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THIS month there will be assembling at Margate the Conference on Automation organised by the Institution of Production Engineers.

The concept of customer value is becoming increasingly used in strategy and marketing literature in recent years. Customer value is considered central to competitive advantage and long‐term success of business organizations. Consequently, a great importance attached to this concept. This paper attempts to build an integrative configuration of the concept of customer value that reflects its richness and complexity. It reviews, synthesizes and extends the literature on the subject. The configuration includes three complementary models, namely: customer value in exchange, customer value buildup, and customer value dynamics. Thinking about customer value in this way is helpful in the designing of and studying service offerings.

To better understand the role of industrial big data in promoting digital transformation, the authors propose a theoretical framework of industrial big-data-based affordance in the form of an illustrative metaphor – what the authors call the “organizational drivetrain.”

This study investigates the effective use of industrial big data in the process of digital transformation based on the technology affordance–actualization theoretical lens. A software platform and services provider with more than 4,000 industrial enterprise clients in China was selected as the case study object for analyzing the digital affordance and actualization driven by industrial big data.

Drawing on a revelatory case study, the authors identify three affordances of industrial big data in the organization, namely developing data-driven customized projects, provisioning equipment-data-driven life cycle services, establishing data-based trust and determining affordance actualization actions driven by technology and market. In addition, the authors reveal the underlying drivetrain mechanisms to advance industrial big data affordance and actualization: stabilizing, enriching and pioneering.

This study builds a drivetrain model on digital transformation by industrial big data affordance actualization. The authors also provide practical implications that can help practitioners to implement digital transformation effectively and extract value from their investment.

The purpose of this paper is to establish a combined model for selecting key indexes of complex equipment, and then improve the cost forecasting precision of the model. The problem how to choose the key elements of complex products has always been concerned on many fields, such as cost assessment, investment decision making, etc.

Using Grey System Theory to establish a cost estimation model of complicated equipment is more reasonable under the few data and poor information. Therefore, this paper constructs cost index’s system of complex equipment, and then quantitative and qualitative analysis methods are utilized to calculate the grey entropy between the characteristic parameter and the behavior parameters. Further, establish the grey relational clustering matrix of the behavior sequences by using the grey relative incidence analysis. Finally, the authors select key indicators according to the grey degree.

The experiment demonstrates that the cost key parameters of complex equipment can be successfully screened out by the proposed approach, and the cost estimation accuracy of complicated products is improved.

The method proposed in this paper could be utilized to solve some practical problems, particularly the selection of cost critical parameters for complex products with few samples and poor information. Taking the cost key indexes of civil aircraft as an example, the results verified the validity of the GICM model.

In this paper, the authors develop the method of GICM model. Taking the data of civil aircraft as an example, the authors screen the key indicators of complex products successfully, and improve the prediction accuracy of the GM (1, N) model by using the selected parameters, which provides a reference for some firms.

The purpose of this paper is to propose a new system frame named ACPI for research and development (R&D) management of complex equipments according to the ideas of artificial societies, computational experiments, parallel execution and interactive optimization.

An artificial system which can effectively model, simulate and recur the main features and behaviors of a real R&D system of complex equipment is established at first. The structure and function of the system and its subsystems, and the relation of factors in the system are analyzed. Then one can perform computation experiment, modeling and simulation in the artificial system to obtain the optimal solutions. Finally, practice these solutions in a real system and at the same time perform the solutions in artificial system, forecasting and warning the possible new situations and problems in a realistic process, and provide controlling scheme.

The typical characteristics and solutions of the R&D system of complex equipment are analyzed. The sketch scheme of ACPI, the system frame of ACPI for R&D management of complex equipments are proposed, and the key technologies used in implementing ACPI of R&D system of complex equipment are studied.

The outcome of this paper can be used in computation experiments, management and optimization of R&D systems of complex equipment.

The sketch scheme of ACPI, the system frame of ACPI for R&D management of complex equipments are proposed first. The ACPI system can supply a high‐performance, open and interactive platform for the analog simulation and computation experiments of the R&D process management of complex equipment.

The purpose of this paper is to demonstrate industrial Internet of Things (IIoT) solution to improve the equipment condition monitoring with equipment status data and process condition monitoring with plasma optical emission spectroscopy data, simultaneously. The suggested research contributes e-maintenance capability by remote monitoring in real time.

Semiconductor processing equipment consists of more than a thousand of components, and unreliable condition of equipment parts leads to the failure of wafer production. This study presents a web-based remote monitoring system for physical vapor deposition (PVD) systems using programmable logic controller (PLC) and Modbus protocol. A method of obtaining electron temperature and electron density in plasma through optical emission spectroscopy (OES) is proposed to monitor the plasma process. Through this system, parts that affect equipment and processes can be controlled and properly managed. It is certainly beneficial to improve the manufacturing yield by reducing errors from equipment parts.

