The assembly line doesn’t work in today’s manufacturing environment of change and unexpected demand. High production variation requires new, adaptable assembly technologies.
Industry 4.0 helps production lines adapt to global market challenges. This technology improves flexibility, robustness, productivity, and traceability.
Paradoxically, manual production proves this. Assembly lines, especially for gearboxes, still rely on people. As product complexity and diversity increase, assemblers must process more information, increasing the chance of mistakes.
Digital work instructions, cobots, RFID, AR, and other Industry 4.0 technologies can aid. These devices help assembly line workers mentally and physically. They’re not always suitable. When should engineers invest in such technologies? We developed a strategy to assist.
Most study on Industry 4.0 technology has concentrated on quantitative statistics like throughput, ignoring subjective elements that are unique to each organization. Industry 4.0 technology must support the company’s strategic objectives to succeed. Technological and managerial competencies determine how each organization introduces new technology. One company’s success may not be replicated.
Our assessment system accounts for “soft” characteristics that are hard to define. We use objective facts and subjective. It also acknowledges criteria dependency.
Why subjectivity? Industry 4.0 success depends on leadership, according to study. Our strategy includes individual characteristics, interdependence, and decision-maker preferences to improve manual assembly.
Using corporate KPIs and strategic goals, identify the assembly line that requires improvement. Unbalanced workflow, heavy manual jobs, excessive physical or mental demands, process time variability, and extended lead times are common issues.
Secondly, engineers should undertake material and information flow mapping to assess the status, identify concerns, and identify investment opportunities. Evaluate limitations and line layout. To understand the line’s strengths and limitations.
Engineers should test standard work, line balance, and ergonomics before utilizing Industry 4.0 technology. Such initiatives will boost KPIs and lay the groundwork for future improvements.
Secondly, engineers should evaluate the assembly line’s capabilities, resources, and strategic goals. Afterward, a list of helpful technologies can be made. KPIs and strategic goals will rank these technologies. Discrete event simulation can anticipate the results of each proposed technology.
Most firms employ cost, quality, productivity, cycle time, energy consumption, and throughput KPIs to assess changes. Engineers should also create new indicators specific to the product family, operational context, and organizational goals.
We suggest PROMETHEE to analyze choices (preference ranking organization method for enrichment evaluation). The 1980s PROMETHEE technique combined mathematics and sociology. It is utilized worldwide in business, government, transportation, healthcare, and education decision-making. The PROMETHEE technique helps decision-makers determine the optimal option for their goals and knowledge of the situation rather than suggesting a “correct” choice. It offers a reasonable framework for constructing a decision issue, identifying and quantifying its conflicts and synergies, and identifying the major choices.
This strategy works effectively when a finite number of options subject to numerous conflicting criteria must be ordered by attractiveness. The preference function can contain any quantitative data for the decision-maker. Weight statistics indicate each criterion’s decision-maker relevance. All criterion weights equal 100%.
The preference function considers how two options’ judgments for a criterion differ. The decision-maker must assign weights to each criterion and do pair comparisons to generate alternate judgments of each criterion. A preference index can be calculated.
Finally, each option is rated. Based on the decision-criteria makers and weights, the top technology will be the best compromise.
Validation On An Assembly Line
In the university’s Learning Factory, a demonstration center for testing new technologies and teaching lean manufacturing, we built a model assembly line to make automobile gearboxes to test our decision-making system. Five workstations comprise the assembly line.
The gearbox has two base types. With 20 variants, the gearbox may be created. The gearbox has several gears, levers, screws, shafts, and other elements. It’s constructed in five steps. Each stage requires a particular amount of labor at one workstation. Real hand tools and supermarkets with parts are at each workplace. Workstations are connected via a conveyor.
The gearboxes were constructed manually utilizing paperwork instructions and manual data collection. Early attempts to balance the line revealed substantial cycle time differences across workstations.
Industry 4.0 might aid. Considering the line’s space and resource constraints, seven Industry 4.0 technologies were considered: RFID, digital work instructions, pick-to-light technology, AR, cobots, autonomous guided vehicles, and ergonomic manipulators.
In a manufacturing system, RFID is crucial for identifying and tracking assemblies. It completes the IoT in manufacturing by providing real-time information about items’ whereabouts and statuses.
