Supply chain financial model

The product of these two ratios is the supply chain ratio. 

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The value objective function is oriented at the company s profit and loss definitions. Guiding principle is to only use value parameters that can be found in the cost controlling of the company signed by controlling. Penalty costs and without currency and weighting factors being applied to steer optimization results but having no actual financial impact - as it can be often found in supply chain optimization models - do not meet this requirement. 

In this work, a discrete event supply chain is modeled from the point of view of one of the members. The model takes into account uncertainty and it determines an optimal ordering policy so that profit is maximized and financial risk is controlled. Two cases are considered. In one case, uncertain the behavior of the other members of the chain is known while in the other they are not. 

A forecast is not a forecast is not a forecast. As companies work on demand architectures, they will find that they have multiple forecasts—sales forecast, financial forecast, production forecast, supply chain forecast, and procurement forecast—each with a different data model, granularity, and bias. As a result, tight integration is not a good idea and the so-called one-number forecast is not realistic. Instead, as companies work through the issues, they will find the need to model market demand in a ship to or channel data model, and translate this demand to a ship from data model. The sales forecast then becomes an input into the corporate forecast, and this corporate forecast becomes the input into the financial forecast. These concepts require education and are often a major change management issue. 

VaR models were first developed for the financial industry in the early 1990s. They are considered as a standard measure for market risk and used extensively in portfolio risk management. From the financial point of view, VaR measures the maximum possible loss in the market value of a given portfolio. Considering the characteristics of VaR type risks caused by rare events, the concept of VaR can be applied to risk quantification in supply chain management also. 

Finally, we presented a real-world application for designing a resilient global supply chain for a multinational consumer products company. A multi-criteria optimization model explicitly considered conflicting criteria that integrated financial, customer service, supply chain risk and strategic factors of the company. Model results and managerial implications were also presented. 

Ramezani M, Kimiagari AM, Karimi B (2014) Closed-loop supply chain network design a financial approach. Appl Math Model 38 4099-4119 Ratha PC (2014) Towards concurrent product and supply chain designing a review of concepts and practices. Int J Procure Manag 7 391... 

In formula (3) c represents a factor translating human loss into financial terms. Furthermore, we assume that fz< fn< f < fi wid road transportation and inland waterways would lead to the lowest possible financial and human losses, respectively, and railway would lead to the highest financial and human losses. These are just some assumptions that we made without loss of generahty which can be easily changed by the user of the MlSntAC model to fulfill the requirements of a specific chemical supply chain. 

Figure 5.1 A generalised domestic and international supply chain model with information, material and financial flows...

Figure 5.1 shows a generalised domestic and international supply chain model with information, material and financial flows. In this figure, each box with a black border represents supply chain members, and the uncertainty reasons (taken from interview data) are represented by the boxes with white borders. There are three types of flows information (line with square dot), material (solid line) and financial (long dash line) in the model. All information flow related uncertainties are represented by time uncertainties. All material flow related uncertainties are represented by quantity uncertainties. The financial flow related uncertainties are considered in the first objective function (cost). The activities in the early description can be categorised and consolidated into four sub-models that are represented in the SC simulation program. 

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