Assessing the Suitability of Automation Using the Methods–Time–Measurement Basic System (2024)

1.1. Structure of the Article

Motivated by the abovementioned situation, this paper presents a procedure model for identifying HRC potential based on MTM-1. This allows the data previously collected for time optimization to be further used for automation suitability. For this purpose, a classification of existing methods and work in the state of the art (Section 2) is first given before Section 3 describes the developed method in detail. The process is then evaluated using an example product (Section 4) before the results are summarized in Section 5. Finally, an outlook and potential for further work are given in Section 6.

1.2. Requirements for the Procedure Model

Based on the challenges described above, requirements can be derived for developing the method and can be divided into three categories: (1) model structure, (2) method, and (3) results.

• To develop the model and enable a systematic approach for transparent results, an iterative and step-by-step approach and a description of the current situation should be provided. (1)

• Furthermore, due to the complexity of the assembly processes described above, the model should focus on the HRC and the assembly processes, considering the performance capabilities of humans and robots. (1)

• The method must include the possibility of a knockout (K.O.) criterion for HRC so that the evaluation can be terminated directly to prevent unnecessary planning processes. (2)

• Time measurement, more specifically the use of MTM, is necessary to ensure industry adaptability due to the method’s high prevalence. (2)

• In addition, the method’s evaluation criteria must be transparent regarding the suitability of the automation or the HRC for good reproducibility. (3)

• Finally, the results should be transparent and comparable next to economic efficiency. (3)

2.1. Evaluation Methods with the Scope of an Assembly-Friendly Product Design

As described above, four established methods in the industry evaluate the product design in the context of the suitability for assembly. The overarching aim of the B&D method is to reduce costs by saving time during the handling and assembly processes. This is achieved by reducing the number of components within the assemblies [14,15].

The Lucas method is subdivided into three separate analyses and is based on a relative scoring system. In contrast to the B&D method, the functional analysis is used for early design analysis. Not all design questions must be fully answered at the time of application. The components are categorized into two groups: essential and non-essential components. Non-essential components should be eliminated to reduce the number of components to a minimum [16].

The Westinghouse method calculates product complexity using the assembly time, a complexity factor, the assembly time value, and the part efficiency. As in the previous methods, the complexity is determined using predefined influencing factors, resulting in the assembly time value. For the assembly time, the processes are classified as reaching, gripping, and bringing, and the process itself and the assembly time are calculated [17]. The modified Westinghouse method simplifies and differs from the superordinate method in creating the joining sequence diagram and eliminating non-essential components [9,13].

These methods are designed to identify weak points in the product design. However, this approach focuses on the assembly processes to identify tasks for humans and functions with a high automation potential. The Hitachi-AEM is categorized as an evaluation method for the assembly process [9,13,18]. According to the Hitachi AEM, two key figures are determined and evaluated to assess the product design. On the one hand, the complexity of the handling and assembly processes is determined using the evaluation factor E. On the other hand, the cost evaluation factor K is used to determine the savings potential through the optimized product design. As with the methods described above, predefined operations are used to classify and evaluate upcoming processes to estimate the handling and assembly effort. In contrast to the B&D and Lucas methods, the Hitachi AEM does not differentiate between a manual and an automated assembly because of the strong correlation between an assembly-friendly product design and the resulting simplified assembly [13]. With that in mind and the presented approach of an optimal distribution of the assembly task, this method is unsuitable for that procedure model.

The MTM ASSOCIATION e.V. has developed a method (ProKon) to minimize the costs during product development. It evaluates the assembly effort and focuses on low-cost solutions through targeted modification of parameters that the designs can influence. The procedure is divided into seven steps. The first step is remarkably similar to the methods described above, whereby various evaluation criteria such as weight, material, geometry, or specific handling conditions are analyzed. This is followed by the determination of the assembly processes. In step three, the assembly position of the individual parts is determined on the assumption that the parts to be assembled are not in an optimum state of order. After that, in step four, the individual parts and higher-level assemblies are then evaluated by an evaluation matrix. This matrix differs from the card in that it analyzes the time estimation of the assembly process. Steps 5 to 7 deal with optimizing the product design, which is outside this approach’s scope and is therefore not considered in more detail [3].

