URL study guide
https://tue.osiris-student.nl/onderwijscatalogus/extern/cursus?cursuscode=1CV60&collegejaar=2025&taal=enOmschrijving
Data analysis and mathematical modelling to support decision making when designing and controlling business processes.
The following topics with stochastic component will be covered in the course:
The following topics with stochastic component will be covered in the course:
- Forecasting
- Inventory Control subject to uncertain demand
- Forecast-based inventory control when demand is uncertain
- Process performance based on uncertain processing times and demand for single resource:
- Cycle time calculations
- Lead time setting
- Batching
- Safety stock calculation
The course builds upon prior knowledge in the area of Data analytics for engineers (2IAB0) and more particular in deterministic environments within the Operations Management domain (1CV00), basic knowledge and skills on topics like mathematical functions, limits, integration, differentiation, (2WBB0), probability distributions, expectation and variance of (functions of) stochastic variables, discrete and continuous stochastic variables (2DD40 Mathematics 1 and 2DD80 Statistics), queueing theory, Markov processes and optimization (2DD50 Mathematics 2) and Fundamentals of algorithmic programming (1BK60).
Doelstellingen
At the end of this course students should be able to:
- Select a suitable forecasting model and analyse and parameterise the model for a system with stochastic demand.
- Analyse and describe the relevant characteristics of an inventory system: demand process, delivery process, inventory control logic, costs and operational customer service.
- Construct a simple decision support system for an inventory control system in a stochastic environment based on a qualitative description of the system and implement it in Python or another programming environment.
- Apply elementary probability distributions in the context of stationary demand, esp. Determine the average and standard deviation of the demand during a (stochastic) time interval, as well as the applicability and fit of distribution functions given empirical data
- Derive, analyse and optimise single product single stockpoint models in stochastic environments base don case descriptions
- To reflect on the output of a toolbox, which calculates the performance of inventory systems
- To reflect on the applicability of the theory when designing inventory control systems.
- Quantify and manage for a production system with limited capacity the effects of variability on the process performance.
- Quantify for a production system with limited capacity the effects of variability on the system’s effective capacity
- Determine cycle times, norm lead times, lot-sizes and safety stocks in a system with demand uncertainty and a single production unit with stochastic processing times and lead times.