The technologies were ranked by total investment cost, labor effort, workspace usage, and cycle time savings. Each technology’s ranking is shown below. Source: Universitatea Split
Digital instructions decrease complicated assembly time and mistakes.
LEDs on racks or shelves indicate to assemblers where and how many parts to choose from. Assemblers follow the lights. These systems commonly work with warehouse management systems.
AR may also enhance cycle time, mistake rate, mental strain, and worker attention.
While handling big weights and repeated tasks, cobots are ideal. Cobots and humans can operate together, allowing managers to assign duties more efficiently.
AGVs can replace workers who transfer components and assemblies to and from the assembly line.
The fifth manufacturing workstation’s ergonomic manipulator is electronic. Handling hefty gearbox components is easier using the gadget.
Figure 6: Five criterion weight sets were utilized in the authors’ investigation. Each weight change is a scenario. Scenario 0 has equal weight for each criterion. In the other four cases, one criterion had double the weight of the others (Scenarios 1-4). Graph courtesy Universitatea Split
Digital instructions decrease complicated assembly time and mistakes.
LEDs on racks or shelves indicate to assemblers where and how many parts to choose from. Assemblers follow the lights. These systems commonly work with warehouse management systems.
AR may also enhance cycle time, mistake rate, mental strain, and worker attention.
While handling big weights and repeated tasks, cobots are ideal. Cobots and humans can operate together, allowing managers to assign duties more efficiently.
AGVs can replace workers who move components and assemblies to and from the assembly line.
The fifth manufacturing workstation’s ergonomic manipulator is electronic. Handling hefty gearbox components is easier using the gadget.
Implementing the Decision-Support System
Our gearbox assembly line’s seven technology options were assessed using PROMETHEE. The technologies were ranked by total investment cost, labor effort, workspace usage, and cycle time savings. Cycle time and worker effort reduction were to be maximized, while the other two were to be reduced.
I4.0 technology attempts to reduce cycle time since production time depends on the workstation with the greatest cycle time. Cycle time data is collected using discrete event simulations. Our model assembly line data was used instead.
The cost of equipment determines the entire investment.
Workers’ experience estimates effort values. This data came from worker interviews. (Heart rate or body movement sensors or webcams can better quantify worker effort.)
New technology always changes a production line or workstation layout. For a robot, more room is needed. Space is needed less often.
Five criterion weight sets were employed in our investigation. Each weight change is a scenario. Scenario 0 has equal weight for each criterion. In the other four cases, one criterion had double the weight of the others (Scenarios 1-4).
We utilized the linear preference function with defined indifference and preference thresholds for every criterion in PROMETHEE. For defining an indifference threshold, the linear preference function is recommended for quantitative criteria. The indifference threshold is the biggest deviation the decision-maker considers inconsequential, whereas the preference threshold is the lowest variation needed to create a complete preference of one choice.
Under the “total cost investment” criteria, the indifference threshold is 135 euros and the preference threshold is 667 euros. If the price difference is less than 135 euros, both options are equally favoured. Alternative 1 is favored over alternative 2 if it costs 667 euros or less.
Pictures 5, Figure 6, and Table 1 represent PROMETHEE input data for each scenario. Figure 7 compares each scenario’s top technological options.
RFID is the cheapest method that doesn’t increase layout in scenario 0. RFID is the best compromise in scenario 1, which prioritizes cost reduction. This is expected as it’s the cheapest technological choice. In scenario 4, if “layout growth” is minimal, RFID is the best compromise since it needs the least area to implement.
Cycle time reduction and labor effort were crucial in Scenarios 2 and 3. As the cobot reduces cycle time, it was the best solution in Scenario 2.
Scenario 3’s best choice was the manipulator because to the massive labor effort decrease.
Most AGVs finish last, which is unexpected. As this technology is widely utilized in industry, it might be perplexing. Some technologies may not be reflected well in our criteria.
Our study underlines the relevance of decision-makers’ preferences, reflected by the weights of the selected criteria. This research also stresses the requirement for enterprise-specific criterion definitions. Each company must be careful while setting and weighting criteria. It’s best to work together.
Iterative is our method. After one technology is deployed on the assembly line, the complete selection method must be redone to determine the next best technology for the altered condition as the criteria for the following iteration may vary.