Supply chains that are complex are commonly modeled as linear programs (LPs). They can effectively trade off broad range of criteria. To model FMCG supply chains accurately, one must include discrete aspects of decision making, which requires solving a mixed-integer program (MIP).
It has become significantly important for managers, given the widespread use of linear models today, to be able to develop good, efficient models to aid them in the decision-making process.
Three important factors;
Familiarity,
Simplification, and
Clarity, are to be considered when developing such models.
The greater the modeler’s familiarity with the relationships between competing activities and the limitations of the resources, the greater the likelihood of generating a usable model. Treating the problem from as many perspectives as possible such as various levels, horizontal and vertical helps in this regard.
Linear models are always simplifications …show more content…
What is the maximum value the queues attain? Can we control the distribution such that we achieve a maximal outflow? What is the maximal load of the factory? How long does it take to process a certain type of snack or cookie? The question we ask is how to control the flow through the network so that a maximum number of goods can be produced and storage costs are minimized. In a simulation only the product flow propagates according to user-defined distribution rates. The simplest choice is equally distributed rates i.e. the arriving goods are consistently fed in parallel machines. However, this may result in an efficient load of queues and does not guarantee the greatest possible output. Unlike this way, optimization procedures seek to operate the production network at minimum queue costs and high output. In particular, for time varying inflows of raw materials and highly connected suppliers this leads to a challenging mathematical
It has become significantly important for managers, given the widespread use of linear models today, to be able to develop good, efficient models to aid them in the decision-making process.
Three important factors;
Familiarity,
Simplification, and
Clarity, are to be considered when developing such models.
The greater the modeler’s familiarity with the relationships between competing activities and the limitations of the resources, the greater the likelihood of generating a usable model. Treating the problem from as many perspectives as possible such as various levels, horizontal and vertical helps in this regard.
Linear models are always simplifications …show more content…
What is the maximum value the queues attain? Can we control the distribution such that we achieve a maximal outflow? What is the maximal load of the factory? How long does it take to process a certain type of snack or cookie? The question we ask is how to control the flow through the network so that a maximum number of goods can be produced and storage costs are minimized. In a simulation only the product flow propagates according to user-defined distribution rates. The simplest choice is equally distributed rates i.e. the arriving goods are consistently fed in parallel machines. However, this may result in an efficient load of queues and does not guarantee the greatest possible output. Unlike this way, optimization procedures seek to operate the production network at minimum queue costs and high output. In particular, for time varying inflows of raw materials and highly connected suppliers this leads to a challenging mathematical