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AIM 2013 Conference

Advances in

Sustainable Production

Split, Croatia

19-22 September 2013

Professor Jan Ola Strandhagen

Centre Manager SFI Norman

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Norway, Trondheim, NTNU and SINTEF

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SINTEF is the largest independent research organisation in

Scandinavia• Leading expertise in the natural sciences and technology, environment,

health and social science

• 2000 employees from 70 countries

• Annual sales of NOK 3 billion (EUR 400 mill) – customers in more than 60

countries

• A non-commercial research foundation with subsidiaries

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Joint use of laboratories

and equipment

SINTEF staff

teach at NTNUNTNU

personnel work

on SINTEF

projects

Strategic coordination

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Photo: Schrøder, Trondhjem Photo: Mentz Indergaard, NTNU Info

NTNU, Trondheim Norway

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Short CV

• 28 of October 1959

• Master in 1985

• Phd in 1994

• Professor II in Production Logistics 1996

• Full Professor 2010

• Research manager and director in SINTEF 1993-2010

• Innovation Research Centre Manager since 2010

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Photo: Roger Midtstraum

17th most innovative country

Ekofisk PlatformPhoto: Phillips Petroleum Company Norway / Kjetil Alsvik

HDI Index 2009 map

1st place UN Human Development

Index

Ranking by EconomistIntelligence Unit

Norway

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Still in the 50 ties we were among thepoorest in Europe

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The adventourers and arctic explorers

Photo: Statoil

Nansen and Amundsen

Norway

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The Norwegian IndustryHistorical perspective

Kristian Birkeland

(1867–1917)

Rjukan, Photo: Norsk Hydro

Qatalum aluminium site, Photo: Norsk Hydro

Sam

Eyde

(1866-1940)

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The Norwegian Industry:Labour and natural resources

Foto: Jarle Lunde /www.vernegruppa.com

Protected waterfallKvanndalsfossen

Åbødalsvassdraget, Sauda

“Build the country” Campaign Poster

The Norwegian Labour Party, 1945

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- The country of water power

Norway

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Norway: the country of water power

Sunndalsøra Aluminium Plant

Photo: Norsk Hydro

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Photo: Teknisk Ukeblad

Ulstein: Selling design as a product

- ship design sold to Brazil

Norway: a country of innovation and competence

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Production Management Research Group;Department of Production and Quality Engineering;

Faculty of Engineering Science and Technology;Norwegian University of Science and Technology

Institutt for produksjons- og kvalitetsteknikk

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Lean production

Supply chain management

Production strategy

ICT in logistics

Sustainable logistics

Production management

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Research Staff

Associate Professor Erlend Alfnes

Professor Heidi C. Dreyer

Professor Jan Ola Strandhagen

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Institutt for produksjons- og kvalitetsteknikk

VisionVision:An internationallyrenowned & recognisedresearch group in production management

Mission: To educate MSc & PhD, and carry out fundamental, innovation-based researchin production management in close cooperation withindustrial and internationalpartners

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Towards sustainable industrial value chains

Productionstrategy

Lean production

Supply chainmanagement

ICT

Planning & Control

Principles

Models

Tools

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Master programmes

• Product Development and Manufacturing• 5years approx 6-10 student per year

• Global Manufacturing Management• ( 2years), approx 10 students per year

• ICT in Engineering

• ( 2years), approx 3-5 students per year

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Master courses

• Production management (JOS)

• Production logistics (JOS)

• Production strategy (EA)

• Supply chain management (HCD)

• ERP/PLM systems (EA)

• Specialisation course (final year for master) (JOS)

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Phd courses

• Advanced planning and analysis (JOS)

• Sustainable logistics (JOS)

• Supply chains; analysis and desgin (HCD)

• Manufacturing strategy (EA)

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Continued education

• Lean production (JOS)

• ICT based production Management (JOS)

• Supply chains Management (HCD)

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Centre for Research-based Innovation (SFI)

Norwegian Manufacturing future

NORMAN

8 years – 25 mill Euro

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Main objective

Develop new and multi-disciplinary research on

next-generation manufacturing, and create

theories, methods, models, and management

tools that enable Norwegian manufacturers to

succeed in global competition.