Since ProKon focuses on an assembly-friendly product design and process and does not distinguish between an automated or manual assembly task, this method does not fit this approach as well as the methods before.

2.2. Evaluation Methods with the Scope of Human–Robot Collaboration

Bauer et al. present a procedure model based on a cost-effective identification of practical HRC applications developed by the Fraunhofer Institute for Industrial Engineering (IAO). The model is divided into three steps and focuses on avoiding large volumes of data. Despite its simple structure and application, individual company goals are considered. In the first stage, assembly system data are analyzed for the three fundamental areas of classic system planning data: the problem situation, the degree of preparation of material provision, and potentials. The second stage is planning data at the workstation level, and potentials are considered. Finally, the last step is evaluating the process and product level. Subsequently, the assembly processes with the most significant automation potential can be selected, and HRC solutions can be implemented based on the preselection and evaluations [19].

Weber and Schüth developed a model for introducing HRC. It is structured on a step-by-step basis and is intended to enable companies to prioritize the factors to be checked successively. The focus shifts from the process to the product to the resource regarding HRC suitability. The first three steps consider component orientation, gripping properties, temperature, or flexural rigidity. In addition, the method evaluates the workstation and training needs. It concludes with an assessment of economic efficiency [20].

The model approach developed by Petzold et al. is divided into five steps. It defines six requirements for analyzing the potential for the use of HRC: specific focus on industrial assembly activities, method-supported analysis and evaluation, strong process orientation, consideration of quality, evaluation of economic efficiency, and consideration of ergonomics. The authors use MTM in their model approach for the assessment of economic efficiency but not for the identification of potential processes to be automated. To evaluate the use of HRC, both the assembly processes and the product design and geometry are considered [21].

The SafeMate method was developed to quickly and intuitively identify and assess the potential for using HRC. The research project of the Institute of Assembly Technology and the Institute of Production Systems and Logistics at the University of Hanover is intended to serve as a guideline for safe and accepted HRC implementations in companies. The SafeMate method consists of two successive stages. In the first stage, the added value of the HRC application is assessed, while the second stage evaluates the technical implementation [22].

The KoMPI Quick Check was also developed as part of a research project and focused on creating a new method for the integrated planning and realization of collaborative workstations for variable production scenarios. It is used to assess and identify HRC potential. This method, divided into three levels, uses a point system based on process criteria for evaluation. A good knowledge of the evaluated work system is a prerequisite. No other special prior knowledge is required [22,23].

Linsinger et al. present a method that identifies the suitability of HRC potentials in five steps. Step 1 begins with an overall analysis of the assembly system and is supplemented by a detailed examination in step 2. Here, the individual stations are analyzed using cycle time analysis and a checklist to assess their technical suitability. This depends on various factors, such as the dimensions and geometric characteristics of the product or the supplied assembly part. The assembly station with the highest HRC potential is then analyzed in step 3. This analysis focuses on determining the division of labor between robots and humans. It uses the checklist described above and the MTM-1 for this purpose. Steps 4 and 5 deal with the application scenarios’ design and implementation planning [24].

Teiwes et al. present a method to identify the HRC potential in automotive assembly lines based on the MTM Universal Analysis System (UAS). The authors describe their approach in three steps. Step 1 evaluates the automation potential for each movement within the assembly. For this purpose, each module of the MTM UAS is assigned a degree of automation potential based on the description of each module. The value determined from this is multiplied by the frequency of the activity in step 2. The final step summarizes the values to obtain an overview of the assembly line [25].

2.3. Classification of Described Literature

The literature will be categorized based on the state of the art presented and the previously derived requirements. This results in the following analysis shown in Scheme 1. Overall, no model in the literature meets all the requirements for this approach. Although the model structure and result categories fulfill the requirements placed on the model in various constellations, deviations from the requirements can be identified in the fulfillment of category 2. In particular, the combination of MTM time data analysis and the consideration of a K.O. criterion cannot be found in existing approaches.

With that in mind, the comprehensive development of a procedure model that fulfills all requirements is described below. Individual aspects and methods from the previous analysis are incorporated into the process model as steps are developed in-house. A detailed explanation of all aspects of the process model ensures a comparatively simple and transparent application. However, prior knowledge of performing an MTM-1 analysis is required.

