Functional supply chain models

Consider for example a production unit supplied by one or more raw material [5]. An order cannot be released before aU the required has arrived. That is an order j has an earliest possible starting time (release date rj), a committed shipping date dj, and a priority factor or weight Wj. Every time a machine switches over from one type of item to another, a setup may be required and a setup cost may be incurred. The supply chain model here is composed of a model within each production unit depending on its structure, job shop, flowshop, single machine, etc., and an objective function that links the two models. The objective to be minimized may include the minimization of the total setup times and the total tardiness 7 denoted as,... 

The basic supply chain model was a multi-period MILP. The binary variables identified whether a certain product was produced at a given plant. The continuous variables represented the annual volume in supply, production, and distribution. The objective function included fixed capital expenditure as well as variable costs for supply, production, and distribution. An example model in the case study had 6 plants, 36 products, and 8 sales regions for a 12 year planning horizon. The MILP model had 60,000 variables (2000 binary) and 145,000 constraints. The model was solved using the ILOG/CPLEX solver on a 1.6GHz processor injust4min ... 

Inter-organizational planning of material flows Software that provides multiple data models including the business rules and metrics for the entire supply chain planning process. Algorithms use the business rules and metrics as the drivers for the planning engine 6-Functional supply chain processes (plan) 0 X ... 

Collaborative planning, forecasting, replenishment (CPFR) Collaborative planning, forecasting and replenishment is a concept that allows collaborative processes across the supply chain, using a set of process and technology models 6-Functional supply chain processes (source and make) X 0 O X... 

Supply chain modeling and visualization system Capability to run simulated full-stream supply/demand balancing for what-if scenarios 6-Functional supply chain processes (plan) X X X... 

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. 

Different types of industries require different characteristics to be taken into account, because in model-based planning the real decision situation must be represented adequately, as the solution will otherwise not provide any benefit. Along the lines of Meyr and Stadtler [3], the characteristics of different supply chains can be classified into functional attributes (procurement type, production type, distribution type, and sales type) and structural attributes (topography of a supply chain, integration, and coordination). 

The case study scenario is modeled using SAP s SAP SCM software package, the supply chain solution within the SAP Business Suite. The SAP SCM solution map in Figure 11.5 shows the complete scope of functionalities offered in the 2005 release. 

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. 

Provimi Pet Food is the pet food division of Provimi, one of the world s largest animal feed manufacturing and commercial companies. The dynamic growth of the company in the last years resulted in the necessity to optimize the supply chain. The supply chain problem has special characteristics such as special logical constraints of homogeneous transport, complex cost function, and large size. The developed mathematical model and computational experiences are presented here. 

An MILP model has been developed for solving the supply chain problem of Provimi Pet Food. The model includes special logical constraints such as stepwise constant cost functions of production and packaging lines, minimum constraints for production, packaging and transportation, and the constraints of homogenous product transport. The solution procedure is developed in AIMMS modeling language. 

In the near future, the main concerns of CAPE will probably focus on issues related to product design (molecular modeling for solving function-property-composition problems), supply chain management (cost reduction of raw materials, effective use of energy and its new sources), and life cycle assessment (mitigation of climate change, process sustainability). 

The production and supply side is analysed mainly using the MOREHyS model (Ball, 2006 Seydel, 2008). MOREHyS is a technology-based (bottom-up), mixed-integer, linear optimization model. The objective function used for the optimization, which is carried out sequentially, is yearly cost minimization for the whole country and the complete supply chain (production to dispensing) in each snapshot. 

The flowshop problem is the simplest structure that resembles a simple supply chain structure. The problem is extensively studied in the literature with various models of different types and efficiencies developed and examined for different objective functions and constraints. Integer programming was one of the first models developed for optimizing flow shops. 

The models have different modehng approaches. One approach deals with independent models at each stage hnked with the objective function and/or delivery requirement. The other is the integrated approach in which the whole chain is considered as a single production unit. Both approaches use traditional scheduling models as a base for modeling in supply chains. 

This model reflects a physics perspective, however this model might not fit a regular supply chain design since objective function employs a second degree penalty for unit distance travelled. However, gravity location model can give insight for potential location areas. Every model is an abstraction of reality that comes with assumptions. This model is no exception. This model does not take into account the physical features of location areas, i.e. mountainous or not, proximity to labor force or required infrastructure etc. 

Graves and Willems [48] provide an example of a network model to determine safety stocks in a supply chain at Eastman Kodak. The supply chain involves a high-end digital camera in which the camera, procured from an outside vendor, came with lens, shutter, and focus functions. The imager, circuit board assembly, and many other parts were assembled and tested. The final product was then moved to a distribution center and shipped against demands. The parts in the camera were classified into two groups, one with a lead time of under 60 days and the other set with a lead time of over 60 days. 

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