**Decision Technique Analysis of Catanza Technologies**

Executive Summary

Process redesigning, facility site selection, and making-versus-buying are some of the collective decisions that require sophisticated financial analyses that the regular, simple, and traditional reports. Generally, the requirement for increased effective and efficient decision making at all faculties within an organization is imposed through the forces of competition. Quantitative analysis for decision-makers in all sections, including analysts in planning departments and public organizations, manufacturing process planners, economic and financial analysts, and engineers, is offered by the decision analysts. There is a progressive approach to modelling that entails two distinct parties, the model-constructor (analyst) and the decision-maker when making decisions. The decision analyst should be taking into regard the needed set of analytical techniques since he or she is the decision-maker in the decision-making process.

Overview of Key Facts

The most critical factor determining the success of implementing a decision model relies upon the forward-thinking and modern model building, which is also known by the name the bootstrapping approach. Outcomes of models tend to determine the deterministic model when making decisions as much as in probabilistic models; the decision-maker is interested not only on the total result but also the number of risks entailed when making decisions. Application of statistics when assessing uncontrollable factors or events and the risk assessment of decisions by checking their probabilities is the central concept in probabilistic modeling (Osman, 2010). Operations and activities are based on the anticipated outcomes, are the method of compiling the probabilistic models, which are also compared to games. Taking into consideration the subjective statistical methods for predicting, testing, and estimating is also a centre of interest, as illustrated in deterministic probabilities. Therefore, the decision analysis procedure can be utilized below for both private and public decision making under distinguished decision process, quality, and type of available data, as illustrated in the systematic decision-making study framework for applying the complex causes of action, conflict-ridden and uncertain situations.

Catanza Technologies specialized in Industrial Sector Technology and was founded in 2002 at Eagle Farm. The company introduced a commercial-grade robotic floor cleaner that generated slow development in market share and revenue in 2014 following the financial decline in automotive manufacturing in 2008. An elaborate plan has been put forward to increase the production of the RLM19 as the initial action in developing the commercial robotic lawn management tools and equipment in January 2019. Some of the decisions such as mowing area per day based on battery charging period and running time per charge, ease of navigation, ease of programming, anti-theft and safety, adaptability to uneven ground levels, maximum cut heights etc. include the considerations put forward when manufacturing the new prototype. Therefore, the company plans to come up with the EW (Electric Wire) perimeter or GPS. Other specifications of both products entail the EW model being connected to a wire and have a mowing area of around 300 sq. meters each day while the GPS model utilizes sensors to navigate without using a cable thereby enabling more versatility and covering of more significant areas of around 12000 sq. meters per day. A successful prototype is expected by December 2019 as accorded by the R&D, which has already estimated the cost of this production process at $1.65 million. A further investment of $1.75 million will fast forward the production process, thereby completing the EW prototype, according to Scott Shorten’s 75% chance completion rate.

The company shall review the process in January 2020, suppose the target is not attained, and an unsuccessful prototype modification would require additional six months and $0.8 million to adjust, thereby finalizing all financial problems by the end of June 2020. Moreover, a $3.3 million investment in the GPS model would hasten the production process by December 2019. A $1.9 million cost shall be incurred towards the modification of an unsuccessful prototype suppose the target shall not be achieved by January 2020 as much as all challenges could be over handled by December 2020. Furthermore, a six-month and twelve-month lead-time for component sourcing and retooling for the EW and GPS solutions respectively shall be implemented in the Chinese manufacturing facilities. Sales are estimated at $3500 and $4500 for the EW units and $14000 and $16000 for the GPS units upon commencing of the Jan 2021 and Jan 2022 production period s, respectively. A strategic marketing and advertising campaign around direct to ground maintenance contractors, owners of golf courses, playing fields and airfields, and trade shows shall also be conducted when planning a launching strategy. However, the main threat was increased competitive products by other leading mower manufacturing entities and new technologies in the electric motor and robotics technologies (Gilboa, 2011). An edge in the market can be launching their product earlier in the market, as illustrated in their market conditions probabilities, as demonstrated in both 6-year planning horizon table and month production commence tables. No residual value is expected towards the intellectual property suppose either of the prototypes shall be cancelled, and the cost of capital is estimated at 8%.

Approach to the Problem

Decision tree is a common classification and regression technique. It has a tree structure. In the classification issue, it speaks to the way toward ordering occurrences dependent on highlights. It very well may be considered as a lot of on the off chance that rules, or as a restrictive likelihood appropriation characterized on the component space and class space. The main advantages are that the model is readable and the classification speed is fast. When comprehending, use the training data to build a decision tree model as per the principle of minimizing the loss function. When forecasting, the new data is classified using a decision tree model. The idea of ??decision tree learning mainly comes from the ID3 algorithm proposed by J. Ross Quinlan in 1986 and the C4.5 algorithm proposed in 1993, and the CART algorithm (Breiman et al., 1984).

