The “digital twin” is now a recognized core component of the Industry 4.0 journey, helping organizations to understand their complex processes, resources and data to provide insight, and help optimize their operations. Despite this, there are still multiple definitions and understandings of what a digital twin is; all of which has led to a “mysticism” around the concept. Following the “hype curve” model, it can be seen that digital twins have moved past their initial hype phase with only minimal implementation in industry, this is often due to the perceived high cost of initial development and sensor outfit. However, a second hype peak is predicted through the development of “lean digital twins.” Lean digital twins represent conceptual or physical systems in much lower detail (and hence at much lower cost to build and manage the models), focusing in on the key parameters and operators that most affect the desired optimal outcomes of the physical system. These lean digital twins are requirements managed with the system to ensure added value and tapping into existing architectures such as onboard platform management systems to minimize costs. This article was developed in partnership between BMT and Siemens to demystify the different definitions and components of a lean digital twin and discuss the process of implementing a lean digital twin solution that is tied to the core benefits in question and outlining the tools available to make implementation a reality.

While there is an increasing prevalence of dieting in the overall population, weight loss (WL) practices could be a risk factor for weight gain (WG) in normal-weight (NW) individuals. The aim of the present work was to systematically review all the studies implicating diet restriction and body weight (BW) evolution in NW people. The literature search was registered in PROSPERO (CRD42021281442) and was performed in three databases from April 2021 to June 2022 for articles involving healthy NW adults. From a total of 1487 records initially identified, eighteen were selected in the systematic review. Of the eight dieting interventional studies, only one found a higher BW after weight recovery, but 75 % of them highlighted metabolic adaptations in response to WL favouring weight regain and persisting during/after BW recovery. Eight of the ten observational studies showed a relationship between dieting and major later WG, while the meta-analysis of observational studies results indicated that ‘dieters’ have a higher BW than ‘non-dieters’. However, considering the high methodological heterogeneity and the publication bias of the studies, this result should be taken with caution. Moreover, the term ‘diet’ was poorly described, and we observed a large heterogeneity of the methods used to assess dieting status. Present results suggest that dieting could be a major risk factor for WG in the long term in NW individuals. There is, however, a real need for prospective randomised controlled studies, specifically assessing the relationship between WL induced by diet and subsequent weight in this population.

Lean management and related ideas have had a significant impact on organizations throughout North America and the world. Despite its popularity and impact, I-O psychologists have largely neglected Lean as a research topic and few I-O psychologists engage in applied practice in the area. In this focal article, we provide a working definition of Lean and present examples of Lean’s influence. Next, we outline possible reasons to explain I-O psychologists’ indifference to Lean. Finally, we provide some topic areas that I-O psychologists can use to contribute to the Lean literature. By using I-O psychologists’ skill in measurement and evaluation, along with our considerable organizational theory, we believe that I-O psychology can improve Lean and broaden their impact. We hope this focal article will inspire I-O psychologists to reconsider a research and practice area that they have previously ignored. In addition, we hope that this article causes I-O psychologists to reflect on their role to play in addressing popular management trends.

Background: Chest tube insertion is a time and safety critical procedure with a significant complication rate (up to 30%). Industry routinely uses Lean and ergonomic methodology to improve systems. This process improvement study used best evidence review, small group consensus, process mapping and prototyping in order to design a lean and ergonomically mindful equipment solution. Aim Statement: By simplifying and reorganising chest tube equipment, we aim to provide users with adequate equipment, reduce equipment waste, and wasted effort locating equipment. Measures & Design: The study was conducted between March 2018 and November 2018. An initial list of process steps from the best available evidence was produced. This list was then augmented by multispecialty team consensus (3 Emergency Physicians, 1 Thoracic Surgeon, 1 medical student, 2 EM nurses). Necessary equipment was identified. Next, two prototyping phases were conducted using a task trainer and a realistic interprofessional team (1 EM Physician, 1 ER Nurse, 1 Medical student) to refine the equipment list and packaging. A final equipment storage system was produced and evaluated by an interprofessional team during cadaver training using a survey and Likert scales. Evaluation/Results: There were 47 equipment items in the pre-intervention ED chest tube tray. After prototyping 21 items were removed while nine critical items were added. The nine items missing from the original design were found in four different locations in the department. Six physicians and seven RNs participated in cadaver testing and completed an evaluation survey of the new layout. Participants preferred the new storage design (Likert median 5, IQR of 1) over the current storage design (median of 1, IQR of 1). Discussion/Impact: The results suggest that the lean equipment storage is preferred by ED staff compared to the current set-up, may reduce time finding missing equipment, and will reduce waste. Future simulation work will quantitatively understand compliance with safety critical steps, user stress, wasted user time and cost.

