Supply Chain Challenges: Source Beyond MW Capacity - Essay Sample

Introduction

The concept of the supply chain (SC) was first mentioned in 1982 by Oliver and Webber (Das, Maity & Maiti, 2007). Since its introduction, researchers focus on a system consisting of a single warehouse, non-defective product, and predictable demand. These conditions are not practical in a real market. Due to the limited capacity of the market warehouse (MW), the market quickly satisfies while the goods, while the production of the products continues. The state necessitates sourcing of extra storage space near the market in rental warehouses (RW). Hartely discussed a two warehouse model neglecting the cost of transportation from RW to MW while Pakkala and Achary focused on two warehouses and deteriorating products (Das, Maity & Maiti, 2007). This work presents the improvement of the two pieces of research. It extends the usual SC model by developing a realistic collection-production-inventory stock dependent demand under imprecise constraints.

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The Concept of the Model

The new model integrates necessity, possibility, and credibility constraints considering a collection of finite raw materials and a single production-inventory system that redefines and makes precision in the two warehouses depending on the demand (Das, Maity & Maiti, 2007). The academia production system accounts for defective goods, imprecise budget constraints, and relationships. The independent variables include the material and production, while the dependent variable is the rate of collection for different raw materials. Bhunia et al. (2014) notes the screening and reworking on the defective units within known standards and propose the establishment of an optimal policy for reworking on the faulty items with the imprecise resources acting as the constraint. To improve the accuracy of the model, Das et al. (2007) narrow the model to an equivalent deterministic level that minimizes total cost with imprecise demand through the necessity, possibility and credibility constraints, and integrates the Generalized Reduced Gradient (GRG) technique that sums and solves all the approaches.

The proponents use different numerical tests to qualify their models. Considering the idea of the three constraints in a fuzzy environment, the Weighted Possibility and Necessity (WPN) takes the sum of the product of the existing fuzzy quantities and product of the investors' attitude index and the fuzzy numbers. When investors' attitude index is half the expected level, the results of the expression equates to credibility level. The reliability of the feasibility tests for the investment occurs for a single objective problem under the minimum value (Das, Maity & Maiti, 2007). The model indicates the dependence of optimal production, stock levels, and stock turnover. The expected total cost (ETC) for the investment takes the summation of all expenses, which include transportation, holding, screening, reworking, and suppliers' maintenance costs (Das, Maity & Maiti, 2007).

Das et al. (2007) model provides for an expression that stabilizes the imprecise condition. The quotient of ETC less the crisp objective level in terms of the possibility and necessity (Z) by the difference between ETC at two periods under consideration indicates the effect of the fuzzy state on the investment. The model assumes a non-negative slack variable to the inequality constraints to obtain a practical GRG. The three academia integrates the above numerical ideas into an expression that indicates the demand for goods and services as a factor of other fuzzy and imprecise quantities (Das, Maity & Maiti, 2007). This expression allows investors to control their optimum results. The authors also considered the sensitivity of the prevailing production conditions. The sensitivity expression indicates that an increase in the maximum production capacity leads to a corresponding decrease in ETC given that demand, necessity or probability remains constant. Bhunia et al. (2014) also indicate that an optimistic attitude of the decision-makers leads to an increase in the total production costs.

Practical Application

The model represents an actual case in the real business world. For instance, if a given manufacturer ventures into the production of fashionable products like plastic goods, the capital for this investment is fuzzy due to the fluctuating demand and production costs (Bhunia, Jaggi, Sharma & Sharma, 2014). Assuming the business averagely needs about $500, the fuzzy state means that the investor should consider the possibility of using 525 if everything gets worse or $475 if the investors decide to only deal in what is necessary. These possible spendings create a triangular membership function of 475, 500, and 525. In this case, ETC ranges from $475 to $525.

In the sense of necessity, ETC falls within (475, 500) while the possibility sense, it falls within (500, 525). The variation results from the need for different raw materials, including plastics and color. The goods are first stored in a market warehouse until they fill their capacity. The investors will then rent supplemental warehouse near the market that fills any instance of new demand that strains the market warehouses (Bhunia, Jaggi, Sharma & Sharma, 2014). In such a case, the investor transfers the rented goods to the market warehouse to meet the demand.

Question

Considering the general formulation and analysis of this model, is it possible to modify it to simultaneously account for imprecise defectiveness and price discount? Please expound.

References

Bhunia, A. K., Jaggi, C. K., Sharma, A., & Sharma, R. (2014). A two-warehouse inventory model for deteriorating items under permissible delay in payment with partial backlogging. Applied Mathematics and Computation, 232, 1125-1137. https://www.sciencedirect.com/science/article/pii/S009630031400160X

Das, B., Maity, K., & Maiti, M. (2007). A two warehouse supply-chain model under possibility/necessity/credibility measures. Mathematical and Computer Modelling, 46(3-4), 398-409. https://www.sciencedirect.com/science/article/pii/S0895717706004043