Examining Levels of
Automation in the Wood
Processing Industry
Christian Schneider & Oscar Andersson Jönköping 2016-11-18
Postadress: Besöksadress: Telefon:
This thesis has been carried out within the Department of Industrial Organization and Production at the School of Engineering in Jönköping as part of the M.Sc. program Production Development and Management. The authors take full responsibility for the opinions, conclusions and findings presented.
Examiner: Kristina Säfsten
Supervisors: Anette Karltun & Roaa Salim Scope: 30 credits
To our inspiration from the gym,
thank you for your continuous motivation!
Abstract
Companies operating in the wood processing industry need to increase their productivity by implementing automation technologies in their production systems. An increasing global competition and rising raw material prizes challenge their competitiveness. Yet, too extensive automation brings risks such as a deterioration in situation awareness and operator deskilling. The concept of Levels of Automation is generally seen as means to achieve a balanced task allocation between the operators’ skills and competences and the need for automation technology relieving the humans from repetitive or hazardous work activities.
The aim of this thesis was to examine to what extent existing methods for assessing Levels of Automation in production processes are applicable in the wood processing industry when focusing on an improved competitiveness of production systems. This was done by answering the following research questions (RQ):
RQ1: What method is most appropriate to be applied with measuring Levels of Automation in the wood processing industry?
RQ2: How can the measurement of Levels of Automation contribute to an improved competitiveness of the wood processing industry’s production processes?
Literature reviews were used to identify the main characteristics of the wood processing industry affecting its automation potential and appropriate assessment methods for Levels of Automation in order to answer RQ1. When selecting the most suitable method, factors like the relevance to the target industry, application complexity or operational level the method is penetrating were important. The DYNAMO++ method, which covers both a rather quantitative technical-physical and a more qualitative social-cognitive dimension, was seen as most appropriate when taking into account these factors. To answer RQ 2, a case study was undertaken at a major Swedish manufacturer of interior wood products to point out paths how the measurement of Levels of Automation contributes to an improved competitiveness of the wood processing industry. The focus was on the task level on shop floor and concrete improvement suggestions were elaborated after applying the measurement method for Levels of Automation.
Main aspects considered for generalization were enhancements regarding ergonomics in process design and cognitive support tools for shop-floor personnel through task standardization. Furthermore, difficulties regarding the automation of grading and sorting processes due to the heterogeneous material properties of wood argue for a suitable arrangement of human intervention options in terms of work task allocation. The application of a modified version of DYNAMO++ reveals its pros and cons during a case study which covers a high operator involvement in the improvement process and the distinct predisposition of DYNAMO++ to be applied in an assembly system.
Keywords
Hierarchical task analysis, literature review, level of competence, level of information, value stream analysis
Acknowledgement
First and foremost, we would like to offer our most sincere gratitude to our supervisor Roaa Salim for her guidance, patience and valuable help during the course of this thesis. Additionally, special thanks are dedicated to our devoted professor and senior supervisor Anette Karltun for her knowledge and support during the writing process. Her commitment and helpful comments were essential for the completion of this project.
Not to forget, we would like to express our gratitude towards the case company and its staff, who made this project possible. Thank you for all your commitment and support!
Finally, we want to acknowledge professor Mats Winroth for his expertise and guidance during the second phase of the case study.
Contents
Abstract ... iii
Keywords ... iii
Acknowledgement ... iv
Contents ... v
List of abbreviations ... ix
1
Introduction ... 1
1.1 BACKGROUND AND PROBLEM DESCRIPTION ... 1
1.2 AIM AND RESEARCH QUESTIONS ... 2
1.3 DELIMITATIONS ... 3
1.4 THESIS OUTLINE ... 4
2
Theoretical background ... 6
2.1 CHARACTERISTICS OF THE WOOD PROCESSING INDUSTRY ... 6
2.1.1 Quality criteria ... 6
2.1.2 Work place ... 7
2.1.3 Job profile ... 7
2.1.4 Production aspects... 7
2.1.5 Product categories ... 8
2.1.6 Supply chain characteristics ... 8
2.1.7 Factors of competitiveness in the wood processing industry ... 8
2.2 LEVELS OF AUTOMATION ... 11
2.2.1 Challenges with the automation of production processes... 12
2.2.2 What is Levels of Automation? ... 13
2.2.3 Methods for measuring Levels of Automation ... 16
Cognitive Reliability and Error Analysis Method ... 17
The Delphi method ... 17
DYNAMO++ ... 17
KOMPASS ... 20
Rapid Plant Assessment ... 21
Lean Customization Rapid Assessment ... 21
MABA-MABA task allocation ... 21
Productivity Potential Assessment ... 22
Systematic Production Analysis ... 22
Task Evaluation and Analysis Methodology ... 22
TUTKA ... 22
Unit cost related approaches ... 23
2.3 LEVELS OF COMPETENCE ... 23
2.3.1 The SRK model ... 23
2.3.2 Operator roles ... 24
2.4 LEVELS OF INFORMATION ... 25
2.5 HIERARCHICAL TASK ANALYSIS ... 26
2.6 VALUE STREAM ANALYSIS ... 27
3
Research design... 28
3.1 RESEARCH STRATEGIES ... 28
Traditional literature review ... 29
Systematic literature review ... 29
3.1.2 Empirical methods ... 29 Case study ... 29 Applied DYNAMO++ ... 30 Observation ... 31 Document analysis ... 31 Focus group ... 31 3.2 METHOD APPLICATION ... 32
3.2.1 Application of theoretical methods ... 32
Traditional literature review ... 32
Systematic literature review ... 34
3.2.2 Application of empirical methods in a case study company ... 36
Applied DYNAMO++ ... 37
4
Findings and analysis ... 40
4.1 ISSUES WITH AUTOMATION IN THE WOOD PROCESSING INDUSTRY BASED ON THE TRADITIONAL LITERATURE REVIEW ... 40
4.2 SELECTION OF LOA ASSESSMENT METHOD BASED ON THE SYSTEMATIC LITERATURE REVIEW 43 4.3 CASE DESCRIPTION ... 51
4.4 FINDINGS ‘APPLIED DYNAMO++’... 53
4.4.1 Phase I – pre-study ... 53
4.4.2 Phase II – measurement ... 55
4.4.3 Phase III – Analysis ... 58
Workshop ... 58
SoPI matrices ... 61
4.4.4 Phase IV – Implementation ... 62
5
Discussion and conclusion ... 69
5.1 METHOD DISCUSSION ... 69
5.1.1 Discussion of traditional literature review ... 69
5.1.2 Discussion of systematic literature review ... 69
5.1.3 Discussion of ‘Applied DYNAMO++’ ... 70
5.2 DISCUSSION OF FINDINGS ... 72
5.2.1 Research question 1 ... 72
5.2.2 Research question 2 ... 74
5.3 VALIDITY AND RELIABILITY ... 75
5.4 TRIANGULATION ASSESSMENT ... 76
5.4.1 Data triangulation ... 77
5.4.2 Investigator triangulation ... 77
5.4.3 Theory triangulation ... 77