A web-based remote monitoring system provides much of benefits to equipment engineers to provide equipment data for the equipment maintenance even though they are physically away from the equipment side. The usefulness of IIoT for the e-maintenance in semiconductor manufacturing domain with the in situ monitoring of plasma parameters is convinced. The authors found the average electron temperature gradually with the increase of Ar carrier gas flow due to the increased atomic collisions in PVD process. The large amount of carrier gas flow, in this experimental case, was 90 sccm, dramatically decreasing the electron temperature, which represents kinetic energy of electrons.

Semiconductor industries require high level of data security for the protection of their intellectual properties, and it also falls into equipment operational condition; however, data security through the Internet communication is not considered in this research, but it is already existing technology to be easily adopted by add-on feature.

The findings indicate that crucial equipment parameters are the amount of carrier gas flow rate and chamber pressure among the many equipment parameters, and they also affect plasma parameters of electron temperature and electron density, which directly affect the quality of metal deposition process result on wafer. Increasing the gas flow rate beyond a certain limit can yield the electron temperature loss to have undesired process result.

Several research studies on data mining with semiconductor equipment data have been suggested in semiconductor data mining domain, but the actual demonstration of the data acquisition system with real-time plasma monitoring data has not been reported. The suggested research is also valuable in terms of high cost and complicated equipment manufacturing.

The unmanned aerial vehicles (UAVs) entered into their development stage when different applications became real. One of those application areas is agriculture. Agriculture and transport currently follow infrastructure as the top industries in the world UAV market. The agricultural UAV can be acquired as a ready-made, built by its future user or UAV-as-a-service (UaaS) way. This paper aims to help the UAVs’ users to choose the right sensors for agricultural purposes. For that sake, the overview of the types and application areas of onboard sensors is presented and discussed. Some conclusions and suggestions should allow readers to choose the proper onboard sensors set and the right way of acquiring UAVs for their purposes related to the agricultural area.

The agricultural UAVs’ onboard specialised sensors have been analysed, described and evaluated from the farmer’s operational point of view. That analysis took into consideration the agricultural UAVs’ types of missions, sensor characteristics, basics of the data processing software and the whole set of UAV-sensor-software operational features. As the conclusions, the trends in the onboard agricultural UAVs’ sensors, their applications and operational characteristics have been presented.

Services performed by the UAVs for the agriculture businesses are the second in the UAV services world market, and their growth potential is around 17% compound annual growth rate in the next years. As one of the quickest developing businesses, it will attract substantial investments in all related areas. They will be done in the research, development and market deployment stages of that technology development. The authors can expect the new business models of the equipment manufacturers, service providers and sellers of the equipment, consumables and materials. The world agricultural UAVs’ services market will be divided between the following two main streams: the UAVs’ solutions dedicated to the individual farmers, systems devoted to the companies giving the specialised services to individual farmers, in the form of UaaS. It will be followed by the two directions of the agriculture UAV set optimisation, according to each of the above streams’ specific requirements and expectations. Solutions for the individual users will be more straightforward, universal and more comfortable to operate but less effective and less accurate than systems dedicated to the agricultural service provider. UAVs are becoming important universal machines in the agriculture business. They are the newcomers in that business but can change the processes performed traditionally. Such an example is spraying the crops. UAVs spray the rice fields in Japan on at least half of them every year. The other is defoliating the cotton leaves, which only in one China province takes place on a few million hectares every year (Kurkute et al., 2018). That trend will extend the range of applications of UAVs. The agricultural UAV will take over process after process from the traditional machines. The types and number of missions and activities performed by agricultural UAVs are growing. They are strictly connected with the development of hardware and software responsible for those missions’ performance. New onboard sensors are more reliable, have better parameters and their prices are reasonable. Onboard computers and data processing and transmitting methods allow for effective solutions of automatisation and autonomy of the agricultural UAVs’ operation. Automatisation and autonomous performance of the UAVs’ agricultural missions are the main directions of the future development of that technology. Changing the UAV payload allows for its application to a different mission. Changing the payload, like effectors, is quite simple and does not require any special training or tooling. It can be done in the field during the regular operation of the agricultural UAV. Changing the sensor set can be more complicated, because of the eventually required calibrating of those sensors. The same set of sensors gives a possibility to perform a relatively broad range of missions and tasks. The universal setup consists of the multispectral and RGB camera. The agricultural UAV equipped with such a set of sensors can effectively perform most of the crop monitoring missions. The agriculture business will accept the optimised sensor-computer-software UAV payload set, where its exploitation cost and operational simplicity are the critical optimisation factors. Simplicity, reliability and effectiveness of the everyday operation are the vital factors of accepting the agricultural UAV technology as a widespread working horse.

