Introduction to innovation analytics

Innovation analytics (IA) is an emerging paradigm that integrates advances in the data engineering field, innovation field, and artificial intelligence field to support and manage the entire life cycle of a product and pro-cesses. In this chapter, we have identified several possibilities where ana-lytics can help in innovation. First, we aim to explain using a few cases how analytics can help in innovating new products to the market specifi-cally through collaborative engagement of designers and data. Second, we will explain the use of artificial intelligence (AI) techniques in the manu-facturing context, which progresses at different levels, i.e., from process, function to function interaction, and factory-level innovations

Analysis of Critical Factors for Automatic Measurement of OEE

The increasing digitalization of industry provides means to automatically acquire and analyze manufacturing data. As a consequence, companies are investing in Manufacturing Execution Systems (MES) where the measurement of Overall Equipment Effectiveness (OEE) often is a central part and important reason for the investment. The purpose of this study is to identify critical factors and potential pitfalls when operating automatic measurement of OEE. It is accomplished by analyzing raw data used for OEE calculation acquired from a large data set; 23 different companies and 884 machines. The average OEE was calculated to 65%. Almost half of the recorded OEE losses could not be classified since the loss categories were either lacking or had poor descriptions. In addition, 90% of the stop time that was classified could be directly related to supporting activities performed by operators and not the automatic process itself. The findings and recommendations of this study can be incorporated to fully utilize the potential of automatic data acquisition systems and to derive accurate OEE measures that can be used to improve manufacturing performance

Data-driven machine criticality assessment – maintenance decision support for increased productivity

Data-driven decision support for maintenance management is necessary for modern digitalized production systems. The data-driven approach enables analyzing the dynamic production system in realtime. Common problems within maintenance management are that maintenance decisions are experience-driven, narrow-focussed and static. Specifically, machine criticality assessment is a tool that is used in manufacturing companies to plan and prioritize maintenance activities. The maintenance problems are well exemplified by this tool in industrial practice. The tool is not trustworthy, seldomupdated and focuses on individual machines. Therefore, this paper aims at the development and validation of a framework for a data-driven machine criticality assessment tool. The tool supports prioritization and planning of maintenance decisions with a clear goal of increasing productivity. Four empirical cases were studied by employing a multiple case study methodology. The framework provides guidelines for maintenance decision-making by combining the Manufacturing Execution System (MES) and Computerized Maintenance Management System (CMMS) data with a systems perspective. The results show that by employing data-driven decision support within the maintenance organization, it can truly enable modern digitalized production systems to achieve higher levels of productivity

Real-Time data-driven average active period method for bottleneck detection

Prioritising improvement and maintenance activities is an important part of the production management and development process. Companies need to direct their efforts to the production constraints (bottlenecks) to achieve higher productivity. The first step is to identify the bottlenecks in the production system. A majority of the current bottleneck detection techniques can be classified into two categories, based on the methods used to develop the techniques: Analytical and simulation based. Analytical methods are difficult to use in more complex multi-stepped production systems, and simulation-based approaches are time-consuming and less flexible with regard to changes in the production system. This research paper introduces a real-Time, data-driven algorithm, which examines the average active period of the machines (the time when the machine is not waiting) to identify the bottlenecks based on real-Time shop floor data captured by Manufacturing Execution Systems (MES). The method utilises machine state information and the corresponding time stamps of those states as recorded by MES. The algorithm has been tested on a real-Time MES data set from a manufacturing company. The advantage of this algorithm is that it works for all kinds of production systems, including flow-oriented layouts and parallel-systems, and does not require a simulation model of the production system

An Enhanced Data-Driven Algorithm for Shifting Bottleneck Detection

Bottleneck detection is vital for improving production capacity or reducing production time. Many different methods exist, although only a few of them can detect shifting bottlenecks. The active period method is based on the longest uninterrupted active time of a process, but the analytical algorithm is difficult to program requiring different self-iterating loops. Hence a simpler matrix-based algorithm was developed. This paper presents an improvement over the original algorithm with respect to accuracy

Stream-IT: Continuous and dynamic processing of production systems data - Throughput bottlenecks as a case-study

Considering the needs for continuous availability of information out of data generated in Cyber-Physical production systems, we investigate the use of continuous stream processing as a paradigm for generating useful information out of the data, to support efficient and safe operation, as well as planning activities.Our contributions and expected benefits: (i) we show possibilities to automate and pipeline the validation and analysis of the data, hence providing an automated way to improve the quality of the latter and parallelizing the two phases; (ii) we show how to induce lower latency in generating the desired information, enabling it to be continuously made available, before whole batches of data are gathered, in cost-efficient ways; (iii) besides the automation of the above procedures that are commonly done in a batch fashion and with significant manual effort by the production system analysts, we show additional options for configuring ways in which to automate deeper analysis of the data; in particular, we provide evidences about how the rich semantics of stream processing frameworks can ease the development and deployment of data analysis applications in production systems.Moreover, using the problem of bottleneck detection as a sample scenario, we illustrate the above in a concrete fashion, on cost-efficient systems, that are plausible to have in existing deployments. The experimental study is on a 2-year data-set with more than 8.5 million entries, from a system including more than 30 interconnected machines and it demonstrates the benefits of the proposed methods, in providing timely and multidimensional information from the data, enabling possibilities for deeper analyses