5.4.4 Methodology triangulation ... 77
5.5 SUGGESTED FUTURE RESEARCH... 78
5.6 CONCLUSION ... 78
References ... 80
Figures
Figure 1-1: How the research questions correlate to each other ... 3
Figure 1-2: Overview of the chapter structure with sections linked forming a logical entity ... 5
Figure 2-1: The LoA taxonomy and relevant implications on production characteristics ... 19
Figure 2-2: Task optimization (left) and possible operation optimization ... 20
Figure 2-3: The SRK model ... 24
Figure 2-4: Example of how an HTA is conducted ... 26
Figure 2-5: Steps in the value stream analysis procedure... 27
Figure 2-6: Value stream analysis can be applied in various system levels ... 27
Figure 3-1: Overview of the Applied DYNAMO++ approach ... 30
Figure 4-1: Number of hits for the first section of keywords of the traditional literature review ... 40
Figure 4-2: Number of hits for the second keyword section of the traditional literature review 41 Figure 4-3: Number of hits for the third keyword section of the traditional literature review .... 42
Figure 4-4: Distribution of reviewed papers regarding assessment method ... 44
Figure 4-5: Grouping of assessment methods according to dimensional viewpoint derived from Fasth (2012) ... 47
Figure 4-6: Examples of mouldings with high geometric complexity (left) as in Line B and low (right) as it is the case in Line A ... 51
Figure 4-7: SoPI for rework ... 62
Figure 4-8: SoPI for planing/ feeding ... 62
Figure 4-9: SoPI for primer/ top coat ... 62
Figure 4-10: SoPI for stacking/ plastic wrapping ... 62
Figure 4-11: Layout of a rework loop to improve LoA in material handling ... 64
Figure 4-12: Suggestion 4 - Parallel processing of rework activities with transport conveyors and puttying machine ... 65
Figure 4-13: Schematic layout for a moulding selection according to quality levels before the first processing step ... 66
Tables
Table 2-1: Typical causes of quality costs in the wood processing industry ... 6
Table 2-2: Overview of OEE structure modified according to a case study in wood processing ... 10
Table 2-3: Sheridan's Levels of Automation offers an overview about common degrees of automation ... 14
Table 2-4: The integrated concept of Levels of Mechanization ... 15
Table 2-5: Levels of physical and cognitive automation according to ... 16
Table 2-6: DYNAMO++ overview according to Fasth et al. (2008) ... 18
Table 2-7: The MABA-MABA list ... 21
Table 2-8: Table 2 9: Evaluation matrix illustrating supervisory control roles and Rasmussen’s human behavior levels ... 23
Table 2-9: Model of the relation between conscious and automatic behavior, based on Rasmussen and Vicente ... 24
Table 2-10: Operator roles and tasks ... 25
Table 2-11: Abstraction hierarchy for information requirements ... 26
Table 3-1: Methods used for answering each of the research questions ... 28
Table 3-2: Overview of the research process ... 32
Table 3-3: First keywords used during the traditional literature review... 33
Table 3-4: Keywords comparing the degree of exploration of automation in different manufacturing industries ... 33
Table 3-5: Keywords showing the degree of exploration of the concept Levels of Automation ... 33
Table 3-6: Inclusion/ exclusion criteria for the systematic literature review ... 35
Table 3-7: Overview of author related research (keyword 2) in Google Scholar ... 36
Table 3-8: Overview over the data extraction categories of the reviewed articles ... 36
Table 4-1: Overview of the evaluation criteria used for figuring out the most suitable LoA assessment method ... 45
Table 4-2: Operational and space levels of a factory by Fasth (2012) ... 46
Table 4-3: Comparison of assessment models according to evaluation criteria in table 4-1 .. 48
Table 4-4: Comparing the analysis units line A and B ... 52
Table 4-5: Comparing Line A and B after the Value Stream Mapping ... 54
Table 4-6: Overview how rework is performed depending on operator ... 54
Table 4-7: Scrap data regarding the scanner/ operator interface ... 55
Table 4-8: Comparison operator - machine work task division Line A ... 56
Table 4-9: Comparison operator - machine work task division Line B ... 56
Table 4-10: LoA taxonomy for line A ... 56
Table 4-11: LoA taxonomy for line B ... 56
Table 4-12: LoC matrix for line A ... 57
Table 4-13: LoC matrix for line B ... 57
Table 4-14: LoI matrix for line A ... 57
Table 4-15: LoI matrix for line B ... 57
Table 4-16: Overview of how the processes of line A affected the competitive factors ... 58
Table 4-17: Results from the workshop and the subsequent analysis of future LoA ... 60
Table 4-18: Graphic results from the workshop with relevant minimum and maximum LoA values ... 61
Table 4-19: Schematic Fault - Symptom matrix for the packaging processes ... 68
Appendices
Appendix 1: Characteristics of the wood processing industry ... 85Appendix 2: LoA-, LoC- and LoI taxonomy for line A ... 88
Appendix 3: LoA-, LoC- and LoI taxonomy for line B ... 91
List of abbreviations
AMS Advanced Manufacturing System
CNC Computerized Numerical Control
CREAM Cognitive Reliability and Error Analysis Method
DYNAMO Dynamic automation in manufacturing
FSM Fault Symptom Matrix
HRA Human Reliability Assessment
HTA Hierarchical Task Analysis
KOMPASS Complementary Analysis and Design of Production in
Socio-technical Systems
KPI Key Performance Indicator
LCRA Lean Customization Rapid Assessment
LoA Levels of Automation
LoC Levels of Competence
LoI Levels of Information
MABA Man/ Machines Are Better At
OEE Overall Equipment Effectiveness
PPA Productivity Potential Assessment
REBA Rapid Entire Body Assessment
RPA Rapid Plant Assessment
RULA Rapid Upper Limb Assessment
SoPI Square of Possible Improvements
SPA Systematic Production Analysis
SPC Statistical Process Control
SRK Skill-, Rule- and Knowledge based behavior
TEAM Task Evaluation and Analysis Methodology
TPS Toyota Production System
TUTKA Name of the production system assessment tool derived
from the Finnish “tuotantojärjestelmän kehittäminen ja arviointi”, meaning production system improvement and assessment
VSA Value Stream Analysis
1 Introduction
This chapter explains the background and problem description of the study which is followed by the aim and research questions, the scope of the study and the outline of the report. The reader receives a reasonable overview about the targets in the course of the research process, which delimitations are given and how the thesis is structured.
1.1 Background and problem description
Automation is seen as an efficient way to achieve cost-effective production systems in various industries (Satchell, 1998). In a production context, it is generally extended to work activities and functions that workers cannot perform as accurately and reliable as automated machines (Parasuraman, 2000). Automation can help to achieve enhanced product quality, improved handling of broad product ranges, higher process safety and more efficient resource utilization (Jämsa-Jounela, 2007). With an increasing global competition and off-shoring to low-cost countries, the design of competitive production systems has a high priority for manufacturers, especially in highly industrialized countries like Sweden (Säfsten, Winroth & Stahre, 2007).
