All Posts By

Sriram Parameswaran

Business

Monte Carlo Simulation in Supply Chain Management

Monte Carlo simulation is a statistical technique that involves generating random samples from probability distributions and using these samples to simulate a wide range of possible outcomes. In supply chain management, Monte Carlo simulation can be used to model various aspects of the supply chain, such as demand variability, lead times, and inventory levels. The following steps describe the Monte Carlo math in detail for supply chain management: Define probability distributions: The first step in Monte Carlo simulation is to define the probability distributions for the variables that are uncertain or variable. In supply chain management, these variables can include…
Sriram Parameswaran
May 7, 2023
BusinessIntegrated Business PlanningManagment

Outliers in Demand

Identifying outliers: The first step is to identify the outliers. This can be done manually or by using statistical methods. Some common methods for identifying outliers include: The interquartile range (IQR): The IQR is a measure of the spread of data. To calculate the IQR, first find the first and third quartiles of the data. The IQR is the difference between the third and first quartiles. Data points that are more than 1.5 times the IQR away from the nearest quartile are considered outliers. The z-score: The z-score is a measure of how far a data point is from the…
Sriram Parameswaran
May 3, 2023
BusinessPlanning

Multi Echelon Inventory Optimization Models

Safety stock is a level of inventory that is held to protect against unexpected demand or delays in supply. It is an important part of inventory management, and it can help to prevent stockouts and lost sales. Multi-echelon inventory optimization is the process of determining the optimal level of safety stock to hold at each level of a supply chain. This can be a complex task, as it involves balancing the costs of carrying inventory with the costs of stockouts. There are a number of factors to consider when optimizing multi-echelon safety stock, including: The demand for the product The…
Sriram Parameswaran
May 1, 2023
BusinessPlanning

Why do machine learning algorithms need training data set before predictions?

Training data is a crucial component in the development of machine learning and data science algorithms. It serves as the foundation upon which models are built, and allows the algorithms to learn patterns, relationships, and rules that they can then use to make predictions or classify new data. In more technical terms, training data is used to train a machine learning model, which is essentially a mathematical function that maps input data to output data. During training, the algorithm iteratively adjusts the parameters of the model based on the input data and the desired output, so that it can make…
Sriram Parameswaran
April 26, 2023