Adaptive Stream-based Shifting Bottleneck Detection in IoT-based Computing Architectures

Cloud computing is revolutionizing the backbone of data analysis applications, including industrial ones. One of its main pillars is the separation of the logic with which data is accessed (e.g., to study the efficiency of a manufacturing system) from the actual hardware (e.g., server) that maintains and analyses the data. Large distributed cyber-physical systems enabled by, among other technologies, the Internet of Things (IoT), made nonetheless clear that \u27what to do\u27 with the data and \u27where to do it\u27 are not disjoint problems; i.e., cloud computing on its own is not enough. Fog and edge computing have emerged as complementary options, to distribute the analysis, helping with challenges by means of close-to-the-source data analysis.We show for a key problem for industrial processes, that of shifting bottleneck detection, how to take advantage of such multi-tier computing architectures, to perform continuous and configurable analysis of data from Manufacturing Execution Systems. We propose a processing framework, STRATUM, and an algorithm, AMBLE, for continuous, data stream processing. STRATUM seamlessly distributes and parallelizes the processing across the tiers and AMBLE guarantees consistent analysis in spite of timing fluctuations, which are commonly introduced due to e.g. the communication system; it also achieves efficiency through appropriate data structures for in-memory processing. The experimental study on a real-world dataset, taken from a production line over two years and including 8.5 million entries, shows the benefits of the proposed solution in enabling configurable and efficient analysis

An algorithm for data-driven shifting bottleneck detection

Manufacturing companies continuously capture shop floor information using sensors technologies, Manufacturing Execution Systems (MES), Enterprise Resource Planning systems. The volumes of data collected by these technologies are growing and the pace of that growth is accelerating. Manufacturing data is constantly changing but immediately relevant. Collecting and analysing them on a real-time basis can lead to increased productivity. Particularly, prioritising improvement activities such as cycle time improvement, setup time reduction and maintenance activities on bottleneck machines is an important part of the operations management process on the shop floor to improve productivity. The first step in that process is the identification of bottlenecks. This paper introduces a purely data-driven shifting bottleneck detection algorithm to identify the bottlenecks from the real-time data of the machines as captured by MES. The developed algorithm detects the current bottleneck at any given time, the average and the non-bottlenecks over a time interval. The algorithm has been tested over real-world MES data sets of two manufacturing companies, identifying the potentials and the prerequisites of the data-driven method. The main prerequisite of the proposed data-driven method is that all the states of the machine should be monitored by MES during the production run

A prognostic algorithm to prescribe improvement measures on throughput bottlenecks

Throughput bottleneck analysis is important in prioritising production and maintenance measures in a production system. Due to system dynamics, bottlenecks shift between different production resources and across production runs. Therefore, it is important to predict where the bottlenecks will shift to and understand the root causes of predicted bottlenecks. Previous research efforts on bottlenecks are limited to only predicting the shifting location of throughput bottlenecks; they do not give any insights into root causes. Therefore, the aim of this paper is to propose a data-driven prognostic algorithm (using the active-period bottleneck analysis theory) to forecast the durations of individual active states of bottleneck machines from machine event-log data from the manufacturing execution system (MES). Forecasting the duration of active states helps explain the root causes of bottlenecks and enables the prescription of specific measures for them. It thus forms a machine-states-based prescriptive approach to bottleneck management. Data from real-world production systems is used to demonstrate the effectiveness of the proposed algorithm. The practical implications of these results are that shop-floor production and maintenance teams can be forewarned, before a production run, about bottleneck locations, root causes (in terms of machine states) and any prescribed measures, thus forming a prescriptive approach. This approach will enhance the understanding of bottleneck behaviour in production systems and allow data-driven decision making to manage bottlenecks proactively

Artificial intelligence for throughput bottleneck analysis – State-of-the-art and future directions

Identifying, and eventually eliminating throughput bottlenecks, is a key means to increase throughput and productivity in production systems. In the real world, however, eliminating throughput bottlenecks is a challenge. This is due to the landscape of complex factory dynamics, with several hundred machines operating at any given time. Academic researchers have tried to develop tools to help identify and eliminate throughput bottlenecks. Historically, research efforts have focused on developing analytical and discrete event simulation modelling approaches to identify throughput bottlenecks in production systems. However, with the rise of industrial digitalisation and artificial intelligence (AI), academic researchers explored different ways in which AI might be used to eliminate throughput bottlenecks, based on the vast amounts of digital shop floor data. By conducting a systematic literature review, this paper aims to present state-of-the-art research efforts into the use of AI for throughput bottleneck analysis. To make the work of the academic AI solutions more accessible to practitioners, the research efforts are classified into four categories: (1) identify, (2) diagnose, (3) predict and (4) prescribe. This was inspired by real-world throughput bottleneck management practice. The categories, identify and diagnose focus on analysing historical throughput bottlenecks, whereas predict and prescribe focus on analysing future throughput bottlenecks. This paper also provides future research topics and practical recommendations which may help to further push the boundaries of the theoretical and practical use of AI in throughput bottleneck analysis