Performed research studies have been done taking into consideration the factors influencing the real operational decisions made by the farmers or companies offering UAV services to them. In that case, e.g. the economical factors have been considered, which could prevail the technical complexity or measuring accuracy of the sensors. Then, drawn conclusions can be not accurate from the scientific research studies point of view, where the financing limits are not so strict.

The main goal of the paper is to present the reasons and factors influencing the “optimised” solution of the configuration of agricultural UAV onboard sensors set. It was done at the level useful for the readers understanding the end-users expectations and having a basic understanding of the sensors-related technologies. The paper should help them to configure an acceptable agricultural UAV for the specific missions or their servicing business.

Understanding the technology implications related to the applying of agricultural UAVs into everyday service is one of the main limits of that technology market deployment. The conclusions should allow for avoiding the misunderstanding of the agricultural UAVs’ capabilities and then increasing their social acceptance. That acceptance by the farmers is the key factor for the effective introduction of that technology into the operation.

Presented conclusions have been drawn on the base of the extensive research of the existing literature and web pages, and also on the own experience in forestry and agriculture and other technical applications of the onboard sensors. The experience in practical aspects of the sensors choosing and application into several areas have been also used, e.g. manned and unmanned aeroplanes and helicopters applied in similar and other types of missions.

The case illustrates how environmental forces affect an industry’s profitability. PESTEL and five forces analyses can be used to examine the retail agricultural equipment industry.

Single case study.

Jonathan Sullivan has a decision to make. His company is struggling due to difficult industry conditions. He is questioning if the company can continue to survive. MEC is an agricultural equipment dealer. The industry has experienced boom-and-bust periods since the company was founded. But the current downturn seems different. The past five years have been difficult as manufacturers have changed their dealership practices. Jonathan has struggled with some of the new practices the manufacturers have implemented. These new practices could negatively impact the company’s ability to survive. Jonathan wonders, “What is the best path forward for the business?”

The case is designed to be used in an undergraduate strategic management course.

Heavy equipment residual value forecasting is dynamic as it relies on the age, type, brand and model of the equipment, ranking condition, place of sale, operating hours and other macroeconomic gauges. The main objective of this study is to predict the residual value of the main types of heavy construction equipment. The residual value of heavy construction equipment is predicted via deep learning (DL) and machine learning (ML) approaches.

Based on deep and machine learning regression network integrated with data mining, random forest (RF), decision tree (DT), deep neural network (DNN) and linear regression (LR)-based modeling decision support models are developed. This research aims to forecast the residual value for different types of heavy construction equipment. A comprehensive investigation of publicly accessible auction data related to various types and categories of construction equipment was utilized to generate the model's training and testing datasets. In total, four performance metrics (i.e. the mean absolute error (MAE), mean squared error (MSE), the mean absolute percentage error (MAPE) and coefficient of determination (R2)) were used to measure and compare the developed algorithms' accuracy.

The developed algorithm's efficiency has been demonstrated by comparing the deep and machine learning predictions with real residual value. The accuracy of the results obtained by different proposed modeling techniques was comparable based on the performance evaluation metrics. DT shows the highest accuracy of 0.9111 versus RF with an accuracy of 0.8123, followed by DNN with an accuracy of 0.7755 and the linear regression with an accuracy of 0.5967.

The proposed novel model is designed as a supportive tool for construction project managers for equipment selling, purchasing, overhauling, repairing, disposing and replacing decisions.

The aim of this study is to develop a 3D model of decision- making grid (DMG) considering failure detection rate.

In a comparison between DMG and failure modes and effects analysis (FMEA), severity has been assumed as time to repair and occurrence as the frequency of failure. Detection rate has been added as the third dimension of DMG. Nine months data of 21 equipment of casting unit of Mobarakeh Steel Company (MSC) has been analyzed. Then, appropriate condition monitoring (CM) techniques and maintenance tactics have been suggested. While in 2D DMG, CM is used when downtime is high and frequency is low; its application has been developed for other maintenance tactics in a 3D DMG.

Findings indicate that the results obtained from the developed DMG are different from conventional grid results, and it is more capable in suggesting maintenance tactics according to the operating conditions of equipment.

In failure detection, the influence of CM techniques is different. In this paper, CM techniques have been suggested based on their maximum influence on failure detection.

In conventional DMG, failure detection rate is not included. The developed 3D DMG provides this advantage by considering a new axis of detection rate in addition to mean time to repair (MTTR) and failure frequency, and it enhances maintenance decision-making by simultaneous selection of suitable maintenance tactics and condition-monitoring techniques.