven decision support systems are emerging to help enterprises improve the speed and effectiveness of business operations with the ability of data-driven decision-making (Baïna, Tata & Benali 2003). Like other industries, several players implementing ERP have used BI as a potential analytic technology to extend decision support capabilities, and to generate various aspects of business views through manipulating existing data from different sources of each functional
application captured by the company’s information systems (Chou, Tripuramallu & Chou 2005).
Since ERP includes the entire range of a company’s activities and integrates all facets of the business, including planning, marketing, and manufacturing (Shehab et al. 2004), BI has elements or processes incorporating related technologies for transaction-based systems8
Analysing sales trend and patterns, customer profitability and product profitability. This includes demographic-based response modelling for product offers and
(Foster, Hawking & Stein 2005; Hawking, Foster & Stein 2008). Thomas Jr. (2001)
suggested that BI has had a significant impact on different functions (e.g. marketing, finance, production, etc.) in high IT and information usage industries including the ERP industry. In particular, Abukari & Jog (2002) pointed out that firms use BI in a number of ways to seek competitive advantage including the following:
8
23 advertising campaigns, customer-characteristic segmentation, and cross selling of products;
Analysing customer lifetime valuation by understanding the pattern of repeat purchases, money spent, and longevity;
Analysing customer satisfaction through multi-functional processes of on-time delivery, support calls, complaints, and returns;
Supplying information for procurement decisions such as inventory velocity and supplier delivery performance; and
Analysing the firm’s value-creating activities through analysis of financial statements and financial figures of merit such as profit margins and economic value added.
Under these circumstances, BI can assist in enhancing ERP, achieving customer relationship management (CRM), supporting supply chain management (SCM), and generating near real- time monitoring abilities (Liautaud & Hammond 2001; Payton & Zahay 2005). Moreover, from this perspective, ERP can use information for different functional applications in order to enable costs to be cut, to enhance stronger customer linkages to be created, to innovate near real-time monitoring, and to plan for their businesses (Hannula & Pirttimaki 2003). Most ERP have highly integrated databases used to congregate all needed data modules from the system and load them into a data warehouse or a data mart, and then link to BI tools (e.g. OLAP, DM, query, and reporting). Chung, Lee & Pearn (2005) reported that BI will become a new direction for enterprises with the deployment of ERP, SCM, CRM, etc. It is clear that the use of BI and decision support applications (BIDSA) in the organization is growing at unprecedented levels and shows no signs of slowing down (Chou, Tripuramallu & Chou 2005).
24 With rapid increases in the number of users considering the value of information, BIDSA has become a critical aspect for organisations in environments of high information usage to consolidate analysis of the data with user friendly reporting capabilities for making intelligent and correct decisions to gain advantage over competitors. It also offers functionality and new ways of doing data analysis and decision-making that no business can afford to ignore. In addition, on the demand and supply side, the use of BI application packages increases
worldwide every day. The market for business intelligence solutions has grown quickly (see Figure 2-1). This is being driven by a trend for consolidation, with several large application and software infrastructure vendors initiating major BI acquisitions (Gartner 2008).
From discussion above, it is implied that BI and decision support applications (BIDSA) has now become an essential decision support component for many companies. BI techniques will be embedded into business processes. It is the embodiment of the data-driven DSS. BI as business information and business analyses within the context of key business processes leading to better decisions and actions and that results in improved business performance. Significantly, for business BI in practice is to assist in increasing revenues and/or reducing costs, thereby improving performance and increasing profits. Thus, with the rapid increase in the number of BI software packages and their use by many organisations, these are not only the place where they manage information about products and services, but they also offer commercial value in terms of profitability.