The generation and development of decision trees are carried out under the theoretical background of "decision analysis" in the field of economic management. Decision analysis is easy to understand. It is the process of data analysis for making certain decisions. Its goal is to help people make tools and methodologies for further good decisions. Decision analysis is generally divided into four steps: (1) forming a decision problem, comprising proposing a plan and determining goals; (2) judging the natural state and its probability; (3) formulating multiple feasible plans; (4) evaluating the plan and making choices. For example, as a 30-year-old north drifter, "Should I go home to develop or continue to drift" is a typical decision-making problem. To this end, collect data to determine the various benefits and disadvantages under the two programs of returning home and continuing to drift north. Determining weights and probabilities to calculate and evaluate various schemes is a typical decision analysis process. The final analysis results will help the north drift to make rational choices (ADB, 2015).

Decision analysis can be divided into three categories based on whether the decision problem contains random factors: deterministic decision (does not contain random factors), risk-based decision (the probability of random factors is measurable), uncertain decision (probability of random factors) Not measurable) (Gao et al. 2013). Different types of problems are very different in specific treatment methods. The decision-making example of the North Drifting Youth mentioned earlier, because of many factors, such as the calculation of total income in the next 20 years, there are too many intermediate variables, it is difficult to calculate the accurate probability, so this case is a typical uncertain decision (Kaur & Chhabra, 2014). Nobel Prize in Economics Daniel Carney has published a collection of papers "*Judgment in Uncertainty: Heuristics and Bias*", which is about cases and various solutions of uncertain decision-making, including a large number of mathematical concepts, including, for the quantification of subjective evaluation, interested children's shoes can be seen. Uncertain decisions are more complicated and subjective evaluation principles are more important. Therefore, if possible, the probability of random factors should be determined as much as possible, and the uncertain decisions should be converted into risk-based decisions for calculation, to obtain a more objective basis for decision (Song & Lu, 2015). From this perspective, in the foreseeable future, big data technology and related infrastructures are the magic weapon for turning uncontrollable to controllable and uncertain to risky decisions. Commonly used decision analysis techniques for risk-based decision-making include expected value method, decision tree method, and Markov analysis method and so on. Among them, the decision tree method is a graphical illustration of the decision-making situation, the most intuitive and clears (Ahishakiye et al. 2017).

Decision Tree, as early as the end of the 19th century, was applied to practice by experts in Western economics and mathematics. In the 1930s and 1940s, there was another great development, which is now a compulsory content in economic management. It is essentially a risk-based decision-making method based on the idea of ??probability and graph theory to obtain the optimal plan. The diagram of the decision tree is clearer than the table. Moreover, the decision tree is also a calculation model. Starting from each terminal node of the decision tree, the probability and payoff are calculated layer by layer, so that the actual profit and loss value of each decision can be obtained (Kapoor & Rani, 2015). This calculation process is called rollback calculation, which can help in figuring out which solution is better.

Probabilities and net present value in the decision tree

A decision tree has been made below. On this tree, a small square means a place where a person makes a decision, and a light circle is a place where chance decides everything. The probability values ??that are already familiar to us are written on the branches of the tree, and the outcome values ??or results are written on the right of the final branches.

We can use it to represent our possible actions and to find a sequence of correct decisions leading to the maximum expected utility. To show this, we complicate the task. We provide the person choosing between the actions of d1 and d2, additional opportunities. Let him, before his answer, can pull one ball out of the vase for a fee, and after pulling out the ball is put back into the vase. The fee for pulling one ball is CU 60. The decision tree with its two main branches is shown in Fig. 5. Now the question of which decision should be made has become more difficult: it is necessary to decide whether to take out the ball and what answer to give after pulling out the red or black ball.

Let us return to the description of the problem. The probability of pulling a red ball from a type 1 vase is pK (Bi) = 0.6, and from a type 2 vase Pk (B2) = O, 3. Knowing all the conditional probabilities (depending on the condition), as well as the probabilities pi and p2 of the choice of vases of the 1st and 2nd type, we can pose the following questions.

First question: what are the probabilities of pulling red and black balls? To answer this question, we perform simple calculations. The probability of pulling out the red ball is PK (Bi) = 0.7® 0 0.6 = 0.42 if the vase is type 1, Pk (B2) = O, 3 0 0.3 = 0.09 if the vase is 2nd type. Therefore, the probability of pulling out a red ball is generally pk = 0.51. Similarly, we can calculate that the probability of pulling out a black ball rc = 0.49.