This paper discusses how the processes of Continuing Airworthiness Management Organisations can be made more efficient by applying Lean principles and digital solutions, to obtain costs savings and improved compliance standards. Lean management is the process of eliminating waste by maximising value through balancing workloads and cutting out inefficient processes. Though Lean was initially applied to manufacturing production lines, an opportunity exists to apply the principles of Lean in airworthiness management. One such aspect of airworthiness management is the processing of records which is an essential part of safety and compliance. Software solutions designed with Lean philosophy and focus on airworthiness management processes can provide benefits in managing the flow of records and extracting actionable information from them for cost reduction and improving safety. Further value can be extracted by applying additional software solutions that will lay a strong foundation for machine learning and predictive analytics – taking aircraft maintenance from a reactive model towards a proactive model.

The English National Health Service (NHS) is a public sector organisation with a longstanding objective to deliver high quality healthcare that is free at the point of use. In order to achieve this, the NHS has endured an evocative and controversial theme of reform across many decades. Despite such high levels of reform, the recent Operating Efficiency Framework declared that the NHS is about to enter its toughest ever financial climate. This paper will illustrate the complexities and tensions of implementing service improvement in the NHS in a climate of persistent policy reform, reduced budgets and tough regulation. The paper reports findings of three case studies of hospital trusts in the UK in relation to the implementation of Lean improvement methodologies, highlighting key complexities of a hospital context and the corresponding tension with service improvement activity.

Ward round is an integral platform for patients and the multidisciplinary team to communicate, assess and plan treatment. With such a key process, it is vital that the effectiveness of the ward round is maximised and that reliable follow of information is addressed. This can be achieved using lean methodology.

Although psychiatric intensive care is a relatively new speciality, it has been well defined. The use of lean management techniques in association with accurately defining services lends itself well to treating patients in an effective and efficient manner. This paper summarises the development of lean management, its use in health services, and its early application within psychiatric intensive care units (PICUs) when examining quality of care provided. It then discusses its possible function in conjunction with robustly describing needs of patients within psychiatric intensive care and how this further enhances patient care by grouping these needs into different types.

In this paper we report on the results of a recent dissection experiment in The Netherlands where prediction formulae for the percentage lean meat in pig carcasses with the Hennessy Grading Probe (HGP) and a vision system (from now on referred to as Vision) were determined. Predictions with the HGP were based on one fat and one muscle depth measurement only, while predictions with Vision were based on as many as 115 direct and derived measurements. The data from this dissection experiment were used to illustrate the statistical calculations involved in relation to the number of carcass measurements. Prediction with instruments that gather a large number of measurements per carcass is not covered by the present European Community (EC) regulations. Therefore the calculations were conducted according to new regulations for statistical methodology in pig carcass grading that are expected to be adopted by the EC in the near future. The calculations included consideration of 3 subpopulations (females, entire males and castrated males). The Vision data were also used to show that ordinary regression after selection of a subset of carcass measurements severely under estimates the accuracy of prediction: instruments and associated prediction formulae are seemingly much more accurate than they truly are. When standard regression methods are used for instruments that gather a large number of measurements, there is a considerable risk that measurement instruments will be selected for the wrong reasons. Accuracy of approved instruments may not even comply with the EC-regulations, with poor consequences for harmonization within the EC.

