Supply Chain Simulation

Overview

Supply Chain Simulation is a discrete event simulation that demonstrates or predicts “how” a given supply chain network or process will perform with demand and supply variability and operations constraints.  On-time customer deliveries, fill rates, back-orders, transportation asset utilization, unit manufacturing times are a few of the many metrics and outputs from the simulation.   

Practical applications for network simulation include:

Evaluate the results from a Network Optimization
Supply chain risk analysis
Inventory analysis
Process Improvement
Bottleneck analysis


Business Problem

Are you experiencing these business challenges?  Do these questions sound familiar?

Common Business Challenges

Supply chain risk; disruptions in supply or rapid changes in demand
Need to understand customer service implications of changing supply chain design
Demand or supply distribution not reflective of real variability
Highly volatile demand or seasonality
Extended and variable supplier lead times
Erratic fluctuations in inventory levels
Inconsistent or low fill rates and increasing stock-outs
Limited transportation fleet capacity
Poor customer service or late transportation deliveries 

Key Questions for Consideration

What is the potential cost and service impact of an unexpected supply disruption?  Demand spike?
Can the alternative network design continue to support or improve customer service levels?
How does the simulation compare when I model actual historic demand (order history) compared to calculated demand variability?
What impact does fulfillment or transportation lead time variability have on my fill rate or service level?
What are my peaks and troughs in inventory levels?  When do they occur?
How much inventory storage space do I need this year?  When will I need additional capacity?


Service Solution

Supply Chain Acuity’s Supply Chain Simulation service helps our clients validate existing or proposed network structures and processes by predicting performance and customer service levels.  We use a sophisticated supply chain simulation tool to build and run discrete event simulation models to study a variety of practical applications listed in the overview section.   We examine how key replenishment, transportation and manufacturing sourcing policies and large demand fluctuations impact actual customer service metrics such as fill rate or on-time performance, inventory levels over time, and detailed financial results.  Demand can be modeled either using actual order history or a demand pattern with a variability distribution.   

Delivered Benefits and Desired Outcomes

The power to holistically understand the impact of supply chain policies and variability on supply chain performance across a multi-echelon supply chain network and identify improvement opportunities
Ability to model and quickly run “What-If” scenarios to understand key supply chain processes and predict customer service levels
Validate network optimization results
Validate demand distribution assumptions
Validate and predict expected customer service levels and location fill rates
Understand peaks and troughs in inventory levels overtime
Quantify and mitigate supply chain risk