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SINTEF Technology and Society

SINTEF Raufoss Manufacturing AS

IPK – Production and Quality Engineering

IPM – Engineering Design and Materials

IØT – Industrial Economics and Technology Management

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Research area 2: Operations Managementin Norwegian Manufacturing

Research area 1: Advanced Manufacturing Technology

Research area 3: Product and Process Development

WP IRC Industrial Research Coordination (IRC)

WP AB Advisory Board

WP CM Centre management

Collaboration with relevant projects and networks

WP N Norwegian Manufacturing Future (from 2013)

PhDs and Post.docs

Norman 2011 - 2014

Use cases&Demonstrators

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An enterprise mapping,

analysis and design tool:

The operations model

Department of Production and Quality Engineering,Norwegian University of Science and Technology, NTNU

Erlend Alfnes, Ola Strandhagen and Heidi Dreyer

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The operations model

Model-driven approach to improve enterprise operations

Reengineering of operational processes

Tools and procedures for extensive mapping and

modelling

Includes also methods and tools for analysis, design

and implementation

Processes

Proc. diagrams

Resources

Layout & maps

Materials

Flow diagram

Information

Flow diagram

Organisation

Org. map

Control

Control model

MPC

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Operations model-set

Processes

Proc. diagrams

Resources

Layout & maps

Materials

Flow diagram

Information

Flow diagram

Organisation

Org. map

Control

Control model

MPC

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Control Model: Hagen Furniture

Control Area 1:

Reorder Point

Control Area 4: Customer Order

Control Area 2: Customer Order

Fabrication Comp AFinishing, Prod 1, Stryn

Fabrication Comp B

PurchasingOrder mgmt/

constructionPlanning and control

Finishing, Prod 2, Tr.heim

CustomersCustomers

Customers

Supplliers

Supplliers

Supplliers

Work orderInventory

LevelPurchase

Order Customer order

Pick, pack

& ship order

Finishing, Prod 2, Gjøvik

Control Area 3: KANBAN

Fabrication Comp C

Fabrication Comp D

Work order

CODP2

CODP1

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Raufoss Technology1999-2000: Life time contract (-> 2007) with General Motors (GM) for Epsilon platform (Vectra, SAAB 93, Vauxhall, + US brands)

750 000 cars per year 3 mill pieces per year

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RCT Extended Enterprise

Rawmaterials

Production

and assembly

Component

productionTransport Transport Final assembly

PRODUCERSUPPLIERS CUSTOMERSCARRIERCARRIER

Transport RecyclingDispatch

Design coordinationCo-engineering

Customer Order FulfillmentVendor Supply

Product & Process

development

Production control

Business processPhysical process Stock

More detailed and visualised model must be developed

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Web-based Supply Chain Dashboard

• Real-time plans and information• Process descriptions

• KPI`s • ERP-linked

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Production

Assembly

BufferBuffer

SuppliersCustomer

GM

SCMC

Year

plan

Forecast

Delivery

planProd. planStatus

Year

plan

Forecast

Automated into Intranet

Made available to all involved

”Non-disturbance” production control principle

Integrated with ERP-system

Planning concept

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PhD candidates

Institutt for produksjons- og kvalitetsteknikk

Torbjørn Netland:Implementing company-specific production systems (XPSs) in global production networks

Philip Spenhoff:Lean Spare-Part Production in the Automotive Industry

Anita Romsdal:Food supply chains; concepts, principles and guidelines for differentiated production planning and control

Emrah Arica:Real time production, planning and control

Mario Mello:Supply chain management in Global Ship Production network

Pavan Sriram:ETO Windmills

Lukas Chabada:Planning and Control Models in Sustainable Fresh Food Supply Chains

Taravatsadat Nehzati:New Adaptive Intelligent Model for Manufacturing Planning Control

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Production planning and control in food supply chains

Product characteristics

demand flexibility and responsiveness

Market characteristics

demand flexibility and responsiveness

Production system designed for

economies of scale

• Perishability

• Increasing variety and

fresh food sales

• Frequent deliveries

• Short response times

• Demand uncertainty

Technology, equipment

and processes adapted to

high volume, low variety Financed by:

In collaboration

with:

PhD candidate

Anita Romsdal

Problem: Misalignment between external requirements and

internal capabilities of production system

Means: Link different PPC approaches with product and market

characteristics

Objective: Investigate how external requirements can be met through

differentiated production planning and control (PPC)

Outcome: Concept, frameworks, methodologies

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Ordinary sales;

Low demand uncertainty

Market activity;

