Lean Manufacturing and MES — Minimize Waste and Improve Productivity
Warren Andrade
January 13, 2021
https://www.pinpointinfo.com/blog/lean-and-mes-minimize-waste-improve-productivity
Defining the Right Manufacturing Metrics is the First Step to Proving the Benefits of MES
FEBRUARY 09, 2021
https://www.ibaset.com/defining-the-right-mes-metric-is-the-first-step-to-proving-the-benefits/
Expand Lean Manufacturing with MES
White Paper
https://discover.3ds.com/expand-lean-manufacturing-with-mes
A Novel Methodology to Integrate Manufacturing Execution Systems with the Lean Manufacturing Approach
Gianluca D’Antonio, Joel Sauza Bedolla, Paolo Chiabert
Procedia Manufacturing
Volume 11, 2017, Pages 2243-2251
https://doi.org/10.1016/j.promfg.2017.07.372
https://www.sciencedirect.com/science/article/pii/S2351978917305802
A recent research [9] showed that companies need to increase the degree of use of IT tools in order to implement lean practices. The importance of MES in this field has also been shown. Cottyn [10] developed a first framework for the alignment of MES to lean objectives. He defined an automatic Value Stream Mapping (aVSM) methodology: the aVSM benefits from the information provided by the MES, since it is a rich source of information and historical data useful to define continuous improvement actions. The methodology is validated through the case studies of a furniture firm and a food and beverage company. In [11], the support of MES to lean manufacturing has been discussed through the case study of a supplier of components for buses and coaches. Nevertheless, a methodology for fully integrating the MES capability in data analysis and dispatching with lean practices is still lacking.
The methodology for data analysis
Data source. First, the data necessary to perform the analysis and their sources must be defined. On the shopfloor several kinds of devices can be deployed to collect data. First, the PLC of the machine involved in the process can provide helpful data concerning, for example, axes position and errors, axes and spindle movement, the deployed tool and the content of the stock, the applied power and torque, and some key performance indicator (e.g. cycle times, throughput, the incidence of failures). Furthermore, different kind of sensors can be integrated into the machine to collect data related to the quality process and the state of the tool. In machining processes, the most deployed sensors are dynamometers, accelerometers, thermometers, acoustic emission and current sensors.
Sensors can be used both online - while the process is occurring - or offline, for example to evaluate the quality of a finished part (e.g. geometrical dimensions, mechanical strength, electrical properties); of course, sensors collecting different kind of data can be used and their information can be integrated to have a more exhaustive picture.
Data processing and Feature generation. The second step consists in choosing the mathematical technique to analyze the collected data. The aim of data processing is to transform data, regardless of the source, into information through the generation of a finite set of features.
Mainly, two classes of data processing techniques can be used. The first one consists in mathematical models, based on deterministic or statistic approaches. This technique is convenient when the analyzed system is not too complex and its behavior is fully known. In particular, the statistical approach is effective in dealing with a huge amount of data and is widely used, for example, with data acquired by a sensor set.
The second class of data processing techniques consists of simulation tools: they are preferable when the analytical description of the system is too complex. Data provided in input to the simulation can provide from several sources: theoretical (or expected) data can be used to evaluate the behavior of the system in standard situations; real data, collected at the shop-floor are helpful to be aware of the reaction of the system in the current situation.
Feature extraction and Decision making. The role of the data processing technique is to synthesize the collected data into a smaller set of information features; nevertheless, some of them may be not significant or reliable to take decisions and, thus, should be discarded. Furthermore, new significant features can be extracted by combining some parameters: overall indices can be obtained by averaging features, by generating response surfaces or by comparing the expected state with the real condition of a process or a product.
Finally, a strategy for decision making must be defined, based on the results of the feature extraction. The decision can be automatically taken by an algorithm able to choose the values of a set of parameters in order to optimize a given metric. Alternatively, the algorithm may provide hints to an operator and leave him free to act on the process. Furthermore, the decision making algorithm should also provide an estimation of the state of the process after such intervention, to evaluate the impact on the performance of the process.
Case study
Step 1. Process and wastes. A manufacturing process in the field of aeronautics is presented.
The process of gear grinding is considered. This is a critical process, because these workpieces must be manufactured with great accuracy. Since grinding is a costly operation , it should be utilized under optimal conditions. The established manual operation consists of two steps. First, a pre-processing task is made to identify the workpiece axis that minimizes the geometrical distortions. This action is performed by finishing the two countersinks of the gear, which are used to place the part into the grinding machine. Then, gear grinding is performed. The operators highlighted an excessive rate of defective parts; this led to expensive reworking operations and to process variability resulting, in turn, in excessive waiting times and inventory parts accumulating through the process. The latter two waste sources were confirmed by the Value Stream Mapping analysis. Therefore, a novel system to perform gear centering prior to the grinding operation has been studied.