Groover (2001) names increased labor productivity and the reduction of labor cost next to the elimination of repetitive routine tasks as reasons for pursuing automation strategies. Yet, the decision for the implementation of automated process technology seems to be rather taken without any support system guiding the decisions (Lindström & Winroth, 2010). Säfsten, Winroth and Stahre (2007) describe cases where initiatives from top management to install automation technologies without linkage to the manufacturing capabilities, such as operators’ skills, have become massive failures.
Sheridan and Parasuraman (2000) present several perspectives on which the decisions for automation can be based. Economic (automation cheaper than human labor), technical (everything gets automated whenever it is technical possible) or humanist perspective (repetitive, risky and boring tasks get automated) are quite common applied criteria. An adaptive approach by Scallen, Hancock and Duley (1995) focuses on human operator workload and situation awareness.
Increasing product customization leads to more complex products and therefore to an increased extent of automation (Youtie, Shapira, Urmanbetova & Wang, 2004; Sheridan, 2002). Yet, as described by Parasuraman (2000), extensive Levels of Automation do not necessarily result in a high performance of production systems. Connors (1998) mentions a strong rise in mental workload due to a more complex situation awareness. In such systems, i.e. complex manufacturing systems, present and future situations have to be understood during the static monitoring of production activities, as a higher probability of catastrophic failure has to be accepted (Frohm, 2008).
The concept of Levels of Automation (LoA) has been discussed as means of achieving a sufficient operator involvement which leads to an improved situation
to implement those technologies according to the motto “all-or-none” (Fasth, 2012). Frohm, Lindström and Bellgran (2005) argue for the LoA concept as tool for achieving the most optimal production system with regard to robustness and flexibility. Following this approach, Frohm, Granell, Winroth and Stahre (2006) name both skilled human workers and technical systems as evident components for highly competitive production processes. The major concern of the LoA approach is to examine the pros and cons of combined human – technique task allocation designs with regard to different automation solutions (Fasth, 2012).
A need for automation as means for increasing competitiveness through increased productivity and product quality is also present in the wood processing industry. A relatively low value of outcome products and value added during manufacturing compared to the metal or other similar industries as well as high labor costs are reasons for that (Sandberg, Vasiri, Trischler & Öhman, 2014). Outsourcing tendencies regarding production facilities in low-cost countries strengthen this view (Schuler & Buehlmann, 2003).
Increases in material cost and difficulties regarding the recruitment of competent personnel put additional pressure on firms within the wood processing industry (Sandberg et al., 2014). As a survey among Swedish wood processing blue-collar workers reveals, a majority names ‘underdeveloped production technology’ as main issue in terms of working conditions (Karltun, 2007). Indeed, a challenge regarding process automation in wood processing industry lies in the anisotropic and variant character of the material (Karltun, 2007). Teischinger (2010) argues for a roadmap of new technologies to be applied in wood processing industry in order to stay competitive.
These factors build up the circumstances of the wood processing industry in which investments in new automation technology have to be thoroughly elaborated based on the specific industry profile and the balancing of operators’ skills and technological capabilities. Measuring Levels of Automation as supportive element to achieve this strategic fit when designing competitive production systems is subject to review in this thesis.
1.2 Aim and research questions
The aim of this thesis was to examine to what extent existing methods for assessing Levels of Automation in production processes are applicable in the wood processing industry when focusing on an improved competitiveness of production systems. The following research questions (RQ) are answered:
RQ1: What method is most appropriate to be applied with measuring Levels of Automation in the wood processing industry?
It is aimed for finding the most suitable method for measuring LoA taking into account the prerequisites regarding the wood processing industry’s ability to automate production processes. Therefore, particularities must be identified which influence the implementation of the LoA concept in this context.
The authors assume that applying a method how to measure LoA in industrial practice represents a contribution for the whole industry, as it is formulated in RQ2.
RQ2: How can the measurement of Levels of Automation contribute to an improved competitiveness of the wood processing industry’s production processes?
Answering the second research question is supposed to point out paths where the application of the LoA concept in the mentioned industry leads to tangible competitiveness enhancements. This is achieved by formulating concrete improvement suggestions after having tested the LoA measurement in the wood processing industry. From these, generalizing conclusions are drawn out based on the Key Performance Indicators (KPIs) which are analyzed and soft factors, which are not directly measurable.
Figure 1-1 below illustrates the relation between the research questions and the contributions which are presented in the research process. A thorough study of each part of the research strategy is therefore highly important as the results of the first research question build a foundation for tackling the second question.
Figure 1-1: How the research questions correlate to each other
1.3 Delimitations
The focus regarding the industrial sector is, as mentioned in the previous part, the wood processing industry. Both the traditional-, and scientific literature reviews focus on scientific publications in English language which are not older than 1990. Regarding the method selection, aspects dealing with automation software and relevant computer simulation are excluded. The method evaluation focuses on production processes in the manufacturing industry, which leaves considerations regarding end customers and their interfaces to automated self-service applications aside.
The testing of a method for measuring Levels of Automation in order to properly answer RQ2 is limited to two production lines in one plant. Furthermore, the relevant level of analysis is on a production level, which indicates that questions regarding management operations are not included in this study.
The study of work tasks and task allocation between operators and machines is seen as crucial for the project as the focus is lying on the assessment of human – technology interaction. This motivates also the fact why product design
RQ1
• Evaluation and selection of a suitable assessment method in the context of the wood processing industry
RQ2
• Examining the potential of the LoA measurement in the wood processing industry with regard to competitiveness improvement in a production system
1.4 Thesis outline
Chapter 2 presents the theoretical foundation of the thesis. This chapter deals with an industry profile of the wood processing industry with focus on the aspect of competitiveness. Furthermore, the concept of Levels of Automation (LoA) is presented in detail together with the assessment methods reviewed for measuring LoA. Level of Competence (LoC), Level of Information (LoI), Hierarchical Task Analysis (HTA) and Value Stream Analysis (VSA) are additional concepts which become important when modifying a method for the case study which is described in subsection 3.1.2.
Chapter 3 deals with a detailed presentation of the research strategy as well as the research methods applied. A separation according to theoretical and empirical methods provides a distinctive classification regarding the means for studying each research question. Literature reviews and case study design are described in order to give an understanding how the overall reasoning is built up. The data collection and analysis described in the method application encompasses various methods which raises validity and reliability of the study.
In chapter 4, the findings from the method application are described. The distinct particularities of the wood processing industry and a well-thought motivation for a selected assessment method as finding from the systematic literature study lay ground for the presentation of the case study results. These are analyzed and presented according to the phase model of the chosen methodology. The chapter is completed with a description of the case study conducted in collaboration with a major Swedish wood processing company.
