When we talk about product life cycle costing, we look at the cost of the product over its life cycle including the accumulated costs right from R&D to disposal. The terms LCC was first coined by the US Department of defense, a term which was very common among the men in uniform and also among the quality surveyors and constructors (Popovi? and Boji?, 2012). The software industry has been the discipline slow to pick up the methods of LCC in software cost estimations. But this has been fast changing since more industry players are coming to terms with the LCC methods and factoring many matrices in handling LCC.
Few scholarly articles have been written in this subject areas to broaden the research on this area. A study in Finland has shown that a scaring 5% of the major ICT industries use the LCC in totaling the project costs. The Swedish, according to report penetration of LCC is about 60%. This shows clearly that the LCC concept in really making penetration and about to hit mass adoption (Munassar and Govardhan, 2010).
Several philosophers have argued on the best definition of LCC. From business, LCC can be defined as the total cost of purchasing a good and or service from the supplier of choice. The main purposes of LCC are first, it is vital in selecting the suppliers since the project lead can way the LCC for each supplier quotation and pick the reasonable one. LCC can also serve the purposes of evaluating the different supplier and compare their quotation by factoring their different LCC. This enables the project lead to easily have an evaluation of different suppliers. To put this into perspective, scholars have deduced that TCC concerns majorly with the transactional costs foregoing the operational costs (Basbagill et al., 2013).
According to research, most LCC focuses primarily on the capital invested and the fixed assets required to finish the project. Several factors affect the LCC and some scholar have published work on the same. The factors includes the service life including design and life cycle of the product, the period it takes to conduct analysis, and the amount of costs variables which includes but not limited to the cost of acquisition, maintenance and other software operational and management costs, costs associated with disposal of the software product normally called the residual costs, the current inflation rates in the particular region and other utility costs which may include the energy consumptions accrued to the software product (Azhar, 2011).
The design and analysis phase particular requires expertise to deliver the phase deliverables. The cost of hiring this expertise must, therefore, be included in the LCC. According to [], LCC does not get aggregated once the software is delivered, factors such as maintenance come into play as more cost continuously get incurred to ensure the software delivers its user requirements and system other non-functional requirements (Krutz and Vines, 2010).
From the literature review, it is clear LCC methodology has come along way and has the gain foot in the software life cycle. This is evident by the penetration it has achieved especially in North America and Europe. The project leader has realized the TCO only is not enough to objectively know the total cost accrued to the software product. This made the LCC be favored as it covers even the hidden costs of disposal and maintenance in calculating the LCC.
Factors affecting the LCC of a product can be categorized in the following categories, first, the total costs incurred during the development of the product. At this stage, all the costs incurred in conducting the research is factored in. Other costs such analysis and the design of the product must again be factored in. The more complex the product is the more development cost it will incur leading to generally a high LCC. Another great factor that can affect the LCC is the customer’s acceptance of the product.
Some product to have a high LCC when the owners try to convince the prospective buyers through costly marketing strategies to buy the product and or service (Ebert and Jones, 2009). This lack of customer taste and preference for the product can lead to a high LCC since it will take a longer time before the product hits mass adoption. Another factor that can greatly affect the LCC is the distribution method adopted by the product owner to its clients. If the method of distribution means more costs incurred on the developers, it is obvious its TCC will be high compared to products where the buyer incurs some costs of distributing the software product to the prospective buyers.
Another hidden cost is the amount of customer support services which are offered by the developers to their clients If the product us buggy and less understood by the users, more resources will be channeled to customer support to ensure all user complains and reviews get handled within the shortest time possible (Islam, Jollands and Setunge, 2015). The overall effect is more costs incurred for customer support services.
All the costs incurred at every life cycle of product normally have a direct effect on the subsequent phase costings and the LCC in general. If the research and development phases take an abnormally long time, more costs shall be accrued on the life cycle since this stage consumes a lot of time, effort and finances (Chen, Ali Babar and Ali, 2009).
The market launch strategies again will have a long impact on the LCC. If the marketing takes long to hit the mass adoption, then the cost will trickle down to maintenance costs and daily operational and management costs. It is therefore vital to have the costs incurred in the various stages of life cycle be put in a budget to avoid over costing which can adversely affect the product success.
To ensure the costings are within budget, the project leader must use the available costs estimation matrix and his/her expert opinion to come up with reasonable costs which can be accepted when presented to the board (Agrawal and Tiwari, 2010). The costs benefits analysis must be done in making costing decisions to ensure the right value for the money spent on the software life cycle.
Figure 1 High-Level Loop Diagram
Figure 2 Detailed Loop Diagram
The system of choice here is a knowledge management system which has the perfect life cycle that is hereby modelled as shown below,
Figure 3 LMS Casual Loop Diagram
When the complete life cycle of a LMS is exploited, the following detailed CLD is obtained as shown below,
Several dangers lurk and buying and using cheap software. Most companies and students always prefer purchasing cheap software to do their functions. According to (Deutsch, 2010), such software poses a serious security risk to the users due to hurriedly developed systems. The user could be buying software which has not undergone the industry quality assurance test and penetration tests to ascertain that the product is legit. This cheap software always provides lucrative deals that are hard to turn down. If you take your security and privacy seriously, (Deutsch, 2010), recommends that you don’t buy cheap off the shelf software, they are a perfect breeding ground for malware.
