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Business Decision Analysis Of Manufacturing Industry

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Question:

Discuss about the Business Decision Analysis.
 
 

Answer:

Introduction

Decision making is one of the most crucial tasks a manager needs to face. It is one of the few difficulties that they face in their daily tasks. Decision models are powerful as they collect vast data.  Data centric decisions rule out the human errors better.  Commonly, such problems may be – over precision, recency bias etc. Decision models do not have this drawback. They are more objective and impartial in handling data. Let us discuss two of the decision-making models applicable to manufacturing industry. Manufacturing industry depends on processes for manufacturing, a single unit can be a combination of various processes (Hopkin, 2017). The project shall discuss specifically various decision models that could be applicable in process industry. While processes help the organization to achieve its objectives, decisions at different stages assist in providing a route for the processes to travel and gain its goals and objectives (Choi, Chan, & Yue, 2017). The models have been studied in context to paper manufacturing industry.

 

Discussions and Analysis

As opined by Lent et al. (2017), application of business decision modelling is important as it ties the business rules and analytics conducted in a way that reduces risk and improves the end product/service. Application of a decision Management model to effectively yield desired results from it involves:

  1. Building the decision services as part of systems development and implementation
  2. Put in place the right tools and processes to check effectiveness as an ongoing process
  3. Collect requirements in manner that they reveal decisions, it should not be treated as part of process or system requirement

Some of the management processes where decision making is applicable are-production management, human resource management, tactical management, IT management etc. The core business process applicable in the paper manufacturing industry are production and maintenance processes (Njenga et al. 2017). In the production tasks, main decision makers are operators who have the task of combining the right production factors to produce goods and effectively with quality (Rodgers & McFarlin, 2017). Say for example, the manufacturer is introducing a new product to the market, common decision problems faced by him would be time taken for the product to reach market and completeness of production process. He could gain more market share if his products hit the market early. He also needs to keep the production costs reduced and he could do so by investing time to improve the production processes. He is but unaware of the decisions by his competitors to enter the market and when, also, the production process development activities.

This is going to be a stopping problem of the manufacturer. There are two staged problems for him:

Stage 1 – When to introduce the product in the market and improving the production process

Stage 2 – Determining the production quantity and the pricing of the product

Solving such problems require computational methods. The researchers must draw out when the production should be stopped based on the process at each decision epoch. Use of numerical algorithms are used in such cases.

Objectives for building such model are:

  1. It gives a managerial insight. They help them develop strategies to maximize the objectives. Example, stopping a process may be fruitful if the current stock prices are below threshold. (American put option)
  2. Structural result insights help build efficient numerical algorithms to solve stopping problems. Example – a manufacturers pricing decision is based on research based on customers’ buying trends
 


This policy that stops the process when the rewards of stopping are greater is called one step look ahead policy used widely in manufacturing industries (Atadil, Sirakaya-Turk & Decrop, 2017). Researchers try to verify this using the monotone case condition.

Two of the most frequently used models in any production environment are:

The Administrative Model – This is a behavioral approach to decision making. It was proposed by Herbert Simon. In comparison to a classic approach where situations are never ideal, he stressed on explaining how decisions were made in real environments of business (Mora et al. 2017). He pointed out that managers never were fully aware or had information handy to make informed decisions or choices. Rather, they contained limited and simplified versions of problems.  That is the reason why a proper alternative solution cannot be presented by them. He further said that decisions either had a bounded rationality or were satisfying. By bounded rationality, he meant limited mental capacity and emotions. Under the influence of mounting pressure, changes in environment etc. they are only able to take a satisfying decision and never an ideal one.

Another is the Retrospective Decision Model. This essentially follows the same framework with a different objective though.  It focuses on the decision makers’ justifications for action taken up by them. This model checks the choices made intuitively and the actions taken to justify those (Shepherd & Patzelt, 2017). Example could be choice of production of a new model out of various choices that were laid on the table and the decision taken by the management to go forth with production of one model. Even though there is lot of data to back up the choices, decision is usually made intuitively and environment is further created by team to show their actions as justifications of making the right choice per them.

 


Some of the external factors affecting decision making model are:

  1. Market conditions – the market conditions for the organization could affect the decision making process. In the paper manufacturing company for example, there is requirement for new size printable papers needed owing to a revolution in printers (Fisher, 2017). All the printers’ sizes and paper requirements have changed. Subsequently, similar changes need to be incorporated in the product so that market capture is not reduced. This can be treated as an opportunity to introduce it as the new product as well
  2. Economy of the environment – Say due to high import charges, and recent environmental crisis, availability of raw materials has impacted cost. The cost factor if not settled and incorporated into the production process may wreak havoc to the bottom-line
  3. Legislations, rules and policies of Government – For example, Government laid policies to usage of recycled paper strictly, subsequently that would change the industries’ roadmap and cause major changes to production planning and processes subsequently
  4. Customer reactions – demand always affects supply and pricing. If the people’s reaction to pricing of a product does not seem favorable, changes shall happen in production processes subsequently to match the demand