Chapter 5 deals with a discussion and conclusion of the methods applied within the research strategy and the research questions. Initially the application of the literature reviews is evaluated. Thereafter, the method chosen for the case study is discussed in detail. This is followed by a structured discussion of each research question in terms of the extent they have been answered. After this, the reliability and validity of the thesis work is discussed. The chapter ends with suggestions for further research and concluding remarks concerning the main generalization aspects. Figure 1-2 illustrates the framework of the thesis with the respective sections which complement each other in order to finally discuss the fulfillment of the research questions in chapter 5.
2.1 Characteristics of the wood processing industry 2.2 Levels of Automation 2.3 Levels of Competence Chapter 2 Theoretical background 2.4 Levels of Information 2.5 Hierarchical Task Analysis 2.6 Value Stream Analysis Section 3.1 Research strategies – What is the content of the study Section 3.2 Method application – How is the study conducted Step I Traditional literature review Step II Systematic literature review Step III ‘Applied DYNAMO++’ Chapter 4 Findings and analysis 4.1 Issues with automation in the wood processing industry 4.2 Selection of LoA assessment method 4.4 Findings ‘Applied DYNAMO++’ Chapter 5 Discussion and conclusion 5.2.1 Research question 1 5.2.2 Research question 2 5.1 Method discussion 5.3 Validity and reliability 5.4 Triangulation assessment 5.5 Suggested future research 5.6 Conclusion
2 Theoretical background
This chapter aims to familiarize the reader with some critical concepts and terms for this thesis. Characteristics of the wood processing industry are presented as well as important factors of competitiveness within this industry. Furthermore, automation challenges and various methods for the measurement of LoA are discussed. Also the concepts of Levels of Competence (LoC), Levels of Information (LoI), Hierarchical Task Analysis (HTA) and Value Stream Analysis (VSA) are presented which forms the theoretical base for the case study.
2.1 Characteristics of the wood processing industry
About 75% of the Swedish woodlands is used industrially to produce raw material for various wood refining industries (Sandberg et al., 2014). The industries processing these goods can be described as “production activities that transform primary wood products (i.e. lumber and panels) into other wood products” (Kozak & Maness, 2001, p. 47). As part of the traditional literature review conducted in the context of the wood processing industry and automation issues, the important
characteristic areas are described in the following subsections. Appendix 1 provides
the complete overview of the wood processing industry’s profile.
2.1.1 Quality criteria
A big issue in wood processing which also affects the automation potential is the heterogeneous character of the raw material (Kozak & Maness, 2001). Depending on origin, which type of tree it is and which season, there is an impact on the overall raw material quality. Kozak and Maness (2001) define four different causes of quality costs in the wood processing industry, namely quality related to raw material, people, processes and products. Table 2-1 provides on overview about four common deficiency categories.
Table 2-1: Typical causes of quality costs in the wood processing industry (Kozak & Maness, 2003)
Raw material Processes
Knots and other natural defects Improper drying (moisture content) Splits and cracks Poor sizing and machining marks Variable moisture content Incorrect drilling
Streaks and discoloration Purchasing defective products
People Products
Hiring the wrong people Poor finishing quality Lack of training Inconsistent color
Poor morale Performance failures
In addition, Eliasson (2014) mentions a varying moisture content, natural dimension and shape as well as biological effects such as fungi-, bacterial- and insect infestations as crucial factors when it comes to the raw material quality. Eliasson (2014) also emphasizes the importance of mechanical factors, such as tensile strength, stiffness and durability.
2.1.2 Work place
The factor work place refers to the physical setting in which the workers operate. Physical work conditions are described as hard which is a result of lacking implementation of automation (Karltun, 2007). The same author names also the widely presence of handicraft work tasks which refers to a rather low automation level and noise, dust, heavy lifting, repetitive motions and solvents as discomfort factors experienced by blue collar workers in the Swedish secondary woodworking industry. Tuntiseranee and Chongsuvivatwong (1998) mention exposure to chemicals as significant aspect affecting the operators.
2.1.3 Job profile
The staff often has a lower education level compared to operators within other comparable industries and are commonly missing certificates and operation licenses (Tuntiseranee & Chongsuvivatwong, 1998; Karltun, 2007; Sowlati & Vahid, 2007). Karltun (2007) also describes a rapid turnover of labor force. Leschinsky and Michael (2004 ) conducted a survey among employees of the wood product industry and concluded that steady employment, good pay and security benefits such as pensions are ranked highest among motivators. As highest ranked company values, fairness, respect for the individual’s right and carefulness regarding work place conditions are named (Leschinsky & Michael, 2004 ).
Pirraglia, Saloni and van Dyk (2009) mention one way to gain competitive advantage, explicitly to implement training and education about lean implementation. Not only training about lean, but regarding new technologies applied in production is beneficial for the operator (Wiedenbeck & Parsons, 2010). This has a positive effect on both manufacturing productivity and the individual worker, as it is a beneficial factor in terms of ergonomics to educate workers and to give them more individual responsibility (Bohgard, 2009).
2.1.4 Production aspects
Hoff, Fisher, Miller and Webb (1997) indicate that there has been an increase in offshore production in the wood processing industry. They mention manufacturers in the U.S.A. who export logs (i.e. oak, ash and various tree varieties.) to production facilities in Taiwan, which in turn send the processed wood products back stateside. Immense savings in labor and production costs result from this procedure (Hoff et al., 1997). Eliasson (2014) writes about automation in relation to the wood processing industry. More stringent control of incoming raw material is proposed, i.e. introducing X-ray and ultrasound scanners as a means of measuring moisture content in a more accurate and effective way.
Karltun (2007) compares the wood processing industry to the metal industry and states varying material characteristics as a problematic factor regarding the implementation of automation. Furthermore, cutting forces and processing speed are significantly lower than within the metal industry (Karltun, 2007). Nevertheless, industrial robots as ‘third arm’ to support handling of the bulky material is seen as a way to introduce more efficient production techniques (Eliasson, 2014). Yet, sorting and grading processes are more difficult to automate (Karltun, 2007). As a consequence, when pursing the implementation of automation technologies, tighter acceptance tolerances regarding the specifications of incoming raw wooden material have to be understood, which is associated with higher rejection rates (Eliasson, 2014).
2.1.5 Product categories
The articles reviewed focuses on various product categories, which are part of the wood processing industry. These are listed below.
Furniture (i.e. office furniture) Fences
Palettes
Kitchen and bath cabinetry Door and window frames Shakes and shingles
Flooring
Mouldings and fittings Timber houses
Miscellaneous categories such as toys or ladders.
2.1.6 Supply chain characteristics
Schuler and Buehlmann (2003) identify that successful wood furniture manufacturers are often clustered within the same region. They mention Denmark and northern Italy as successful examples. However, there are also companies that pursue an opposing strategy and choose to outsource production to countries with lower production costs, as it is mentioned with Schuler and Buehlmann (2003). Karltun (2007) characterizes the Swedish secondary woodworking industry, which consists to a large extent of small firms with less than 50 employees.
