Decision support systems are outlined as applications that combine computer technology with communication and decision making. These systems interpret information to support the decision phases of any organization or business, this will include identifying problems, formulating plans and generating the final solutions (Radu, et al., 2014). Therefore, the central component of DSS is a collaboration where the decision phases are coordinated using the knowledge base held by the business. For instance, the retail industry (e.g. a shop or supermarket) will assess the products it sales and coordinate this information with the customers’ feedback and also with the supplier's cost to yield the best decisions i.e. maximum profit (Russel & Yoon, 2014).
On the other hand, cloud computing is based on the availability of networks, cheap storage and high system performance. These outcomes are met by its defining environment where operational resources are hosted by different users and distributed to others using the internet connections (Russel & Yoon, 2014). Therefore, through the services offered by cloud computing business functionalities supported by technology are simplified and made more efficient. These functionalities include DSS and other financial applications such as those seen in the retail industry. A good example of this convenience and efficiency is a retail store with shops in multiple locations, this store can use the same systems to support its activities by hosting it in a cloud facility.
Decision support system and retail business
DSS can be applied in all the functionalities of business so as to help in solving the many decision-making problems. In this scenario, this report considers marketing strategies and how they can be aided by DSS, thus facilitate the allocation of resources, which is usually a key problem for many retail stores. In this system, the retail business e.g. a shoe selling company, Bata Limited, will combine the analysis methodology with sophisticated computer applications to produce the management decisions (Montgomery, 2004). Therefore, the DSS must account for all the marketing variables exhibited by the business environment. Thereafter, the system will develop a modelling design based on the user’s requirements, for instance, to increase the average price of promoting the products (shoes).
Figure 1: A decision support system overview structure, Bata Limited (Lodish, 2001)
Now, consider the model adopted by the DSS as shown above Figure 1 where the management’s judgement is based on the collaboration of the different variable highlighted, such as market growth, profit margin and predicted sales among many others. In essence, these evaluations would take ages to accomplish if it was conducted using manual calculations. However, with computerised technology, the company (Bata Limited) needs only the figures involved and the predictions, including the marketing strategies, are easily outlined. Furthermore, the DSS can be tuned to fit decision plans outlined by an organization, this will include focusing on broad marketing strategies to narrow and targeted promotional campaigns (Lodish, 2001). In all, decision support systems can greatly minimise the time spent on marketing decisions as they can automatically generate the models needed by retail business based on the existing variables.
Cloud computing and retail business
In any definition given for the retail industry, the end consumer (final customer) is always highlighted. Therefore, despite the field, an enterprise will be classified as a retail business if it provides goods and services to the final consumer. Now, this group of businesses have seen an overwhelming increase in the use of cloud based solutions because of the opportunities and benefits they provide. For one, these businesses realise that their customers require and demand efficient services. Moreover, this demand is intensified today by the emergence of online and mobile markets (Ali & Haseebuddin, 2015).
The opportunities: First, cloud computing is cost effective as it facilitates the creation, sale and management of services. A retail business can use online stores to sell and promote products across the world, this outcome translates into lower operational costs as outlined in the figure below, where traditional retail stores are compared with online stores Figure 2. Furthermore, the same organizations can use data analytics, hosted on the web server to make sale decisions. Secondly, cloud based solutions are extremely flexible and scalable which increases the business reach. For instance, the shoe selling company highlighted above can promote its services over a wide range of environments such as mobile apps and websites. Moreover, the same business can adjust its resource requirements based on its immediate needs, for instance, it can shut down an online store without any significant financial losses. Finally, cloud computing promotes innovation, an outcome that has led to the development of online stores and other active and engaging retail portals (Revesencio, 2017).
Figure 2: Traditional Vs. cloud supported stores (Online) (Godfrey, 2013)
Information technology and its affiliated components such as IS have rapidly increased the availability of information. This information, although helpful in business operations has proven to be a challenging factor as organizations have to assess it in order to make important managerial decisions. Decision support systems are therefore used to manage data and to produce meaningful decisions. This outcome is done by evaluating the variables involved as highlighted in this report where different factors of retail business are outlined. On the other hand, cloud computing facilitates these systems by introducing new technological models for delivering services. In essence, large volumes of data needed by financial systems/applications are stored in any location without the consideration of time or place. Furthermore, the cloud based solutions facilitate the functionalities of DSS by directly engaging with the customers through online applications. These facilities help to segment the market and to increase the sales, based on the conveniences and the opportunities introduced by the digital market.
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Godfrey, M., 2013. With announcement of G-Cloud iii and Cloud First Strategy, how can organisations adopt a Cloud strategy so it is no longer the elephant in the room?. Automated intelligence, Available at: https://www.automated-intelligence.com/blog/as-the-uk-government-cloud-first-strategy-is-announced-how-can-organisations-adopt-a-cloud-strategy-is-announced-so-it-is-no-longer-the-elephant-in-the-room/ [Accessed 14 August, 2017].
Lodish, L., 2001. A Marketing Decision Support System for Retailers. Research gate, Available at: https://www.researchgate.net/publication/227441977_A_Marketing_Decision_Support_System_for_Retailers [Accessed 14 August, 2017].
Montgomery, A., 2004. The Implementation Challenge of Pricing Decision Support Systems for Retail Managers. Research Showcase @ C, Available at https://repository.cmu.edu/cgi/viewcontent.cgi?article=1336&context=tepper [Accessed 14 August, 2017].
Radu, C., Candea, C. & Candea, G. &. Z. C., 2014. Towards a Cloud-Based Group Decision Support System. Recent Developments in Computational Collective Intelligence, Available: https://link.springer.com/chapter/10.1007/978-3-319-01787-7_18 [Accessed 14 August, 2017].
Revesencio, J., 2017. Buying In the Cloud: How Cloud Technology is Revolutionizing the Retail Industry. Business.com, Available at: https://www.business.com/articles/buying-in-the-cloud-how-cloud-technology-is-revolutionizing-the-retail-industry/ [Accessed 14 August, 2017].
Russel, S. & Yoon, V. &. F. G., 2014. Cloud-based decision support systems and availability context: The probability of successful decision outcomes. Research gate, Available at: https://www.researchgate.net/publication/220385010_Cloud-based_decision_support_systems_and_availability_context_The_probability_of_successful_decision_outcomes [Accessed 14 August, 2017].