Secondly, most of these in premium software does not include user support in their contracts. This makes the sell a one time deal. It is the buyer who will be having time to get support for their users who will obviously have support issues but since there is no support from the vendor, the company is always faced with the tasks of outsourcing and or hiring experts to help with support. This will generally increase the LCC (Kneifel, 2010).
Thirdly, cheap software is not reliable and can make a company lose billions of shillings due to high total downtime. This software can crush anytime without giving any warning signs and make the company lose profits (Patra, Söderholm and Kumar, 2009).
Fourth, some cheap products could be counterfeit of the original deal and this can put the company into legal battles with the original developers. This, in turn, will incur more cost onto the LCC of the software (Okasha and Frangopol, 2009).
Fifth, the cheap software will definitely limit user experience as some of the features get locked for premium versions. Important features like the ability to receive updates could be unavailable in the cheaper software making it pose a security threat to the use and the company. Cheap version such as the student version is offered at a cheap price to allow student acclimate to the software and probably will buy full feature software at work. This business model makes fraudulent business people resell this product cheaply than the original product(Stark, 2015)
In a nutshell, cheap software seems appealing from an economic point of view since it greatly reduces the total costs of ownership. It should, however, be noted that such software have inherent problems which when get shipped into the company can adversely affect the operation of the company since they are annoying unreliable, buggy due to hurry in taking them to the market (Ruparelia, 2010), legal issues in arose from their purchase especially if counterfeit is the reason for the cheap price. It is therefore recommended to get premium quality software which has a warranty and guarantee to perform normally and meets the user and organizational requirements. However some situation warrantees the use of cheap software as explained below,
When the usability is not a problem and the users just want specific features of the software to conduct their daily tasks, the cheap software would be highly recommended.
When the user is still a student who just needs the cheap software to get the minimum environment the student needs the software to run on (Kaenzig and Wüstenhagen, 2010). The cheap software seems budget friendly for students who still need to pay tuition fee hence can hardly afford premium software.
When the business has a strong internal ICT department who have mastered the navigation of most system hence can offer technical support to uses, then a cheap software would be recommended to avoid raining the LCC of the software when external support is outsourced (Finkbeiner et al., 2010).
References
Agrawal, B. and Tiwari, G.N., 2010. Life cycle cost assessment of building integrated photovoltaic thermal (BIPVT) systems. Energy and Buildings, 42(9), pp.1472–1481.
Azhar, S., 2011. Building information modeling (BIM): Trends, benefits, risks, and challenges for the AEC industry. Leadership and management in engineering, 11(3), pp.241–252.
Basbagill, J., Flager, F., Lepech, M. and Fischer, M., 2013. Application of life-cycle assessment to early-stage building design for reduced embodied environmental impacts. Building and Environment, 60, pp.81–92.
Chen, L., Ali Babar, M. and Ali, N., 2009. Variability management in software product lines: a systematic review. In: Proceedings of the 13th International Software Product Line Conference. Carnegie Mellon University, pp.81–90.
Deutsch, M., 2010. Life cycle cost disclosure, consumer behavior, and business implications: evidence from an online field experiment. Journal of Industrial Ecology, 14(1), pp.103–120.
Ebert, C. and Jones, C., 2009. Embedded software: Facts, figures, and future. Computer, 42(4).
Finkbeiner, M., Schau, E.M., Lehmann, A. and Traverso, M., 2010. Towards life cycle sustainability assessment. Sustainability, 2(10), pp.3309–3322.
Islam, H., Jollands, M. and Setunge, S., 2015. Life cycle assessment and life cycle cost implication of residential buildings—A review. Renewable and Sustainable Energy Reviews, 42, pp.129–140.
Kaenzig, J. and Wüstenhagen, R., 2010. The effect of life cycle cost information on consumer investment decisions regarding eco?innovation. Journal of Industrial Ecology, 14(1), pp.121–136.
Kneifel, J., 2010. Life-cycle carbon and cost analysis of energy efficiency measures in new commercial buildings. Energy and Buildings, 42(3), pp.333–340.
Krutz, R.L. and Vines, R.D., 2010. Cloud security: A comprehensive guide to secure cloud computing. Wiley Publishing.
Munassar, N.M.A. and Govardhan, A., 2010. A comparison between five models of software engineering. International Journal of Computer Science Issues (IJCSI), 7(5), p.94.
Okasha, N.M. and Frangopol, D.M., 2009. Lifetime-oriented multi-objective optimization of structural maintenance considering system reliability, redundancy and life-cycle cost using GA. Structural Safety, 31(6), pp.460–474.
Patra, A.P., Söderholm, P. and Kumar, U., 2009. Uncertainty estimation in railway track life-cycle cost: a case study from Swedish National Rail Administration. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 223(3), pp.285–293.
Popovi?, J. and Boji?, D., 2012. A comparative evaluation of effort estimation methods in the software life cycle. Computer Science and Information Systems, 9(1), pp.455–484.
Ruparelia, N.B., 2010. Software development lifecycle models. ACM SIGSOFT Software Engineering Notes, 35(3), pp.8–13.
Stark, J., 2015. Product lifecycle management. In: Product Lifecycle Management (Volume 1). Springer, pp.1–29.
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