Strategic alliances with suppliers of adhesives, packaging, steel, plastics, fabrics, lumbers or chips are favored by these firms (Schuler & Buehlmann, 2003). A demand from customers to reduce product lead time while simultaneously adding options to customize the products can be noticed (Teischinger, 2010).
2.1.7 Factors of competitiveness in the wood processing industry
The following subsection deals with the question how competitiveness is defined in general and which factors determine a company’s ability to act successfully on the market in the wood processing industry.
In general, the term competitiveness can be applied to companies, industries and nations (Hoff et al., 1997). Hopper, Jazayeri and Westrup (2008) mention sectorial competitiveness, relative cost competitiveness dealing with the real national exchange rate and productivity as three competitiveness measures on various aggregation levels.
Dealing with the firm’s level, external as well as internal components of competitiveness can be found (Hoff et al., 1997). Whereas the first includes aspects like comparative advantage, price and market distortions and the latter encompasses considerations associated with industrial organization, like efficiency and quality (Hoff et al., 1997).
Especially diverse quality management concepts such as Statistical Process Control (SPC), the Toyota Production System (TPS) or Lean Management with its focus to reduce waste for an optimized production have been extended to the entire organization (Schmitt, Stiller & Falk, 2013). Following this approach, there is the view that an evaluation of competitiveness is build up on both product and process features (Hoff et al., 1997).
An improved quality typically leads to higher production cost, but concurrently with a more stable delivery quality, the costs due to deficiencies will be reduced enormously and higher prices or an increased sales volume can be achieved (Bisgaard, 2008). This emphasizes the connection between internal and external aspects, as price competition in a long term view must also include quality considerations (Deming, 1982).
However, Porter reduces the term competitiveness to national productivity which seems to be “the only meaningful definition of competitiveness at the national level” and names relevant factors such as product quality, design, technology and the efficiency of manufacturing (Ellis & Davies, 2000, p. 1190). Taking over a firm perspective, if a company wants to achieve a more efficient manufacturing system, Fasth, Stahre and Frohm (2007) present various parameters to consider. Efficiency, flexibility, complexity, robustness and proactivity are seen as indicators shaping a production system here (Fasth et al., 2007).
Bellgran and Säfsten (2009) link the common competitive factors cost, quality, speed, dependability and flexibility to the performance of production systems and use a polar to illustrate the extent of fulfillment of market requirements. According to Winroth, Säfsten, Stahre, Granell and Frohm (2007) automated manufacturing systems are seen as highly productive, associating it directly with an improved competitiveness.
In this context, the Overall Equipment Effectiveness (OEE) is regarded as common measure variable for determining the performance of semiautomatic and automatic production systems (Bellgran & Säfsten, 2009). It is calculated by using various generic variables which determine material utilization, availability and machine utilization (see table 2-2). The yellow frames in the table represents the three constituents of OEE; whereas the red squares illustrate losses. Kent, Bakker, Hoyles and Noss (2011) describe the purpose of this measure as balance between performance of a process and the quality whilst keeping it in a sound and sustainable condition with a proper availability.
Table 2-2: Overview of OEE structure modified according to a case study in wood processing Overall Equipment Effectiveness
Availability Set-up Machine stop No planned production Utilization Short stops Reduced pace Set-up Machine stop No planned production Quality/ Material utilization Waste Short stops Reduced pace Set-up Machine stop No planned production Approved running meters
Lead-times and on-time deliveries are effects of a better flow, whereas the reduction of defects increases the reliability of the production processes and reduces variability (Lander & Liker, 2007). Slack (2005) reviews different kinds of manufacturing flexibility as contributors to overall company performance and names product flexibility as ability to introduce or modify products, mix flexibility as measure of the range of products made within a specific time period and volume flexibility as the ability to change the total output level.
Focusing on the wood processing industry, factors which are examined in the reviewed articles include the total factor productivity, labor cost on average and production value per firm (Koebel, Levet, Nguyen-Van, Purohoo & Guinard, 2016). This leads to the assumption that economies of scale and new technology or innovative production processes have a special importance. However, Hoff et al. (1997) argue that small firms are able to capture economies of scale in collaboration with suppliers and distributors and at the same time retain a critical flexibility, so that small firm size is not a disadvantage in the wood processing industry. As Sandberg et al. (2014) describe, the wood processing industry is seen as ‘small-scale industry’ with strong differentiated activities and products, which on the other hand complicates the adaption to new conditions which occur on a global market. Välimäki, Niskanen, Tervonen and Laurila (2011) study the relationship of innovativeness on the competitiveness of wood product companies and conclude that indicators such as patent applications, new products and processes and spending for R&D are more important for the birth of innovations compared to i.e. the level of education of personnel. Also Diaz-Balteiro, Heruzo, Martinez and Gonzalez-Pachon (2006) mention the reduction of production costs by introducing new and more efficient production processes based on technological innovation in Spain’s wood-based industry. An aspect which fastens this development are rising raw material prizes. Investments in new technologies enable firms in the wood processing industry to gain a faster customer response, achieve a quicker production and more customization as well as a greater product variety (Hoff et al., 1997).
The implementation of management systems which provide production data is crucial for the quantification of productivity and quality figures. Nowadays, the development of improved integrated sensors in production processes allows the implementation of so-called “cyber physical systems” which enable the application of i.e. accurate efficiency and quality deficiency measures within production processes (Schmitt, Stiller & Falk, 2013, p. 309). This concept is grounded on the perception that ‘Industry 4.0’, the term by which the future step of industrialization is called in Germany, leads to an improved quantification of relevant measurable variables associated with productivity as it is mentioned by Schuh, Potente, Varandani, Hausberg and Fränken (2014) and Wesch-Potente, Weber, Prote, Schuh and Potente (2014).
Especially when regarding the anisotropic structure of wood, the control over quality deficiencies which are determined according to common property parameters such as moisture content, shape stability, density and knots is important to retain profit margin (Gustafsson & Eliasson, 2014). Fasth, Stahre and Frohm (2007) name in this context material flexibility as “the ability to handle unexpected variations in dimensions or quality in part material”. Eliasson (2014) mentions the example of timber houses manufacturers in Sweden who have focused on process development or introduced Lean principles in order to attain efficient production processes.
Summarizing the factors named by various authors it becomes clear that a specific definition of competitiveness for the wood processing industry is not given. In fact, a focus on internal indicators such as efficiency and quality, also named as process factors, is considered as a suitable approach to achieve long-term competitiveness. This is combined with the implementation of new technologies concerning products and production processes, which increases material flexibility and gains quality as well as cost structure improvements.
When noticing that only one third of the raw material which is processed in the wood processing industry becomes a product with essential higher value than the source material, a higher need for process automation as means for an increased competitiveness in the wood processing industry can be noticed (Sandberg et al., 2014). Based upon this, Frohm (2008) draws a direct line from the implementation of automation technologies to cost reductions and increased efficiency which leads to an improved competitiveness.
