(581f) Improving Supply Chain Management In a Competitive Environment Under Uncertainty
Improving supply chain management in a competitive environment under uncertainty
M. Zamarripaa and A. Espuñaa
aChemical Engineering Department, Universitat Politècnica de Catalunya (UPC), Barcelona, SPAIN
Keywords: Supply Chain Management, Stochastic Programming, MILP- based model
The problem of decision making in the chemical process industry becomes more complex as the scope covered by these decisions is extended. The problem of decision making associated to supply chain (SC) operational management (procurement of raw materials from different markets, allocation of products to different plants and distributing them to different customers), which is attracting the attention of the scientific community in the last years, is, in this sense, on the top level of complexity.
This increasing complexity is additionally complicated by the need to consider a certain degree of uncertainty in the models used to forecast the events that should be considered in this decision making. But although some of the published works in this area explicitly address the problem associated with the uncertainty in the available data, very few of them put the issue in a realistic operational environment, taking into account that the SC of interest will have to compete with other supply chains which, logically, would also like to work as efficiently as possible to cover the same demands on the basis of raw materials obtained in the same (global) markets. But even an efficient behavior should be assumed for these third parties (competitors) their specific way to face demands, suppliers, etc. is, from the point of view of the own interest, a new uncertain element to be considered.
In order to deal with this more realistic problem, this work proposes to introduce into the model information about the expected performance of the competing supply chains (including the SC of interest); such information is entered in the form of scenarios to manage the uncertainty in the optimization model. Based on this information, it is possible to construct a mathematical model (which is clearly non-linear) to solve the problem using stochastic programming, where the variables of the supply chain of interest (raw materials, amount of production, etc.) are the first stage variables, leaving the second stage variables to the transportation and inventory levels according to the behavior of our competitors (previously uncertain), providing a model which correlates the best performance of each of the SC as a function of market conditions which may appear. So the resulting joint model allows to analyze and adapt the performance characteristics of a specific SC (the one of interest for the study) to address the characteristics of the specific operating scenarios (markets for raw materials and products, both subject to uncertainty) taking account of the reaction (presumably optimal, but also subject to uncertainty) of the competitors.
In this regard, the work examines various options which focus on decision making at the operational level (although the methodology can also be applied at the strategic level) among which it is worth to mention:
- The performance of the use of metric robustness, encapsulating the activity of the competitors with the rest of sources of uncertainty.
- The use of reactive approaches, through the definition and implementation of specific efficiency indicators.
- A Multi-Objective asymmetrical approach, where each objective corresponds to one of the competing chains.
The paper also analyzes its comparison with other mathematically more robust systems.