This paper presents a detailed exploration of simulation techniques in mathematics, focusing on the application of Markov Chains and Random Walks. Learn how these stochastic models are used to simulate complex processes in customer experience and stock market analysis.
Q1 Markov chain and Random Walk
Markov chain is a mathematical model involving
unstructured circumstances happening with a particular time where the past
influence the future using the present. Markov chain technique is utilized for
modeling a series of activities or events. Markov Chain customer experience
events are structured in such that task has series of events with different
lengths (Brooks et al., 1998). In other
words, it comprise of a sequence of touch pint with the customer such as ads
and emails. Technicality, the transition matrix from the technique has
potentialities to function as quantitative metric of the Customer Relationship
management (CRM) efficiency and to directly involve to the success of all the
touch points.
The Random Walk
is a mathematical model used in the stock analysis. Random walk technique is
used where the variables follow no discernible trend and pans out
randomly. The technique is widely used
in the stock market. Its theory premise is on the assumption that random walking
influence the evolving of the prices of securities in the stock market.
Therefore, for the investors, the best approach is to invest in the market portfolio.
Q2. DES
Discreet-event-simulation is a model that
stimulates the behavior and the performance of the real-life process. The approach is salient for increasing
efficiency, speed and the performance (Bosilj et al., 2017). For the system, DES not only analyzes the behavior
of the system buy also conducts experiments with the adjustment of the system
structure.
Amongst the characteristics
of the DES, it that is a technique for conducting experiment. The attribute is vital for the monitoring and
prediction of the behavior of investments in the stock market. For businesses using the approach it is
possible to predict the start and end of peak and off-peak season to facilitate
sound investment. Also it facilitates devising approaches to mitigate complex
problems. In other words, traders have the ability to predict the performance
of the market during unprecedented times.
Q3. Semi-structured decisions
Decision support
framework is divided into three sub categories: unstructured, semi-structured
and structured decision. Unstructured decision involves three decision phases
(“intelligence, design and choice”) which are not structured, for example,
sourcing for a company venue for end of year meeting. Structured decision
involves phases that are follows a particular order, for example, a company
selecting an appropriate investment partner. Finally, semi structured decisions
involves combination of structured and unstructured problems and elements. For
example, a company setting promotion budget for a new product.
Semi-structured
decision entails the following controls: Strategic planning, management control
and operational control. Strategic planning is central for the business to
longer range of objectives and policies such as production scheduling. For
example the organization may decide to develop a policy that will require them
to choose between sugar and biscuits to produce in the future. The second factor is the managerial control that
encompasses gathering of resources and prudently utilization to attain the goal
of the company. For example, a semi-structured decisions on the acquisition of
the technology to aid and enhance the process of the budget preparation. Lastly
is the operational control factor that is casted on robustly performing the
tasks. For example, the organization planning for the annual compensation of
the employees based on the performance.
References
Bosilj Vukšić, V., Pejić Bach, M., & Tomičić-Pupek,
K. (2017). Utilization of discrete event simulation in business processes
management projects: a literature review. Journal of Information and
Organizational Sciences, 41(2), 137-159.
Brooks, S. P. (1998). Markov Chain Monte Carlo Method
and Its Application. Journal of the Royal Statistical Society. Series D (The
Statistician), 47(1), 69–100. http://www.jstor.org/stable/2988428
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