Step 2. Process description. After having identified the wastes affecting the process, a thorough description of the grinding process has been made. The input components are the gears leaving the upstream heat treatment process; gears belong to a finite set of well-known part families, and are grinded one-by-one. The quality of the output parts is measured through functional tolerances: residual concentricity for the bearing seats and the gear, and total axial runout of the side surface; the range of such tolerances – defined in the ISO 1101 standard – is in the rder of 0.05-0.1 mm. The performance of he process is measured through well-established indicators: cycle-time, work in process, throughput and rate of failures. In order to perform the process, a skilled operator is necessary to perform the correct positioning of the workpiece in the machine.
In order to improve the performance of the grinding process and the quality of the machined gears, a novel system to support piece positioning has been developed, supported by a proper mathematical technique. Mainly, the gear is placed into a manufacturing machine to finish two surfaces – at the top and bottom extremities of the piece – with the aim of defining a new reference system for the part that minimizes the residual geometrical error. Such surfaces are used in the subsequent grinding operation to easily place the gear into the machine.
Step 3. Data-analysis. First, the sources for data acquisition have been selected. Given the strict quality needs, displacement transducers are used to measure the profile of the gear where the tolerances are set,
2c. To perform the measurement, a rotation of the gear about the axis of the machine is made. Since the tolerances are tight, sensors with high reproducibility (30 μm) have been used and a high acquisition rate is set (3600 points/revolution). After the acquisition, data are processed: to minimize the impact of measurement noise and errors, a least-squares interpolation is made for each of the gathered profiles. In particular, the three radial sections (i.e. the gear and the bearing seats) are interpolated through least-squares ellipses, and the coordinates of their centers are extracted.
Given the cost of the manufactured parts, the manufacturer is interested in exploiting as much as possible the functional tolerances, in order to minimize the quantity of rejected parts. Hence, an objective function has been defined: it collects the current positioning errors, eventually weighted according to the tolerances values. This function is based on two independent variables, corresponding to the two part rotations that can be made to correct the position of the gear into the machine. Finally, the objective function is minimized to reduce as much as possible the residual positioning error; the calculated values for the two feasible rotations are provided to the machine to correct the position of the gear within the machining area. Then, the two reference surfaces are finished.
The role of MES. The integration of this monitoring and control system with a MES enables to analyze and use the collected data at different time-scales with different purposes. On the short-medium term, MES allows to check whether the process is stable or not. Further, when instability symptoms appear, MES can predict when the process is going to be out of control and produce parts not matching the expected quality. Thus, setup or maintenance interventions can be planned in a preventive approach, also taking into account further constraints, such as the availability of operators or already planned downtime. This kind of prediction is helpful to avoid producing parts that will be rejected, thus reducing waste. On the long term, MES information can be further analyzed to extract historical trends, to synthesize criticalities and identify the sources of issues and wastes. The integration of a traceability system strongly supports this functionality: in this case study, each workpiece is identified by a unique ID. Information concerning each gear, such as the time at which the centering operation occurs and the expected results of the alignment, can be collected and stored into a database. This information can be useful to monitor the results of the centering process over time, and identify the reasons for possible decays or drifts; however, a careful analysis of these data is necessary, since issues identified on the centering machine can be due to inefficiencies in the upstream workstations. The results of this analysis can be shared with different departments of the company. For example, the business unit can benefit from this information to define new strategies, or to correct the previously defined ones; the design department can use this experience-driven knowledge to improve the design of a product or process. The feedback information provided by the MES supports the test and validation of new process or product releases. This, in turn, enables the implementation of kaizen practices for continuous improvement, such as the PDCA cycle.
Manufacturing execution systems driven process analytics: A case study from individual manufacturing
Lea Mayer, Nijat Mehdiyev, Peter Fettke
Procedia CIRP
Volume 97, 2021, Pages 284-289
https://doi.org/10.1016/j.procir.2020.05.239
https://www.sciencedirect.com/science/article/pii/S2212827120314608
Online Loss Capturing Using MES
Various losses occurring in a manufacturing plant have direct bearing on reduced productivity and increased costs. Though some losses like asset failure can be measured using traditional methods, advance systems are required for root cause analysis. Then there are many miniscule losses which are very difficult to measure. Their frequency of occurrence can be high and hence their cumulative effect significant. These are referred to as ‘minor stoppages’. Yet other types of losses occur due to lack of coordination between various departments. This case study demonstrates how PlantConnect SFactory, a Smart Factory solution from Ascent Intellimation tracks and analyzes all types of losses and helps in eliminating some losses and reducing others. The installation is done in. Overall business requirements of customer are:
• Loss analysis
• OEE improvement
• Just-In-Time Maintenance
THE SOLUTION PlantConnect SFactory with integrated Loss Analysis Module was deployed. Losses are categorized as:
• availability losses
• performance losses
• quality losses
Pub 29.12.2021
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