The second question is more complicated. Let the drawn ball turn out to be red or black. Which action should be chosen: di or d2? To answer this question, you need to know the probabilities of vases belonging to the 1st and 2nd types after receiving additional information. These probabilities can be determined by the famous Bayes formula.

As shall be demonstrated in the figure below, represents the conditional probability distribution of the class given the characteristics. This conditional probability distribution is defined on a partition of the feature space. Dividing the feature space into disjoint cells or regions, and defining a class probability distribution in each cell constitutes a conditional probabilitydistribution. A path of the decision tree corresponds to a unit in the division. The conditional probability distribution represented by the decision tree is composed of the conditional probability distribution of the class given by each unit. Assuming that X is a random variable representing features and Y is a random variable representing classes, then this conditional probability distribution can be expressed as P (Y | X). X takes a value from the set of cells under a given division, and Y takes a value from the set of classes. The conditional probability on each leaf node (unit) tends to be biased towards a certain category, that is, the probability of belonging to a certain category is large. When categorizing the decision tree, the instance of the node is forcibly classified into the category with a large conditional probability.

Figure 3: Created decision tree

Now, my approach to the problem shall apply the above decision analysis method that initially entails understanding the objectives and identifying the decision situations, which under this case, would be gaining a competitive advantage over other competitive mower manufacturers (Križani?, 2020). Alternatives shall also be identified, which is illustrated from the options of Poor and Good shown in the tables that offer estimated financial insights to launching the product in Jan 2021 and Jan 2022or Jan 2020 and Jan 2021 for both the EW and GPs models respectively. The problem shall further be decomposed, which is illustrated by compiling different strategic pricing approaches for both the models to the 8% capital required in investments and the initial launching process. The best alternative under this method shall be demonstrated by the financial obligations and implementations (such as ROI, profitability, income generated, and reduction of marginal costs), which shall then proceed to the sensitivity analysis where more scrutiny is emphasized on product pricing and generating revenue through the product (Grant, 2011). Therefore, the final process shall continue where the ultimate implications shall prevail; suppose there shall be no need for further analyses.

Analysis is a sufficiently comprehensive concept that underlies all the practical activities of decision-makers. Unformalized methods of analysis are based on the description of analytical procedures at a logical level, and not on strict analytical dependencies. Unformalized methods include expert assessment methods, scenarios, psychological, morphological, comparisons, methods for constructing indicator systems and analytical tables, etc. The use of these methods in the process of PPRUR is characterized by certain subjectivity, as intuition, experience and knowledge of decision-makers are of great importance. Formalized methods include methods based on fairly rigorous formalized analytical dependencies. Among the most common methods of strategic analysis used in the practice of making and implementing management decisions are functional-cost analysis; factor analysis; SWOT analysis; GAP - analysis; CVP - analysis; cause and effect diagram (Ishikawa diagram). We characterize these methods from the standpoint of the application in practice of the preparation and adoption of managerial decisions.

Clear Statements of Assumptions

Firstly, accrual assumption should be utilized in the accrual basis of accounting where the expenses and revenues are only recognized when used or earned. Still, in the assessment, one observes financial statements that are based on cash flows (Ying-Chieh et al. 2013). Thus the business should instead utilize the cash basis of accounting; thereby, financial statements can be audited easily as illustrated from estimated net cash flows occurring during the 6-year planning horizon (Malakooti, 2010). A bias toward initial expense recognition is shown in the conservation assumption where the expenses and revenues are recognized when earned. Over optimistic financial results are offered by an entity suppose the assumption is not correct, as illustrated in the Good net cash flow calculations that never recognized the expenses (taxation and development grants) (Dey, 2006). Similar methods of accounting are to be applied for the 6-year planning horizon indicating the consistency assumption since it will be implemented from period to period unless a more appropriate process can replace it, thereby allowing for comparisons (Güss, 2011).

Furthermore, there is an economic entity assumption where the accruals of the entity shall not be intermingled with other entities, as illustrated from their product segmentations into either Electric Wire (EW) or GPS models. Deferred expenses are included in the 8% capital suppose a bankruptcy occurs as shown in the lack of residual value for the intellectual property as it operates for the foreseeable future as the going concern assumption. Lastly, a consistent period should be covered by these financial results, as illustrated in the six-month lead-time needed for the GPS model and annual considerations for both models when checking the launching strategies.

Decision Tree of Potential Decision Paths and Events

From the Decision Tree Diagram formulated one notes that the stipulated EV was estimated at $10.9 million.