Classification of pig carcasses in the European Community (EC) is based on the lean meat percentage of the carcasses. The lean meat percentage is predicted from instrumental carcass measurements, such as fat and muscle depth measurements, obtained in the slaughterline. The prediction formula for an instrument is derived from the data of a dissection experiment. When the relationship between percentage lean and instrumental carcass measurements differs between subpopulations, such as sexes or breeds, accuracy of prediction may differ between these subpopulations. In particular for some subpopulations predicted lean meat percentages may be systematically too low and for other subpopulations systematically too high. Producers or buyers that largely specialize in subpopulations where the percentage lean is underestimated, are put at a financial disadvantage.

The aim of this paper is to gain insight, on the basis of real data, into the effects of differences between subpopulations on the accuracy of the predicted percentage lean meat of pig carcasses. A simulation study was performed based on data from dissection trials in The Netherlands, comprising gilts and castrated males, and trials in Spain, comprising different genetic types. The possible gain in accuracy, i.e. reduction of prediction bias and mean squared prediction error, by the use of separate prediction formulae for (some of) the subpopulations was determined.

We concluded that marked bias in the predicted percentage lean meat may occur between subpopulations when a single overall prediction formula is employed. Systematic differences in predicted percentage lean between subpopulations that are overestimated and underestimated may exceed 4% and for selected values of instrumental measurements may run up to 6%. Bias between subpopulations may be eliminated, and prediction accuracy may be markedly improved, when separate prediction formulae are used. With the use of separate formulae the root mean squared prediction error may be reduced by 13 to 26% of the expected value when a single prediction formula is used for all pig carcasses.

These are substantial reductions on a national scale. This suggests that there will be a commercial interest in the use of separate prediction formulae for different subpopulations. In the near future, when the use of implants becomes more reliable, subpopulations will be recognized automatically in the slaughterline and use of different prediction formulae will become practically feasible. Some possible consequences for the EC regulations and national safeguards for quality of prediction formulae are discussed.

In The Netherlands a system has been developed for classification of live pigs based on lean meat proportion. Ultrasonic backfat thickness measurements were taken on 377 live pigs to assess the ability of the developed system for estimation of the lean meat proportion. The measurements were made at the sites mid point (half the distance from the occipital bone to the base of the tail), mid point –2·5 cm and mid point +2·5 cm, 5 cm off the dorsal mid line.

On the same day, these pigs were slaughtered and the lean meat proportions of the carcasses were estimated using the Hennessy Grading Probe (HGP). HGP measurements were taken between the third and fourth from last rib, 6 cm off the dorsal mid line on carcasses (3/4 LR). The day after slaughter, 88 left carcass sides were randomly chosen to be dissected according to a simplified European Union (EU) reference method.

From the fat thicknesses measured, the one at the site mid point on live pigs was the most accurate predictor for the EU lean meat proportion. The use of multiple site measurements, compared with a single site measurement, significantly reduced the residual standard deviation for the estimation of lean meat proportion.

The site mid point on live pigs differed in longitudinal as well as in dorsal-ventral direction from 3/4 LR on carcasses.

The results of this study suggest that multiple backfat thickness measurements on live pigs can be used for prediction of lean meat proportion with sufficient precision for practical use. Therefore, the developed system can serve as a classification system for live pigs.