3.1. Analysis of the Actual State

The assembly processes are described in a process profile based on the MTM analysis [3]. The overall work task, including a brief process description, is recorded, and the workstation is defined. The structure of the workstation, as well as size and distance information, are particularly relevant. This step also includes a detailed explanation of the process or assembly result (output). Furthermore, all individual parts, machines, tools, and any means to facilitate the assembly process (input) are recorded. For complex assembly processes, the second phase divides the main overall work task into several individual SPs. Each SP fulfills a task within the overall process.

A coding technique is developed for uniform identification of the SPs, which is based on the 12-digit code of the MTM system but has been shortened and simplified. As this code is only used for error-free identification and is similar in principle to the existing code of the MTM system, the functionality of the code is not explained further here. After that, the SPs are analyzed by the MTM-1. The MTM-1 basic system is used, as it serves as a basis for other MTM systems and enables a high resolution or detailed activity descriptions using the numerous basic movement modules. For that approach, its formula has been extended by the column of the suitability of automation (Scheme 2).

The individual time requirements are calculated based on the MTM-1 code, which describes the process step using the granular movement modules described. Double-handed tasks and similar restrictions for parallelization are also considered automatically. Behind this code, time requirements are stored so that at the end of the MTM analysis, the results show processes with a high optimization potential. Aspects such as long distances, many tasks with a high time requirement, or feeding of parts with an unknown orientation are made visible for short-term optimizations.

3.2. Evaluation of the Automation Potential

To evaluate the automation potential, the MTM-1 formula is extended by the column for the degree of automation suitability (DAS). It is important to note that this approach evaluates the potential of complete automation (step 5), a coexistence, or a synchronized assembly process regarding the requirements of HRC (step 6). The potential of the latter two will be described in the next section. Automation suitability is evaluated using a Likert scale with a point scale from zero to three, where high suitability is indicated by a three. In this approach, in addition to the time requirements, the automation capability ratings of the individual granular motion modules were also derived from the MTM-1 code. For this reason, the criteria used to assess the suitability for automation are also heavily dependent on the descriptions of the MTM basic movements, as found in the data cards [3].

If individual tasks are rated zero in terms of DAS, the entire SP is classified as non-automatable and is to be regarded as the required K.O. criterion. The same applies if at least three activities are assessed with one point. To evaluate the individual tasks regarding suitability for automation, evaluation criteria are defined that consider, among other things, the strengths and weaknesses of a robot as well as economic aspects. The approaches described in Section 2 have different combinations of evaluation criteria for automation suitability. Still, they all have in common that they are based on the four established methods. Due to the link with the MTM-1 code, the evaluation criteria are not to be understood as rigid but based on the cases depicted in the MTM-1 method. Nevertheless, the requirements described by the MTM cases can also be assigned to the methods mentioned, as you can see in Scheme 3. The established methods focus on the product design and its assembly; in addition, this approach rates the assignment of tasks between humans and robots so that the criterion of accuracy was added.

Movements over a more than 60 cm distance reduce the DAS by one. Longer distances are associated with increased effort or require a large, more cost-intensive robot replacement. How objects are approached and the different gripping movements are further criteria summarized under EC2.

Industrial robots can easily approach and pick up a single, easy-to-grasp object that is always in the same place (DAS = 3). Both grasping grips, touch grips, and transfer grips are possible. If the location of these objects changes, for example, when removing them from a pallet, additional programming must be carried out for precise positioning. Alternatively, coarse object recognition can be implemented, e.g., using a camera or sensors on the gripper. The evaluation factor is, therefore, reduced by one. For tiny (smaller than 3 × 3 mm²) or fragile objects, increased financial expenditure must be expected for special grippers, sensors, and precise calibration. Gripping operations are like the need to reposition an object and are assessed under EC5.

The maximum load-bearing capacity of such robotic systems is a limiting factor in evaluating HRC suitability. Handling weights over 10 kg is regarded as an aggravating factor and leads to a reduction in the DAS by one. Moreover, handling, in general, is an essential factor in the suitability of automation. For example, parts that are difficult to grip, flexible, or oily make special programming necessary or may require gripper or robot adaptation. Difficult access to a part to be separated or a joining location diminishes the automation capability. In addition, the maximum travel speed of the robot can be negatively affected. For example, when joining or separating, this is considered by subtracting one point from the suitability for automation.