2.2 Levels of Automation
Relevant information about the LoA concept is necessary to understand the background, the purpose and the methods which exist within the umbrella term “Levels of Automation”. Introductory, common challenges within the domain of automation implementation in production systems are described. This clarifies the motives why LoA is used for achieving an optimal task allocation between humans and machines. Furthermore, various definitions of LoA are discussed and relevant means for measuring LoA are presented which form the theoretical base for the
2.2.1 Challenges with the automation of production processes
This subsection gives the reader hints what companies’ problems associated with automation challenges are about.
When using the term automation, it is often used to describe mechanical, electronic and computer-based systems that are used for carrying out inspection tasks and controlling operations in production (Groover, 2001). Discussing process automation and the inherent consequences for the production system, there is usually a balance to determine between people and technology. On the one side there are the power, speed and physical abilities of automation systems, which stand against the flexibility and intuitiveness as well as analytical ability of humans (Slack, 2005). There is also a distinction about the degree of human involvement, which separates manual, semi-automatic and automatic tasks (Bellgran & Säfsten, 2009). Frohm (2008) mentions producing with a minimum of employees, a better working environment and improved quality as benefits of automation, whereas investment costs, an adaption of product design to automatic manufacturing and a too broad product portfolio are named as disadvantages. In general, automation aims to extend the technical feasibility and human capability and to overtake impossible or hazardous work tasks for humans (Lindström & Winroth, 2010). Also an increased flexibility is a reason to automate according to Osvalder and Ulfvengren (2009), even if it is emphasized that human contribution is generally still required in a technical system.
This aspect includes the human intervention in so-called advanced manufacturing systems (AMS), which – according to (Frohm, 2008) – cannot be reliable to 100 % throughout their operation time. Harlin, Frohm, Berglund and Stahre (2006) mention the operators’ education levels, knowledge about support tools and system interfaces as well as about work procedures as relevant capabilities to consider in the design of task allocations between humans and machines. Not only the best technical system is decisive for an improved performance, but also the division of work tasks between technology and its operators (Frohm, 2008).
A fractional loss of specific working skills and a degradation of the cognitive awareness are common consequences of too excessive Levels of Automation (Parasuraman, 2000). This can in turn lead to decreases in the Overall Equipment Efficiency (OEE) (Ylipää, 2000). Factors which influence the relative situation awareness in a human operated AMS are the perception of critical factors, the relation of these to the process goals and the probable system shift in near future (Endsley, 1996). As Osvalder and Ulfvengren (2009) emphasize in their work, dealing with disturbances and disruptions is critical within automated technical systems.
Following this idea, Ohno (1988) defines ‘autonomation’ as automation concepts with a human touch. The integration of devices which can distinguish normal and abnormal conditions prevents the production of defective parts, which is interpreted as human intelligence given to automated systems (Ohno, 1988).
With a highly automated production system, which cannot perform these decisions choices on its own, the operator could be at risk to fail the detection of the few occasional times when the automation fails (Parasuraman, 2000). Apart from this, trust in automatic systems relies heavily on the overall task complexity as Endsley and Kaber (1999) report: different users adopt diverging task execution strategies which are influenced by individual perceptions and understandings. These form an individual mental workload.
Connected to the issue of situation awareness and mental workload is the risk of physical or cognitive skill degradation which consequently turns the operators into static or passive monitoring roles and further deteriorates the ability to maintain direct control (Frohm, 2008).
The concept of human - centered automation is relevant in this context, which focuses on human capabilities to be complemented with automation and not vice versa (Satchell, 1998). This approach sees operators in a production system actively involved in control or decision making activities, allocation of resources and the evaluation of alternatives (Satchell, 1998). Otherwise, with the operators feeling ‘deskilled’ as professionals due to the loss of process knowledge, the systems become vulnerable if the technology fails.
2.2.2 What is Levels of Automation?
The following subsection presents the concept of LoA and contributes to establish an understanding for the background and the purpose of this approach. Certain diverging definitions are discussed and compared based on their perspective on automation.
Several authors have written about the concept of LoA in the past. Yet, they do not combine the same understanding of this term and apply different perspectives. Groover (2001) uses the expression LoA in a pure physical sense to describe five different levels where automated systems are installed within factory operations; enterprise level, plant level, cell or system level, machine level and device level. These levels build up an automation hierarchy which categorizes a number of components for automation and technologies applicable for different automated control, processing and handling purposes within a firm.
Osvalder and Ulfvengren (2009) raise up the question to which degree automation is suitable when taking into consideration the competitive profile of a company, i.e. high productivity or high responsiveness. A task allocation between humans and machine which is based on the relative benefits of automation was developed in the 1950s and has still a big relevance when designing today’s production systems. The so-called MABA–MABA list is dealt with in subsection 2.2.3.
Looking back in history, LoA was mentioned by Sheridan and Verplank in 1978, who described it as a continuum involving decision making and action processes. The range of options encompasses complete manual control to complete automatic control (Osvalder & Ulfvengren, 2009). Table 2-3 provides an overview about the
Table 2-3: Sheridan's Levels of Automation offers an overview about common degrees of automation (Osvalder & Ulfvengren, 2009)
Sheridan’s Levels of Automation
1. Automation offers no assistance. The human must do it all.
2. Automation suggests multiple alternatives, filters and highlights those which are considered to be the best.
3. Automation selects an alternative, one way to do the task and suggests this to the human.
4. Automation carries out the action if the person approves.
5. Automation provides the human with limited time to veto the action before automatic execution.
6. Automation carries out an action, and then informs the human. 7. Automation carries out an action and informs the human only if asked. 8. Automation selects method, executes task, and ignores the human.
Satchell (1998) describes each of his seven automation levels as a way of humans and machines sharing task to achieve outcomes. The degree of human involvement reaches from assisted manual control, shared control to management by exception and autonomous operation. Consequently, both Satchell’s and Sheridan’s concepts focus on the cognitive workload which is allocated among humans and technology. ‘Level of Mechanization’ as a variation to the Level of Automation is considered as the technical level of a manufacturing system. It consists of three levels and nine steps with a continuum from manual to automated manufacturing (Frohm, Lindström, Stahre & Winroth, 2009). Table 2-4 illustrates the concept, which is built upon the approach that the automation of physical tasks is categorized according to subsystems like a Computerized Numerical Control (CNC) machine. These can be further integrated with e. g. automated transportation systems to form a manufacturing system (Frohm et al., 2009).
Table 2-4: The integrated concept of Levels of Mechanization (Frohm et al., 2009)
Levels of Mechanization according to Kern and Schumann
Pre-mechanization
Manual Line flow
Mechanization
Single units with manual work Single units with mechanical control
Multi-functional units without manual control Systems of units
Automation
Partly automated singe units Partly automated systems Automated manufacturing
Considering the dimensional perspectives which have been referred so far regarding Levels of Automation, it becomes clear that a main distinction concerning research focus can be set between the physical and the cognitive aspect of automation. (Frohm, 2008) provides a definition of LoA which combines both approaches:
“The relation between human and technology in terms of tasks and function allocation, which can be expressed as an index between 1 (total manual work) and 7 (total automation) of physical and cognitive support.” (Fasth et al., 2007)
Their work results in a scheme, which classifies LoA in terms of a ‘Mechanical’ and ‘Cognitive’ perspective, as it can be drawn out from table 2-5.
