Shock-robust Design of Plants and their Supply Chain Networks

Robust Production Planning Use-case

SOLU

TIO

NReduced complexity

Optimization models

Analytical functions

Detailed, realistic analysis

Robust solutions

Machinery segment Results

Problem decomposition

Final assembly

Capacity functionsModule

demands Inventory

Orders (ERP)

Lot sizes and headcountsControl policies

MES log data

Data processing

Lot sizes andoperator control

Simulation of future scenarios

Data analysis models

Production planning

Lot-sizing and capacity planning

PROBLE

M

Within the use case, the objective was to increase the robustness of the production plans, focusing on the processes in a single plant.The production environment is a multi-stage system with machinery and assembly segments, producing several different product variants. In order to sustain the service level expected by the customers, robust plans are required to avoid the negative effects of possible changes and distrubances.

• Unbalanced workloads• High inventories • Capacity calculation issues• Changing market conditions• Large product portfolio• Complex material flows• Pull production strategy

RES

ULT

S

Idle times-14%

Backlogs-11%

Productivity+6%

Robustness+15%

Comprehensive planning workflow

Integration in existing processes

Analysis of alternative solutions

Reduced planning efforts

Shorter response time

Shock-robust Design of Plants and their Supply Chain Networks

Project Summary

The overall platform, the supporting tools and methods defined and implemented within the Robust-PlaNet project aim at supporting the decision makers at different hierarchical levels of production enter-prises to react or even to proactively plan and manage their activities in a more robust way than it used to be in the traditional practice.

Robustness in RobustPlaNet involves refined approaches that aim at handling both predictable and unpredictable changes and disturbances, responding to the occurrence of uncertain events (reactive approaches) or protecting the performance of the production plans by anticipating the occurrence of uncertain events (proactive approaches).

RobustPlaNet covers both the operational, the mid-term tactical, and the long-term strategic decision layers and provides a set of new mathematical and simulation based algorithms and tools, which strengthen the planning loops within the operation of industrial partners’ companies of the project.

Simulation and Navigation Cockpit

Built on a workflow based approach, the functional modules are chained together in the RobustPlaNet Simulation and Navigation Cockpit, tailored for different industrial cases. This cockpit constitutes the central web-based framework assisting the final users to define scenarios, construct what-if experiments and evaluate their impacts before a final decision is taken.

Experiment 1Experiment 2

Service 1 Service 2 Service n

Workflow

Cock

pit

cont

rol

Data manipulationService control

Calculation Tools

Discrete-event simulation

Agent-based simulation

Machine learning

Coordination mechanisms

Mathematical modeling

Queueing theory

Experiment n

Data I/O

Model control

Central DatabaseCentral Database

Graphical user interface

Scenario

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