Depending on the fit class, symmetrical joints pose no difficulties for automation (DAS = 3). Semi-symmetry allows several joining positions, and the robot can only make minor alignment corrections. Thus, no points are deducted for this symmetry case. In the case of non-symmetry, on the other hand, some significant alignment corrections and a fixed joining position require increased effort on the part of the robot. Therefore, a maximum of two points are awarded for this characteristic.

With a loose fit class, there are no restrictions on automation suitability. Tight fits and joining tolerances can only be achieved with time-consuming calibration, hence minus one. Cases with fixed fit classes and, therefore, optionally low joining tolerances or high force requirements, are generally regarded as K.O. criteria and given zero points. Similarly, insertion and the associated application of pressure for fits are assessed. The need for a highly accurate process also impacts the DAS and is assessed under the last criterion of accuracy.

All other MTM-1 process modules whose actions can be carried out by a robot without complications or additional effort, e.g., simple gripping movements, releasing objects, light pressure, etc., receive three points and are rated with the highest suitability for automation.

To obtain an overall DAS, the column of suitability is multiplied by the corresponding values from the column of the “number and frequency (N × F)”. The sum of all values and the subsequent division of it by the total number is used to calculate the provisional average and, thus, the provisional DAS. A high average indicates a high potential for automation, while a low value implies the exact opposite. Furthermore, the standard deviation is determined considering the respective variance. This serves to verify and refine the assessment, to identify potential “outliers”, and to assess the suitability of HRC subsequently. Figure 2 uses a four-field matrix to illustrate the potential result areas and provides instructions for the next steps.

The SP under consideration should be in the second quadrant for suitable automation suitability. SPs in the first quadrant are outliers and should first be attempted to be minimized. The two lower quadrants indicate a low DAS, whereby the HRC potential should be investigated further in all cases.

3.3. Evaluation of the Human–Robot Collaboration Potential

In step 5, the HRC potential and allocation of labor is determined. The test is carried out in two stages:

Considering the strengths and weaknesses of humans and robots, the tasks of an SP are divided between these two actors. Activities with a low DAS are assigned to a human, and those with a high DAS are assigned to a robot. The human activities are entered on the left, and robot activities are on the right. For all activities classified as “manual”, the automation suitability factor is deleted from the MTM-1 analysis. The updated final automation suitability level is then compared with the original value determined from the previous phase. If there is no significant improvement or a substantial imbalance in the allocation of tasks between humans and robots (greater than 75:25), the allocation is discarded. If there is a significant improvement in the value (average greater than two points) and no such imbalance in the allocation, there is a high HRC suitability.

Based on the previous interpretation of the results, the HRC potential is also depicted in a four-field matrix (Figure 3).

Depending on the characteristics of the final average or the DAS and their standard deviation, a result classification is made possible, and instructions for further procedures are derived.

3.4. Assessment of Economic Efficiency, Detailed Planning, and Optimization Potential

When applying the procedure model described here or from previous MTM 1 analysis, the respective times for the described SPs are available, based on which an economic application can be examined. For this purpose, the investment costs are compared with the expected productivity effects and time savings. Here, the total MTM times of the MTM-1 analyses are used as a basis, and the original actual times (before HRC) are compared with the planned times (after/with HRC). A subsequent amortization calculation provides information as to the duration from which the investment is worthwhile and helps with the risk assessment.

Phase 7 of the process model includes detailed planning and subsequent HRC implementation; the qualified HRC potentials are implemented for the respective assembly process, and the introduction, including all measures, is planned. The employees are informed about the planned steps and, if necessary, involved in developing the workstation design. In addition, training on safety and the essential functions of the robots must be carried out. Depending on the type and scope of the HRC implementation, additional needs-specific qualifications may also be relevant.

Due to the high variability in assembly, further or new optimization potentials are examined after detailed planning and implementation. This focuses on activities in the respective analyses with high time requirement values or low suitability for automation. It is checked whether an improvement in the time requirement and the suitability for automation can be achieved through change measures. These can consist of further adjustments to the provision of parts and tools or pick-up options, path changes, or joining aids. In addition, scenarios with new cross-process automation solutions, e.g., acquiring additional machines, are considered in this phase. A redesign of the workstation is also possible. The process model is iterated to qualify further HRC potential. This begins with phase 4.