High demand uncertainty

Make-to-stock

Make-to-order

Findings and proposed solution

PPC in food is becoming more difficult

Increasing complexity, variation and uncertainty

Current mismatch between market requirements and production system

capabilities

Producers use mainly one PPC approach to meet different requirements from

products and markets: make-to-stock

Result: high service levels and responsiveness – at expense of efficiency

Proposed solution:

Combining make-to-stock (MTS) and make-to-order (MTO) to achieve both

responsiveness AND efficiency

Differentiated PPC:

1. Focus PPC resources on products with

low demand predictability MTO

2. Automate PPC for products with

high demand predictability MTS

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Concept and framework for differentiated PPC

(based on Kittipanya-ngam, 2010)

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Decision tree for determination of demand

predictability

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Title: “Effective responsiveness to unscheduled events in production planning and control”

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Research funded by:

Performed in collaboration with:

Research Question How can manufacturing companies effectively reschedule and re-plan

when unscheduled events occur?PhD Candidate: Emrah AricaEffectiveness - timely and appropriate decisions against the disturbance

Appropriate decisions - that are best for the entire organization rather than only for the local unit disturbed by the event

Objectives• Identify the factors that influence the effectiveness of handling

unscheduled events

• Develop a framework for decision-making in the rescheduling and re-planning process, taking the identified factors into account

• Propose a practical decision support model (tool) on the basis of the framework

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Methodology

Literature study

Case study: • Primarily; job shop process

o complex manufacturing environment,

o low volume,

o technologically advanced products,

o large number of high precision machiningoperations with complex routings

o shared resources

• Secondarily;

o The nature of the research problem alsoallows to involve a more stable manufacturingenvironment to investigate the researchquestion and adapt the preliminary findings(continuous process)

Research and practice gap • Traditional academic view; small number of uncertainties and

their time impact are incorporated into models to maketimely re-scheduling and re-planning decisions

• In practice, handling an unexpected event and takingappropriate rescheduling and re-planning decisions entail acomplex organizational process depending on the;

o Underlying cause of the event

o Situation (context) that the event happened

o Full impact of the event, including on both on bothvertically and horizontally related plans

Proposed solution• Integrating event handling and replanning process

• Considering the influence of organizational, human, andtechnological factors in the decision making process

• Facilitating the decision making of the schedulers byidentifying and enabling the required information to makeinformed appropriate decisions

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Preliminary findings

Figure: Event handling and re-planning process

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MSUP Carnaghi

MSUP Carnaghi

MSUP Carnaghi

MSUP Carnaghi

MSUP Forest

MSUP Forest

MSUP Forest

MS SQL

Database

- Control and partly automate celloperations

Production control at the case company

- Monitors and visualizes info from connected cells aboutresources as well as jobstatus

Challenge: Effective responsiveness to real time events/changes due to:

• Lack of evaluation of enterprise-wide status and consequences for appropriate decisions

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Proposed support tool: A real time event advisory tool that enable the requiredinformation to classify the events and evaluate their consequences for informeddecisions in re-scheduling and re-planning process

EVENT INFO

Shift arrangement

Current timePlanned maintenance

Machine status

Available orders & status

Sick leave

Personell competence matrix

Available fixtures

Current manpower Cutting tooling

Order priorization

Operation listing

Informed decision

Y=F(X,Y,Z…)

……

……

……

Time restriction, Actions, Impacts,

Context

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Taravatsadat Nehzati;

Production Planning in Multisite Production Network

Practical problem;

• Essential need for enhancing responsiveness and integration of current production plan in

multi site networks

To do production plan in the network, we need to know:

• Degree of product similarity between plants (Packaging facilities)

• Product type (Shelf life, Customer location, etc)

• Degree of material planning flexibility (Replenishment strategy)

• Available Capacity in the network

Research questions

1. What are the significant elements and challenges in production planning of multi site

production network?

2. How to enhance production plan integration between facilities in production network?

3. How capacity planning and material planning can contribute to improve production plan of

multi-site production network?

Research design:

• Design science; multiple-case and literature study

Research

funded by:

Performed in

collaboration with:

Ce

ntr

al P

lan

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Teaching activities

Strandhagen has scientific responsibility for the following NTNU

courses (master, PhD, cont. ed. (CE)):

Manufacturing Logistics since 1996

Sustainable logistics since 2002

Value Chain Management since 2002 (coresponsible)

PhD course Advanced Logistics since 2001

CE course Value Chain Management (coresponsible)

since 2005

CE course Manufacturing logistics and ICT

since 2005

CE Course Lean Manufacturing since 2011

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