Some of the internal factors which affect the decision making model are-

  1. Policies and procedures in the organization – Organizations mostly have a set of policies and frameworks within which the managers are supposed to perform in certain environments
  2. Hierarchy in the organization – Organizations have more or less hierarchy everywhere and every level possesses different authority levels. That directly has an effect on nature of decisions made
  3. Politics in the organization – behavior by individuals and groups affect a lot of politics in the environment. People use politics to draw favors, influence others for promotions, monetary benefits and many other ways
  4. Perception in the organization – every individual interprets the environment and situations different. A lot of it is influenced by his personal experiences, values, background and interests (Currie & MacLeod, 2017). This also affects how the situation is interpreted by the person and actions and risks taken in decisions. Say, for example if the person is easily affected by incompetence rumors, he may take incorrect decisions and try and influence others in incorrect way to try and justify his actions in process controls and quality processes to prove his competence

Some of the independent variable which affect the dependent variables in a production environment of manufacturing are –production machine hours which may in turn affect the electricity cost which is a dependent variable (Hoelscher et al. 2017). Few others are – skill level of operators, temperature in outside environment, size of the products etc. Some of the dependent variables would be – quality, time taken for production, speed, employee satisfaction etc.

In a decision model like the Retrospective as well as the Classical approach, they assist decision makers to make important production plans and strategies to get achieve organizational goals and objectives.

 

Summary

Verification and validation models are used by manufacturing industries to make proper forecasts with confidence. They reduce the cost, time as well as the risks associated with testing of products or materials. It also provides the decision makers with necessary insights to improvise, work on alternative routes and make important decisions affecting consequences. Verification checks if the model created is right for the process in question. Say for example, for launching the new product, the process prepared for production has been correctly made or not. A mock run would point out the shortcomings of the model. Validation of the model will however point out of the model built is right for product or not. The mock run would yield end products which help in checking whether it met the parameters set for the quality of product (Doxsie, Meyers & Michael, 2017). Verification is achieved by comparing the conception vs what has been created. It asks necessary question that is the process created properly as per concept, are the parameters properly represented? Validation could be checked by proper calibration, comparison of desired vs actual model behavior etc. The process continues till the team gets it right.

 

References

Books

Hopkin, P. (2017). Fundamentals of risk management: understanding, evaluating and implementing effective risk management. Kogan Page Publishers.

Journals

Atadil, H. A., Sirakaya-Turk, E., & Decrop, A. (2017). An Assessment of Decision-Making Styles: An Abstract. In Marketing at the Confluence between Entertainment and Analytics (pp. 817-818). Springer, Cham.

Choi, T. M., Chan, H. K., & Yue, X. (2017). Recent development in big data analytics for business operations and risk management. IEEE transactions on cybernetics, 47(1), 81-92.

Currie, J., & MacLeod, W. B. (2017). Diagnosing Expertise: Human Capital, Decision Making, and Performance among Physicians. Journal of Labor Economics, 35(1), 1-43.

Doxsie, D., Meyers, C., & Michael, A. (2017). Collaborative Decision-making and Outcomes in a Competitive, Simulated, Industry Environment.

Fisher, C. M. (2017). An ounce of prevention or a pound of cure? Two experiments on in-process interventions in decision-making groups. Organizational Behavior and Human Decision Processes, 138, 59-73.

Hoelscher, C. S., Kramer, M. W., Nguyen, C., Cooper, O. D., & Day, E. A. (2017). Decision Making and Communication in a Statewide Interagency Task Force: An Investigation of Planned Versus Utilized Processes. Management Communication Quarterly, 31(1), 39-68.

Lent, R. W., Ireland, G. W., Penn, L. T., Morris, T. R., & Sappington, R. (2017). Sources of self-efficacy and outcome expectations for career exploration and decision-making: A test of the social cognitive model of career self-management. Journal of Vocational Behavior, 99, 107-117.

Mora, M., Wang, F., Gómez, J. M., Rainsinghani, M. S., & Shevchenko, V. S. T. (2017). Decision-Making Support Systems in Quality Management of Higher Education Institutions: A Selective Review. International Journal of Decision Support System Technology (IJDSST), 9(2), 56-79.

Njenga, J. K., Rodello, I. A., Hartl, K., & Jacob, O. (2017, April). Identifying Opportunities and Challenges for Adding Value to Decision-Making in Higher Education Through Academic Analytics. In World Conference on Information Systems and Technologies (pp. 474-480). Springer, Cham.

Rodgers, W., & McFarlin, T. G. (2017). Understanding the Decision-Making Process for Personal Investments. In Decision Making for Personal Investment (pp. 11-15). Springer International Publishing.

Shepherd, D. A., & Patzelt, H. (2017). Researching Entrepreneurial Decision Making. In Trailblazing in Entrepreneurship (pp. 257-285). Springer International Publishing.

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