Naming a definition of automation from (Lee, 2008), which deals with the credo that automation should aim for extending the physical and cognitive ability of people to achieve what is not possible otherwise, this illustrates again the complexity of automating. However, increased information flows in modern mass customization surroundings justify that not only technological feasibility and cost need to be considered when applying automation (Fasth, 2012).
Table 2-5: Levels of physical and cognitive automation according to (Frohm, 2008)
Levels Mechanical Cognitive
1
Totally manual
Totally manual work, no tools are used, only the users own muscle power. I.e. The user’s
own muscle power
Totally manual
The user creates his/ her own understanding of the situation and develops his/ her course of action based on his/ her earlier experience
and knowledge. I.e. The user’s earlier experience and knowledge
2
Static hand tool
Manual work with support of a static tool. I.e. Screwdriver
Decision giving
The user gets information about what to do or a proposal for how the task can be achieved.
I.e. Work order
3
Flexible hand tool
Manual work with the support of a flexible tool. I.e. Adjustable spanner
Teaching
The user gets instruction about how the task can be achieved. I.e. Checklists, manuals
4
Automated hand tool
Manual work with the support of an automated tool. I.e. Hydraulic bolt driver
Questioning
The technology questions the execution, if the execution deviates from what the technology
considers suitable. I.e. Verification before action
5
Static machine/ workstation
Automatic work by a machine that is designed for a specific task. I.e. Lathe
Supervision
The technology calls for the users’ attention, and directs it to the present task. I.e. Alarms
6
Flexible machine/ workstation
Automatic work by a machine that can be reconfigured for different tasks. I.e. CNC
machine
Intervene
The technology takes over and corrects the action, if the executions deviate from what the technology considers suitable. I.e. Thermostat
7
Totally automatic
Totally automatic work. The machine solves all deviations or problems that occur by
itself. I.e. Autonomous systems
Totally automatic
All information and control are handled by the technology. The user is never involved. I.e.
Autonomous systems
The cognitive perspective on automation research aims to speed up information flow and to provide a sufficient decision support, which enables the human to monitor the situation (Frohm, 2008). Together with the mechanical/physical perspective of automation which covers the replacement or support of operator’s muscle power for a faster as well as enhanced performance of repetitive tasks, a more holistic view on LoA is achieved, compared to regarding automation as merely physical (Fasth, 2012).
2.2.3 Methods for measuring Levels of Automation
In order to perform an assessment of LoA in a production system, different methods have been developed in a number of publications. The further parts deal with a number of those, which are presented with their aim, their scope within an organization and relation to automation as means for production performance
optimization. Within the course of the systematic literature review which is presented in chapter 3 of this thesis, the methods for LoA measurement are compared by selecting and applying relevant evaluation criteria.
Cognitive Reliability and Error Analysis Method
Cognitive Reliability and Error Analysis Method (CREAM) is a method for risk assessment belonging to Human Reliability Analysis (HRA), with the aspects of cognitive errors and error mechanisms (Fasth, 2012). It is mentioned by Hollnagel (1998) and aims to model control factors in their context, i.e. categorize human cognition in terms of what competence is required in order to perform certain tasks or operations, and how the actions are controlled. CREAM is mainly used to identify the cause of an observed happening. The cause is categorized as either an accident or as an erroneous action. The need and purpose of this qualitative method has its origins in the air traffic control (Hollnagel, 1998).
The Delphi method
The Delphi method was developed in the 1950s by a private research institute in U.S.A. and is named after the oracle in Delphi. The original intention for the U.S. department of defense was to work out prognoses for the intention of planning the nation’s defensive strategies (Linstone & Turoff, 2002). The methodology is about involving a panel of experts and asking each of them a series of questions. The answers are collocated and are given back to the panel experts, who now have the ability to change their answers depending on the other experts’ opinions. This is an anonymous form of an interview, with the intention of a group reaching common ground regarding an issue. As mentioned previously this method is used when it comes to predictive actions (Linstone & Turoff, 2002).
DYNAMO++
The original DYNAMO method is the result of a project (called the DYNAMO project) conducted between 2004 - 2007 and consisted of eight steps, which were verified and validated through the conduction of six case studies (Granell, Frohm, Bruch & Dencker, 2007). The further adaption of the methodology, given the name DYNAMO++, was developed between 2007 - 2009 and was validated using information gathered from additional five case studies within the Swedish manufacturing industry (Fasth, Stahre & Dencker, 2008; Granell et al., 2007). The modifications encompassed conducting a Value Stream Analysis (VSA) in order to gather information related to time parameters and material as well as information flow. A further part is video documentation to facilitate analysis of the assembly system (Fasth et al., 2008). DYNAMO++ contains four phases with twelve steps presented in table 2-6 below. This is followed by a step-by step walkthrough of the methodology.
Table 2-6: DYNAMO++ overview according to Fasth et al. (2008)
Overview of DYNAMO++ Phase I - Pre-Study
1 Choose a system
2 Walk the process
3 Identify flow and time parameters (VSA)
Phase II - Measurement 4 Identify main operations (HTA)
5 Measure LoA (mechanical and cognitive)
6 Document the results
Phase III - Analysis
7 Decide the minimum and maximum levels for the
identified operations (Workshop)
8 Design the Square of Possible Improvements
(SoPI) based on results from the workshop
9 Analysis of the SoPI
Phase IV - Implementation
10 Visualize suggestions of improvements
11 Implementation of the suggested improvements
12 Follow-up on the implementation
Initially the system and its boundaries have to be selected. This can be done off-site and includes discussing goals and purposes of the measurement. Relevant delimitations deal with production flow considerations and aim to identify a suitable system (Frohm, 2008). The scope of the system also defines the range of possible improvements, as a too narrow scope allows only limited changes regarding size, number and variety of task arrangements (Edwards & Jensen, 2014). Based upon this determination, the process is walked through, in order to visualize the production flow and define the relevant work stations where production activities are performed (Frohm, 2008).
The Value Stream Analysis (VSA) identifies flow and time parameters which are relevant for performing and creating a Value Stream Map (VSM) (Fasth et al., 2008). As the LoA is measured on task level with the DYNAMO++ method, each individual work task performed needs to be identified. This is done with Hierarchical Task Analysis (HTA). It simplifies the breakdown from main tasks into sub-tasks (Frohm, 2008).
The matrix which constitutes figure 2-1 illustrates two reference scales, encompassing 49 types of possible solutions for task allocation (Fasth & Stahre, 2011). The mechanical LoA on the x-axis describes ‘with what’ to assemble, whereas the cognitive LoA on the y-axis deals with ‘how’ to assemble on lower levels (1-3) and ‘situation control’ on the higher level (4-7) (Fasth-Berglund & Stahre, 2013). The taxonomy matrix quantifies the measuring of LoA, allows a comparison of various work tasks on a ranking scale and acts therefore as reference point when discussing possible improvements (Fasth & Stahre, 2011).
