Managing Uncertainty in Production Planning a Real Option Approach Multiple Chapters

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Managing Uncertainty in Production Planning: A Real Option Approach

Risk and uncertainty are inherently part of the business world that affects all parts of production planning. Furthermore, such uncertainty is by no means a simple phenomenon. There are complex issues inherent in risk and uncertainty, which affect the models most usefully applied in production planning. Further complicating the issue is also that all uncertainty and risk factors are unique for each industry. There is therefore no global risk assessment or uncertainty model that can be universally applied with uniform effectiveness. Businesses can however benefit from generalizations, such as the fact that a lack of attention to uncertainty in planning increases the risk of losing in terms of time, money, or quality. Hence, an examination of how risks can best be mitigated via uncertainty models and production planning is usefully applied, even if this needs to be modified for the specific industry in question.

Mula, Poler and Garcia-Sabeter (2007: 783) for example emphasize that general uncertainty factors within the industrial decision environment include market demand, capacity data and cost information. The authors have gone further by qualifying the various manifestations of uncertainty in terms of three types, namely randomness, fuzziness, and lack of knowledge (also known as epistemic uncertainty.Download full Download Microsoft Word File
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TOPIC: Multiple Chapters on Managing Uncertainty in Production Planning a Real Option Approach Assignment

Yang (2009: 22) in turn focuses on the automotive manufacturing industry to demonstrate the critical necessity of including uncertainty in the production planning process. This industry lends itself well to the investigation of uncertainty contingencies, as it is a capital intensive and slow to change. These factors make constructive and effective planning essential. At the same time, the vast scale and scope of the industry bring unique challenges to the planning process. Specifically, uncertainty in this industry relates to variation in terms of product design and its related market demand. The problem is that manufacturing planning and design decisions are needed years before the start of actual production, and before market demand can be known. Market uncertainty remains and evolves even after production. Hence, the author maintains that uncertainty must necessarily be part of the planning and design processes within this industry. This needs to relate to all elements within the production sequence, including supply chain management.

Supply chain management lies at the basis of production planning, and should therefore also be at the heart of contingency planning and uncertainty risk. Snyder, Daskin and Teo (2004: 2) for example state that design decisions made within the supply chain network are necessarily costly and difficult to reverse. Part of the inherent difficulty here is the fact that costs, demands, distances and lead times can undergo significant changes once design decisions are in effect. This could lead to the risk of economic losses as a result of not properly planning for contingencies related to such uncertainties within the supply chain.

In order to plan effectively for such eventualities, the authors note that the traditional approach has been to either focus on the strategic aspects of supply chain design such as facility location or its tactical aspects such as inventory management. This approach does not however consider both of these factors simultaneously. In strategic models, parameters then tend to be treated as deterministic, while the tendency within tactical models have been to assume that strategic decisions are already made.

The shortcomings of these singular approaches have however been mitigated with the rise of a new model that includes both the strategic and tactical approaches within a single modeling platform, the location model with risk pooling (LMRP), developed by Shen, Coullard and Daskin (cited by Snyder, Daskin and Teo 2). The shortcoming of this model is however that it does not account for the changing nature of the supply chain environment, and can therefore not adequately take into account the full implications of the risks and uncertainties involved.

According to Gupta and Maranas (2003: 1219), this is a problem in the current climate of increasing competitive pressures on a global scale. For this reason, supply chain planning as the basis of production planning has become the highlight of business practice for most industries. In order to demonstrate this point, the authors focus on the chemical industry.

What makes uncertainty modeling particularly complex within this industry is the fact of often conflicting objectives within the various business divisions in terms of marketing, distribution, planning, manufacturing and purchasing. In order to mitigate the complexities and conflicts that contribute to the uncertainty factor, the authors suggest that all these functionalities should integrate within supply chain planning. Focusing on this element will also coordinate and integrate the key business activities of the most complex business environment. Appropriate tactical models then need to be integrated with the planning process in order to mitigate eventualities and uncertainties relating to the various functions within the supply chain.

Rosenblit, Ben-Tal and Galany (2010: 2) in turn focus their attention upon a Robust Optimization (RO) methodology to solve problems relating to demand uncertainty and the manufacturing process. In this model, production decisions are led by the trends within previous production periods, where surplus or shortfall are used to calculate probabilities for future market demands. Costs are then incurred in terms of holding or shortage unit costs as a result of surplus or shortage in response to actual market demands. The benefit of this model is that it provides a quantification of uncertainty that can be used as a guideline. This can then minimize the probability of extreme surplus or shortfall problems, unless unforeseen eventualities occur to drive such deviations.

Kazaz, Dada and Moskowitz (2005: 1101) provide an explanation of how the RO model tends to be used by global electronics manufacturers specifically, and multinational companies in general. For electronic manufacturers, the component manufacturing division for example receives a computer-generated projection of future customer demands for specific components. This is the first stage of the RO methodology. The projections are then used to plan for the maximization of expected profits by manufacturing an optimal amount of each component for each plant. The production plan is then accompanied by budgets and transfer prices for sales in the various countries where they operate. This then includes the uncertainty factor of fluctuating foreign exchange rates. While these fluctuations are generally not major on a day-to-day basis, uncertain market conditions could cause sudden spikes or falls.

According to the authors, the usual approach to this problem is to use historical values to indicate the likely exchange rate values for the future. Uncertainty parameters are then replaced by these projected values, like the case is with any other domestic uncertainty factor. The problem is that such a uniform approach does not account for the fact that some exchange-rate realizations are known before allocation decisions are made. Hence, the adverse effect of such fluctuations are not managed adequately. A specific model is then needed for specific global factors such as the exchange rate problem.

Alonso-Ayuso et al. (2003: 97) mention that the uncertainty factor is one among four key aspects of supply chain management challenges, in which the main uncertainties are product demand and price, raw material supply cost, and production cost. They cite various authors that have made modeling suggestions to handle these contingencies. Most importantly, such planning must occur in terms of multiple integrated elements in order to address all the contingencies and complexities involved. Because businesses today are complex, their supply chains must be managed with attention to their nature as multi-level phenomena. Hence, uncertainty management must include a variety of factors from multiple perspectives.

Research Problem

In the light of the above, the research problem can then be said to revolve around the best possible general model that can be developed for managing uncertainty in production planning. Clearly, no author or business person would deny that uncertainty planning is important, and indeed vital to production planning. Without taking uncertainty into account, production problems in terms of quality, cost, and quantity will most likely occur to some degree. It is therefore impossible to simply state the necessity of accounting for uncertainty within production planning as a research problem.

However, a significant problem does relate to the complexity of the issues involved. In production planning, as seen above, uncertainty has many different aspects. Uncertainty in the supply chain for example relates to supply, raw materials, and supplier reliability, while production uncertainty itself relates to market demands, manufacturing costs. Post-production uncertainty relates to the possible surplus or shortage of products, the exchange rates, and other related factors. In addition to the variety of global industries, the challenge is then to create a single model that would be generic enough to include many contingencies and as many possible industries as possible.

Developing such a model should therefore benefit the most manufacturers and industries possible, who should be able to use the model by simply making relevant modifications in order to fit their industry precisely.

In short, the research problem is then to find a suitable model to optimally mitigate the uncertainty in the production process of multiple industries, where such a model… [END OF PREVIEW] . . . READ MORE

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