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INDUSTRIAL CASE: FRIGOGLASS
• Commercial refrigeration (Cool Division)
– Design and manufacturing of Ice Cold Merchandisers
• Packaging
Production of:
– Glass containers
– Plastic closures
– Metal crowns
Frigoglass is a Hellenic-based global corporation,
specializing in the design, manufacture and
marketing of Ice Cold Merchandisers (ICMs) and
the production of Glass Containers.
FRIGOGLASS Group Activities
Company profile
COMMERCIAL
REFRIGERATION
USA S. AFRICA GREECE RUSSIA INDIA INDONESIA TURKEY NIGERIA ROMANIA CHINA
GLASS
CLOSURES
CROWNS
PLASTICS
Production Facilities
The company's customer base consists of the Coca-Cola Company Bottlers
(such as Coca-Cola Hellenic, Coca-Cola Enterprises, BIG, Coca-Cola Amatil,
Coca-Cola Sabco), Brewers (such as Heineken, SABMiller, Carlsberg, ABInbev,
Diageo and Efes), Pepsi, Dairy companies (Nestle, Danone) and many others.
Company profile
FRIGOGLASS Reconfiguration
Initial Design
Performance Analysis
Decision Making
Short Term Scheduling
FRIGOGLASS
Reconfiguration Scenario
Introduction of
Activator 700 to Kato Achaia Plant
AS-IS situation
Activities Alternative Production Line Configurations
Preparation
Definition of material and information flow
Processes Determination
Resources Allocation
Initial Design
Performance Analysis
Decision Making
Short Term Scheduling
Problems: Restricted number of proposed alternatives
Only the experience of the engineers is
utilized
Restricted Communication between design
departments (Data Exchange)
Increased Design Time
AS-IS situation
Activities Evaluation of design alternatives performance
In terms of Key Performance Indicators
such as:
Flowtime
Throughput
Operation Cost
Problems: Empirical calculations of Key Performance
Indicators using Rule of Thumbs
Reduced accuracy of KPIs estimation
Analysis do not take into account different
demand scenarios and market volatility
Initial Design
Performance Analysis
Decision Making
Short Term Scheduling
AS-IS situation
Activities
Selection of the proposed and assessed
alternative production line configurations that
will be implemented
Problems: Not a systematic way of selecting between
alternative solutions
A big amount of data to be analyzed and
presented
Initial Design
Performance Analysis
Decision Making
Short Term Scheduling
AS-IS situation
Activities Tasks assignment to the resources of the
selected production line configuration.
Aim of scheduling optimizing KPIs such as
Makespan
Resources Utilization
Tardiness
Problems: Implementation of simplistic solutions that are
only feasible but not optimized
Not efficient utilization of the available
resources
Increased lead time and inventory
Initial Design
Performance Analysis
Decision Making
Short Term Scheduling
Current weakness/needs
Scarce reuse of past production line configurations solutions 1
2
Detailed Performance Analysis of High Accuracy employing
advanced Simulation Tools & Systematic Decision Making 2
1
3
3
4
Poor and analysis of performance evaluation based on rules
of thumb and engineers’ experience
Capturing, Storage &Reuse of Past Knowledge in the form of
production line configurations using Knowledge Management.
Increase the interoperability between design and planning
tools
Optimized short scheduling of resources in terms of Time and
Cost Key Performance Indicators
Limited data exchange between the virtual tools of the
company
Weaknesses
Needs
FRIGOGLAS Reconfiguration Scenario:
• Reconfiguration of Kato Achaia Plant
due to the introduction of a new product
• Knowledge Reuse, Performance
Analysis, Decision Making and
Scheduling.
• Support and improvement of the
FRIGOGLASS reconfiguration
planning:
• Use of new and integrated VFF
modules and their functionalities
• Integration with the Virtual Factory
Framework
TO-BE situation: the scenario
VF
Manager
Knowledge
Association Engine
(LMS & CASP)
IMPACT
(LMS & CASP)
WITNESS
(LMS & CASP)
SVCP-Module
(WZL)
Decision
Support Module
(CASP & LMS)
Initial Design
Rough
Performance
Analysis
Detailed
Performance
Analysis Decision
Making
Scheduling
Demo: the modules
Pilot presentation
EASY REACH EASY REACH Express
FV650 FVS1000 FVS1200
Activator700
EASY REACH EASY REACH Express
FV650 FVS1000 FVS1200
Activator 700
Introduction Kato Achaia Plant Reconfiguration
Storyline
Storyline
Industrial Engineering Manager downloads the alternative production lines and quickly analyzes their performance with SVCP. Two of them are selected since they outperform significantly among the four solutions.
Head of Process Redesign defines for the Target KPIs and the
Constraints for the new production line. KAE provides him with
past production lines, that constitute the basis for the design of
the four alternative configurations that are stored in the VFM.
Industrial Engineering Manager loads Witness the two
selected production line alternatives and assess their
performance (flowtime, throughput, WIP) in detail.
Initial Design
Performance Analysis
KAE
SVCP
WITNESS
Storyline
Production Manager downloads the selected production line configuration defines the workload, the dispatching rules or the performance indicators to be optimized (makespan, tardiness, utilization) and starts scheduling. IMPACT provides the Gantt chart and the KPIs of the proposed schedule.
The KPIs of the two selected alternative production lines are
loaded to DSM. Head of Process Redesign, Industrial
Engineer Manager and Production Manager define the
weighting factors and select the production line to be
implemented.
Decision Making
Short Term Scheduling
DSM
IMPACT
Benefits:
Reduced Time & Cost for Reconfiguration
Planning Advanced planning tools
Interoperability of planning tools within
Integrated Framework
Collaborative Planning
Optimized planning results Dynamic and detailed performance analysis of
production lines
Accurate KPIs estimations
Improved production lines scheduling
Knowledge Reuse Reuse past similar production lines
and configurations
Standardization
Benefits
FRIGOGLASS Reconfiguration Results
and Conclusion:
• Reconfiguration and optimization of
Frigoglass production line
• Re-Use of Past Knowledge for Initial
Design
• Optimization of Production Line KPIs
with advanced discrete event simulation
tools
• Increasing production lines performance
through efficient scheduling
• Interoperability of modules through the
use of the VFF
Conclusions