Application of SMART Analysis

Initially, Specific can entail analyzing the goals focused upon the study in which one observes the company focusing on one main product, the commercial robotic lawn management tools, and equipment, the commercial-grade robotic lawnmower (RLM19), which was also segmented in the EW and GPS models. Each model had specific inclinations towards the production process, and one observes this from the particular production costs allocated to each segment. Measurable analyses (Polatidis, Haralambopoulos, Munda, & Vreeker, 2006) can be conducted or illustrated as the company has focused on three products over the years, namely industrial sensors, industrial robots and robotic floor cleaner which have amassed distinguished turnover represented differently through the product types. On notes that the robotic floor cleaner has experienced ascending growth from 2014 to 2018. The research presented contains attainable analyses since there are 6-year comparisons to the EW and GPS models respectively, where there are both "Poor" and "Good" expectation calculations from the tables, and probability calculations of the expected values in the tree diagram presented (Rausch & Anderson, 2011). Relevant analyses have been illustrated throughout the study as battery life and mowing area covered by these new models expected at 3000 square meters a day and 12000 meters a day for both the EW and GPS models, respectively. Furthermore, the above assessment is illustrated through the comparison of production cost expected for the two products in a 6-year planning horizon. Lastly, time-based analyses have been demonstrated through the "Poor," and "Good" comparisons for the 6-year planning horizon that has also affected the company's cost of capital is estimated at around 8%, and also the monthly production commences probabilities.

Course of Action

The appropriate course of action that would assist in gaining a competitive advantage for the company would entail launching the product within the six-month additional lead-time. The above process has been stipulated throughout the analysis as the best course of action since it would allow the company to utilize its marketing and launching strategy before their competitors as much as the expected value was estimated at $10.9 million indicating that initial launching periods (Jan 2020 and Jan 2021) for both the EW and GPS models would incur the company more cost. Still, the product shall have reached its optimal expected sales purchases, as illustrated from the comparisons.

Conclusion

To conclude the case study, with the above elaborate discuss it is clear and quite evident that if the product is introduced in the market with the 6 month duration period it will help Catanza technologies to gain an advantage over the rival competitors that it has in the market. This will enable and help the company to expand its operations further and diversify the activities and its control over the entire industry.

References

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Ahishakiye, E., Omulo, E., Taremwa, D. and Niyonzima, I. (2017). Crime prediction using decision tree (J48) classification algorithm. *International Journal of Computer and Information Technology, 6*(3), 188-195.

Dey, P. K. (2006). Integrated project evaluation and selection using multiple-attribute decision-making techniques. *International Journal of Production Economics*, *103*(1), 90-103.

Gao, W., Tang, W., Wang, and X. (2013). Application of an improved C4.5 Algorithm in performance analysis. *Applied Mechanics and Materials*, *1*,380-384.

Gilboa, I. (2011). *Rational choice.* Cambridge: MIT Press.

Grant, R. (2011). *Contemporary strategy analysis*. New York, NY: Wiley.

Güss, C. D. (2011). Fire and ice: Testing a model on cultural values and complex problem-solving. *Journal of Cross-Cultural Psychology, 42*, 1279–1298

Kapoor, P. and Rani, R. (2015). Efficient decision tree algorithm using J48 and reduced error pruning. *International Journal of Engineering Research and General Science, 3*(3), 1613-1621.

Kaur, G. and Chhabra, A. (2014). Improved j48 classification algorithm for the prediction of diabetes. *International Journal of Computer Applications, 98*(22), 13-17

Križani?, S. (2020). Educational data mining using cluster analysis and decision tree technique: A case study. *International Journal of Engineering Business Management*, *12*, 1847979020908675.

Kumar, A. (2014). Design and applications of decision trees*. International Journal of Computer Science Trends and Technology, 2*(4), 94-98.

Malakooti, B. (2010). Decision-making process: typology, intelligence and optimization. *Journal of Intelligent Manufacturing, 23*(3), 733 – 746.

Osman, M. (2010). Controlling uncertainty: a review of human behaviour in complex dynamic environments. *Psychological Bulletin, 136*, 65–86.

Polatidis, H., Haralambopoulos, D. A., Munda, G., & Vreeker, R. (2006). Selecting an appropriate multi-criteria decision analysis technique for renewable energy planning. *Energy Sources, Part B*, *1*(2), 181-193.

Rausch, E., & Anderson, C. (2011). Enhancing decisions with criteria for quality. *Management Decision, 49*(5), 722 – 733.

Song, Y. and Lu, Y. (2015). Decision tree methods: applications for classification and prediction. *Shanghai Archives of Psychiatry, 27*(2), 130-135.

Ying-Chieh, C., Shui-Chuan, C., & Ying-Hao, C. (2013). Decision quality by the loss cost of Type I and Type II errors. *The TQM Journal,25*(2), 202 – 220.

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