List of Figures

Figure 1 Main pillars and foundation for throughput excellence.

Figure 2 Contents and flow of this book.

Figure 1.1 Main performance focuses of a manufacturing company.

Figure 1.2 Business model for system throughput management.

Figure 1.3 Manufacturing throughput management environment.

Figure 1.4 Pillars and foundation of manufacturing system throughput.

Figure 1.5 Systems view on manufacturing performance.

Figure 1.6 Main cost categories of manufacturing operations.

Figure 1.7 (a) Continuous improvement versus (b) Optimization.

Figure 1.8 Time elements of an operation (or equipment).

Figure 1.9 Workstation's cycle time and line's cycle/throughput time.

Figure 1.10 Relationship between cycle time and throughput rate.

Figure 1.11 Throughput time and lead time of a manufacturing system.

Figure 1.12 Characteristics of four types of manufacturing processes.

Figure 1.13 Conversion view of a manufacturing system.

Figure 1.14 Basic configurations of manufacturing systems.

Figure 1.15 Local configuration examples in manufacturing systems.

Figure 1.16 Cascade view of operations in a manufacturing system.

Figure 1.17 Relative potentials of system throughput improvement.

Figure 1.18 Multiple products and different processes in a manufacturing system.

Figure 1.19 Operational states of manufacturing systems.

Figure 1.20 Three production systems in a serial configuration.

Figure 1.21 Example of four states of manufacturing operations.

Figure 1.22 Example of throughput rate elements of three manufacturing systems.

Figure 1.23 Example of throughput status of six subsystems

Figure 2.1 System throughput count display board.

Figure 2.2 Example of throughput accumulation chart and deviation chart.

Figure 2.3 Main contributing areas/factors to throughput performance.

Figure 2.4 Example of KPI radar chart of a small production area.

Figure 2.5 Example of KPI monitoring chart of a production line.

Figure 2.6 Relationships between the three types of manufacturing KPIs.

Figure 2.7 Relationships between OEE and its individual elements.

Figure 2.8 The 3D representation of OEE changes resulting from the three elements.

Figure 2.9 Contributions of three element losses to OEE.

Figure 2.10 Timeline view of OEE elements and equivalent losses.

Figure 2.11 Comparison between system performance and OEE (Society of Manufacturing Engineers. 2013/with permission of Elsevier).

Figure 2.12 Estimation of difference between OEE and system performance (Society of Manufacturing Engineers. 2013/with permission of Elsevier).

Figure 2.13 Example comparison of OEE and weighted OEE with individual element changes.

Figure 2.14 Elements of standalone OEE vs. conventional OEE.

Figure 2.15 Conventional OEE and OEEsa of three vehicle assembly shops.

Figure 2.16 Applications of OEE and OLE at different levels of operation automation.

Figure 2.17 Process flow of KPI selection.

Figure 2.18 Objective–KPI–data relationship map for KPI selection.

Figure 2.19 Interconnections among manufacturing aspects and related KPIs.

Figure 2.20 Relationship between component quality and product quality.

Figure 2.21 Linear reflection model of quality KPI transmissibility.

Figure 2.22 Manufacturing systems, operational KPIs, and finance/profit.

Figure 2.23 Cost implication of production overtime work.

Figure 3.1 Pipeline analogy of bottleneck limiting overall flow.

Figure 3.2 Five steps of applying Theory of Constraints.

Figure 3.3 Example of bottlenecks in a manufacturing system.

Figure 3.4 Two basic types of throughput bottlenecks and their relations.

Figure 3.5 Duration and persistence of bottlenecks and their general contributors.

Figure 3.6 Example of a system with four operations with their performance.

Figure 3.7 Active and inactive periods during manufacturing operations.

Figure 3.8 Bar chart of system active time rates.

Figure 3.9 Bottleneck identification based on turning point

Figure 3.10 Example of production line with different KPIs

Figure 3.11 Example of using OEE for bottleneck identification.

Figure 3.12 Using OEE and standalone OEE for bottleneck identification.

Figure 3.13 System layout of a vehicle paint shop (Tang, 2018/with permission of SAE International).

Figure 3.14 Process steps of conveyor (buffer) function.

Figure 3.15 Buffering effects of a conveyor on connected systems.

Figure 3.16 Effects of upstream and downstream buffers on system throughput.

Figure 3.17 WIP distribution in a buffer for buffer effect discussion.

Figure 3.18 Example of buffer WIP count measurement.

Figure 3.19 Two subsystems connected with a conveyor (buffer).

Figure 3.20 WIP status and trends on a conveyor.

Figure 3.21 Examples of buffer WIP trend monitoring for bottleneck identification.

Figure 3.22 Bottleneck identification based on WIP inventory level.

Figure 3.23 Buffer WIP change rate as an operational status monitoring indicator.

Figure 3.24 Histogram analysis process of buffer WIP using Excel.

Figure 3.25 Example of buffer WIP histogram analysis result.

Figure 3.26 Possible WIP distributions in buffer.

Figure 3.27 Range of WIP units in the buffer during normal operations.

Figure 4.1 Quality categories of product units produced.

Figure 4.2 Main pillars and foundation of QMS.

Figure 4.3 Roles of QMS in manufacturing operations.

Figure 4.4 QMS and vehicle assembly manufacturing system.

Figure 4.5 Efforts and benefits of improved quality planning.

Figure 4.6 Management focuses, cost analysis, and technical KPIs.

Figure 4.7 Breakdown of quality cost categorization.

Figure 4.8 Overall quality cost trends and economic quality and best quality.

Figure 4.9 Cash flow diagram showing P, F, and i.

Figure 4.10 Cash flow diagrams of discussion example.

Figure 4.11 Example of quality inspection and repair in manufacturing processes.

Figure 4.12 Quality contribution pathways to system throughput performance OEE.

Figure 4.13 Quality rate of a serial system (a Bernoulli process).

Figure 4.14 Example of a serial manufacturing system TPY and RTY.

Figure 4.15 Parallel subsystems with individual mean (μ) and standard deviation (σ).

Figure 4.16 Illustration of two different normal distributions.

Figure 4.17 Influence of mean differences on overall variation.

Figure 4.18 Example of individual and combined variations of two systems.

Figure 4.19 Example of online quality inspection using laser sensors (Courtesy of Perceptron, Inc.).

Figure 4.20 Illustration of layered process audit arrangement.

Figure 4.21 Example of error‐proofing – warning sign.

Figure 4.22 Example of error‐proofing – prevention mechanism (Tang, 2017/with permission of SAE International).

Figure 4.23 Qualitative tools for brainstorming and review.

Figure 4.24 Quantitative tools for pattern and reason analysis.

Figure 4.25 Quantitative network methods for relationship analysis.

Figure 5.1 Conceptual reliability “bathtub” curve.

Figure 5.2 Curves of Normal, Exponential, and Weibull distribution functions.

Figure 5.3 MTBF and MTTR of system and equipment.

Figure 5.4 Reliability trends under different MTBF values over time.

Figure 5.5 Role of maintenance management in a plant management system.

Figure 5.6 Customer‐supplier like the relationship between production and maintenance.

Figure 5.7 General relationship between availability and maintenance.

Figure 5.8 Relations between A and MTTR and between A and MTBF.

Figure 5.9 Maintenance contributions to throughput directly and via quality improvement.

Figure 5.10 Process flow and factors of maintenance...