The main objective of the research then relates closely to the research problem. It is to research the problem of uncertainty as it manifests in the global business environment. Specific issues to be investigated include supply chain management and its related uncertainties, the production process itself and uncertainties related to it, as well as the post-production phase and market uncertainties that are related to it.
Time is also an important factor. Some industries require a long-term time frame in their planning process, which exacerbate uncertainties. The time factor should also be an important consideration in terms of creating a model that can effectively help businesses achieve their manufacturing and revenue goals.
To achieve these aims, the main objective of the research will then be to research industries and companies that operate on a global scale. They will be investigated for the models they have implemented to mitigate risk and uncertainty factors in their supply chain, production, and post-production processes. These models will then be used to create a model that could possibly serve as a generic framework for global businesses to mitigate their risks optimally.
Finally, the research will be consolidated into a report that explicates existing models and how the new model was constructed from elements of these. Existing knowledge serves as a vital springboard for the creation of new and innovative designs; indeed, this is also true of the manufacturing process. Hence, business models, experts, and literature will be consulted for ways in which global business can be optimized in order to minimize the effects of risk and uncertainty.
Indicating the complexity of the issue, authors focusing on production under uncertainty tend to do so from a variety of perspectives and in terms of several respective industries. This complexity is the challenge that this research must face in order to optimize its proposed model for managing uncertainty within production planning.
Mula, Polder and Garcia-Sabeter (2007: 784) address this uncertainty from the platform of fuzzy data. The authors hold that foreseen demand, based as it is upon concrete historical data such as sales, supplies or competition, can be no more than fuzzy, as the future necessarily includes uncertainty. Furthermore, unforeseen events might include manufacturing breakdowns, faulty production or preparation delays. Planning costs are also necessarily fuzzy, as they tend to be valuated by human perception; in itself not an exact or crisp science. Other contingencies, such as overtime, loss of clients and backlogs can only be calculated based on past human experience. This is necessarily subject to human perception and therefore fuzzy.
The prevalence of the fuzzy element in so much of production planning, according to the authors, can be usefully mitigated by means of using fuzzy set theory concomitantly with linear programming. Particularly, the authors suggest a model that uses fuzzy programming through a possibilitistic approach as applied to Material Requirement Planning (MRP) problems. Through this model, the very nature of uncertainty is used in order to create a model that better applies to the necessities of uncertainty mitigation.
Wu and Ierapetritou (2007: 1129) take a more general approach to the issue by examining existing literature on process operations optimization. Because of the importance of the time horizon in optimal planning processes, a large body of literature suggests that planning and scheduling should occur simultaneously in order to minimize uncertainty. In such models, several time-frame and multipurpose approaches are used. One such model for example determined both the optimal duration of the operating cycle and the schedule to be followed during each cycle.
A second major approach in the literature on the issue is a hierarchical decomposition resulting in various planning and scheduling levels. In such models, the time horizon is divided into various time periods, each with its own demands and contingencies to be managed. In complex environments, this serves to simplify and streamline the production process somewhat.
It has already been mentioned that model predictive control is often used to mitigate the uncertainty associated with the production process (Wu and Ierapetritou 1130). This provides projected targets for sales, revenues, manufacturing costs and concomitant market demands.
Snyder, Daskin and Teo (2004: 2-3) handle uncertainty by allowing the parameters in their model to be described by discrete scenarios, each with a specifically assigned probability value. Issues such as distribution center locations, assigning retailers, and setting inventory levees can then occur optimally as a result of such projections. Ultimately, the goal is to minimize the total expected system wide cost. The authors named this model the stochastic location model with risk pooling (SLMRP).
In this model, strategic decisions and tactical decisions are not made simultaneously. Instead, only strategic decisions -- such as facility location -- are made immediately, before uncertainties are known. Tactical decisions -- such as the assignment of retailers and setting inventory levels -- are only made once the uncertainty is resolved. This in itself mitigates uncertainty by fracturing the decision process to match the requirements of uncertainty.
The main advantage of this model is its potential use with various complex supply chain demand problems. Indeed, it appears that this model can be applied across a variety of industries within the global arena.
The main problem under discussion, according to Gupta and Maranas (2003: 1222), is the fact that many decisions must be made regarding optimal operations in the future, but with the availability only of current information. This is where the advantage of the above-mentioned SLMRP model becomes apparent. By dividing the decision process in time before and after uncertainty, some of the uncertainty is mitigated.
According to Gupta and Maranas (2003: 1222), there are two main approaches to mitigate the problem of uncertainty: the scenario-based approach and the distribution-based approach. In the first, scenarios are created that project possible futures for the uncertainties in question. Probability levels are assigned to these scenario levels. These probability levels are then used as the basis of decisions. The main limitation is that all possible futures must be projected. As seen above, this is based upon human perception and experience, which may detract from its potential accuracy and optimal use. In fact, the human element tends to add uncertainty rather than mitigate it.
The distribution-based approach is used when there is not possibility of identifying discrete scenarios. Only a continuous range of potential futures is possible. Each of these then receive a probability distribution. There is no need to forecast possible scenarios in this approach.
Another interesting approach is suggested by Eppler, Platts and Kazncioglu (2006: 5). The author suggest that visualization is a possible way to simplify the manufacturing process and mitigate its uncertainties and problems. This is particularly so in the complex environment of strategizing. Among the many models suggested for minimizing risk, the possibility of visualization provides a platform for better understanding the problems and uncertainties at hand. Where as fuzzy set theory accepts the limitations and non-specific nature of production risks and uncertainties, visualization gives these a concrete form. By facing the challenge of uncertainty in this way, the model provides a concrete form to a non-concrete risk factor. The manager can then better mitigate the risk by graphically manipulating the model and projecting scenarios where the decision-making process can be applied optimally.
Visualization techniques can then be said to be particularly useful in concretizing the uncertainty situation in complex environments such as the global business arena. Although this approach will doubtlessly also have its limitations, such as the possible lack of accuracy in its predictions and mitigation measures, it might in fact be used in conjunction with other models in order to better address the issue.
Many models are available to address the complex issue of production and uncertainty risks. Like various businesses themselves, these models vary in their complexity and scope. By using certain elements from these models in conjunction, a specific business can create a unique platform from which to optimally mitigate its risks and uncertainties. In this regard it is important to recognize that there is probably no single model that can either handle all uncertainties at an optimal level at all times. Indeed, the very nature of uncertainty suggests that this would be a futile endeavor. Instead, the uncertainty model should be required to simply meet the needs of it user.
Fuzzy set theory can for example be used in conjunction with a visualization model in order to better understand the nature of the specific uncertainties involved in a model, business strategy or production effort. Although it has been said that the issue is a complex one, it is therefore not impossible to consolidate relevant literature and knowledge in order to find the best scenario to generally apply to global business.
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Alonso-Ayuso, A., Escudero, L.F., Garin, A., Ortuno, M.T. And Perez, G. An Approach for Strategic Supply Chain Planning under Uncertainty based on Stochastic 0-1 Programming. Journal of Global Optimization, No. 26, 2003. Retrieved from http://chentserver.uwaterloo.ca/aelkamel/che720/che720-methods-of-optimization-pse/stochastic_optimization/05100412180122714.pdf