The influence of plane of nutrition and diet on the eating quality of fresh pork was investigated in a study involving 721 animals. Boars and gilts of three genotypes (0, 0·25 and 0·50 Duroc inclusion level) were reared from 30 to 90 kg on seven feeding regimens (combinations of diet formulation and feeding level) to achieve different rates of lean and fat tissue growth during two growth periods (30 to 60 or 75 kg; 60 or 75 kg to 90 kg), respectively. A diet of conventional energy and protein (CEP, 14·2 MJ/kg digestible energy, 205 g/kg crude protein, 10 g/kg lysine) was given using combinations of ad libitum and restricted feeding to produce six treatment groups with variation in lean and fat growth rates. An additional treatment group was given food ad libitum on a higher energy and lower protein diet (HELP, 14·7 MJ/kg digestible energy, 166 g/kg crude protein, 7·0 g/kg lysine) between 30 and 90 kg. Dissected carcass composition at 90 kg was predicted from equations based on P2 fat depth, which were developed from full-side and ham joint dissections on sub-samples of animals. Representative sub-samples of animals were dissected at start (30 kg) and at interim weights (60 or 75 kg) to allow lean and subcutaneous fat growth rates to be calculated for all or parts of the growth period. The feeding regimes produced substantial variation in live-weight gain (DLWG) (744 to 914 g/day) and lean tissue growth rate (LTGR 345 to 417 g/day) and subcutaneous fat growth rate (SFGR 81 to 97 g/day), between 30 and 90 kg, and in longissimus dorsi intramuscular fat content (10·37 to 23·87 g/kg). Pigs given the HELP diet had the highest intramuscular fat and the best eating quality. Pigs offered the CEP diet ad libitum throughout the growth period produced more tender but less juicy meat than those given food restrictedly (0·8 or 0·9 of ad libitum). The correlations between DLWG, LTGR and SFGR for the whole or parts of the growth period and sensory characteristics, although often positive, were generally low, suggesting weak relationships.

The effect of energy supply between 45 and 100 kg body weight (BW) on the contribution of lean tissue (muscle plus intermuscular adipose tissue) to total protein mass was studied in Large White castrated males (cLW), crossbred Piétrain × Large White castrated males (cPPx) and boars (bPPx). The pigs were allocated to four energy levels (0·70, 0·80, 0·90 or 1·00 ad libitum) and kept in metabolism cages in experiment 1 or given food ad libitum and kept in individual pens in experiment 2. Daily protein supplies were calculated to be non-limiting for growth and identical for all pigs in experiment 1. Temperature was 23°C in both experiments. The pigs were slaughtered at 100 kg BW and physically dissected; the body tissues were chemically analysed. Taking into account housing conditions, the food intake of pigs in experiment 2 corresponded to 1·20 of ad libitum intake of pigs in experiment 1; data of both experiments were combined. The energy supply and the type of pig influenced significantly the protein content of empty BW (eBW) (170 g/kg on average), of lean (184g/kg on average) and non-lean compartment (eBW minus lean, 152 g/kg on average), the proportion of total protein deposited in lean (604 g/kg of total protein, on average) but not the protein content in fat-free eBW (209 g/kg on average). The fat-free eBW can be predicted as 4·8 times the body protein mass.

Fat (and muscle) thickness measurements were carried out in five slaughterhouses on 174 selected carcasses to compare the Hennessy Grading Probe (HGP) with the Renco Lean-Meater (Renco) and Pie Medical ultrasonic devices for their ability to predict the lean meat proportion of pig carcasses. The day after selection, all left carcass sides were dissected according to the simplified European Union (EU) reference method. In addition 44 of these sides were further dissected according to the full EU reference method.

In a further set of slaughterhouses it was found impossible to measure fat thickness with the Renco and Pie Medical ultrasonic device due to extreme singeing of the carcasses. From the five slaughterhouses, between third and fourth from last rib, 6 cm off the dorsal mid line, observations on fat thickness with HGP and Pie Medical were similar but observations with Renco were significantly lower (F < 0·001). Correlations between fat thickness measurements taken with HGP, Renco and Pie Medical all exceeded 0·95.

In terms of residual standard deviations of prediction formulae, the HGP performed highly better than Renco or Pie Medical (23·7, 25·1 and 24·9 g/kg respectively). These results, together with practical problems, make the use of ultrasonic measurements under practical conditions at present less feasible than using the HGP.