industrial case: frigoglass company's customer base consists of the coca-cola company bottlers (such...

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INDUSTRIAL CASE: FRIGOGLASS

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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