Figure 2-1: The LoA taxonomy and relevant implications on production characteristics (Fasth-Berglund & Stahre, 2013)
The results are then documented and discussed in a focus group. Fasth and Stahre (2011) name production operators, logistics, engineers and production managers as suitable target group. As a result, the relevant minimum and maximum levels of critical tasks are mapped in the reference scale in order to illustrate the possible improvements (Fasth et al., 2007).
Thus, the Square of Possible Improvements (SoPI) sets the boundaries for the company’s system to develop in the future (Fasth et al., 2008). An example of this matrix is presented in figure 2-2. The left matrix in the figure represents the SoPI for one task in a process. The dark green box shows the current LoA for that task while the area marked with a lighter green shade shows the outcome of possible improvements regarding alterations in the LoA. The matrix to the right in the figure displays the operation optimization, which means the common base of improvement for one process and its included tasks.
Figure 2-2: Task optimization (left) and possible operation optimization (right) (Fasth & Stahre, 2008)
The following analysis of SoPI deals with the issue which measures are most feasible for a task optimization (Fasth & Stahre, 2008). In order to achieve this, companies need to consider an amount of parameters and performers which are relevant for their system (Fasth et al., 2007). After having done an analysis concerning the improvement options, an action plan is worked out which includes detailed measures to consider in the implementation phase and dates for activation (Frohm, 2008). As a last step after implementation of the chosen improvements, a follow-up reveals which effect the suggestions have had on time and relevant flow parameters (Fasth et al., 2008).
Summarizing DYNAMO++, it is about identifying a production system’s current state, visualizing how it can be improved with regard to automation measures, and finding a way to successfully implement the improvements in order to achieve -based on the selection of relevant performance parameters - an increased efficiency or flexibility level or reduce production related disturbances (Fasth & Stahre, 2008).
KOMPASS
The Complementary Analysis and Design of Production Tasks in Socio-technical Systems (shortened KOMPASS) method aims to support design teams in determining the task allocation in automated systems by guiding them with the KOMPASS criteria, whose main attributes are work system issues like task completeness or independence of work stations, human work tasks (communication requirements, time pressure) and human – machine interaction (i.e. decision authority) (Grote, Ryser, Wäfler, Windischer & Weik, 2000).
With its consideration of people-related, technological and organizational factors, it encompasses a holistic overview what to consider when designing work stations and can be validated statistically via a questionnaire and correlation analysis (Grote et al., 2000).
Rapid Plant Assessment
Rapid Plant Assessment (RPA) analyzes a plant according to eleven categories, which encompass amongst others equipment condition or safety and environment. A questionnaire is filled in by relevant experts and as a result, a rating sheet illustrates the Leanness of the diverse sectors, revealing improvement areas (Comstock & Bröte, 2005). The method is proven for its fastness to assess a plants Lean Competence, but also requires expertise knowledge from inside the firm or industry sector to be applied in a suitable manner (Goodson, 2002).
Lean Customization Rapid Assessment
This method is derived from the already mentioned RPA method. The aim of Lean Customization Rapid Assessment (LCRA) is to provide support in the analyzing process and the design of a production system for the implementation of mass customization (Comstock, 2004). This is undertaken with the usage of three sheets; customer elicitation, engineering and manufacturing.
MABA-MABA task allocation
(Frohm, 2008) also provides an overview about the allocation of tasks between humans and machines. This highlights automation levels as production stages where both factors complement each other. Table 2-7 shows where the performance of the one actor exceeds that of the counterpart, also named as MABA-MABA (‘Man/ Machine Are Better at…’) criteria.
Table 2-7: The MABA-MABA list (Fasth, 2012; Frohm, 2008)
Humans surpass machines in the Machines surpass humans in the Ability to detect small amounts of visual or
acoustic energy
Ability to respond quickly to control signals and to apply great force smoothly and
precisely
Ability to perceive patterns of light or sound Ability to perform repetitive, routine tasks Ability to improvise and use flexible
procedures
Ability to store information briefly and then to erase it completely
Ability to store very large amounts of information for long periods and to recall
relevant facts at the appropriate time
Ability to reason deductively, including computational ability
Ability to reason inductively Ability to handle highly complex operations, i.e. to do many different things at once Ability to exercise judgment
The table above reveals that human nature often leaves the operator of a machine in case of an increased automation with the tasks to detect process deviations or the diagnosis of failures and similar abnormalities (Harlin et al., 2006).
Productivity Potential Assessment
Almström’s and Kinnander’s Productivity Potential Assessment Method (PPA) provides detailed instructions how to analyze a companies’ manufacturing performance. It is based on a 4-level view, which sees OEE on the narrowest level, followed by some broader parameters like turnover rate, scrap rate and delivery accuracy on level 2. As level 3, a list of 40 questions about 11 topics (from strategy– goals to quality) assess a companies’ ability to develop and maintain a production. Level 4 encompasses the productivity increase through method improvement, therefore it reflects only an estimation of present potential (Almström & Kinnander, 2007).
Systematic Production Analysis
Systematic Production Analysis (SPA) is a tool which was developed between 2007 and 2008. The goal with SPA is to reduce costs by measuring production condition and simulating various scenarios where three parameters, i.e. quality, production tact time and down-time parameters, vary (Jönsson, Andersson & Ståhl, 2008). This method uses two automation levels (automatic and manual) to categorize operations and assembly or production stations.
Task Evaluation and Analysis Methodology
The aim of Task Evaluation and Analysis Methodology (TEAM) is to assess current advanced manufacturing systems from the users’ perspective. This main purpose is to gain perspective and to pinpoint areas which experience efficiency related problems. As a result, an improved interaction between human operators and advanced technology is achieved (Wäfler, Johansson, Grote & Stahre, 1997). The evaluation is done with help of a matrix, developed by Stahre (1995), which is based on Rasmussen’s behavior levels and Sheridan’s operator roles (see table 2-8). In this model, four factors are examined: work environment, work tasks, information flow and system performance (Johansson, 1994).
TUTKA
TUTKA was developed in the late 2000s by Koho (2010) with the aim of assessing the current state of a production system and identifying potential improvement means. It compares the current state with the desired one (i.e. a well-functioning production system) by using a matrix consisting of 33 selected key characteristics divided over six different decisions areas. Each key characteristic is weighed against six production objectives, which are defined as quality, lead time, product flexibility, volume flexibility, delivery reliability and cost (Koho, 2010). Examples of key characteristics include a reliable production equipment, positioning of customer differentiation points and cross-trained operators. The qualitative judgment assesses the degree of according to four levels – encompassing no correspondence, partial correspondence, full correspondence and adaptability. As a results, a comparison of current and desired values in specific key characteristic reveal action potential (Koho, 2010).
