The Role of Industrial Engineering Technology in Industrial Systems
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The Role of Industrial Engineering Technology in Industrial Systems
Introduction
Quality management is essential to both manufacturing and service industries. Industrial engineering technology is vital to optimizing production, safety, and quality of products and services. Quality management utilizes different industrial engineering technology such as statistical process control and quality circles to ensure quality products that not only meet quality control requirement but also meet consumer satisfaction. The paper seeks to explain the objectives and applications of the sampling plan, quality control procedures, and also emphasize the importance of human factors engineering.
Q1; Part A: Completely and thoroughly explain the objective of acceptance sampling plan and how it is used in the industry by giving an example
Consider an instance where a company receives a shipment of about 5000 widgets from a supplier. Is the delivery of the required quality to be included as part of the inventory? The company may use different approaches to decide. They may look at all products; 100% inspection, or put the whole shipment into stock; 0% inspection, or even look at a sample; acceptance sampling. Acceptance sampling plan provides a method that helps protect from processes that are not capable (Kenton, 2019). In acceptance sampling, the objective is not to control the means of production but to access incoming products against the problems on the supplier’s process.
Simply put, a company may not be able to test all products. Such a test would be 100% destructive in terms of time, cost, and burden of testing every single product. As such, the objective of acceptable sampling is to determine whether the specification or contract agreed upon is met. For example, if a company manufactures firecrackers, acceptance sampling plan may be used to assess the quality of the entire lot. There are two types of acceptance sampling; sampling by attributes and sampling by variables.
Sampling by attributes involves a binary decision; accept or reject, based on classification of samples while sampling by variables relies on specific physical measurements (Shmueli, 2016). Additionally, in attribute sampling, there are single, double, multiple, sequential, chains, and skip-lot sampling plans done on discrete data such as the number of defects. On the other hand, a variable sampling includes single, double, and sequential sampling plans that measure continuous data such as time, length, or volume. In the case of a firecracker, a fuse may be tested as part of sampling by variables. Variables can be converted to attributes if a specific set of the specification is available (ASQ.org, 2020).
In the example of firecrackers, acceptance sampling will involve determining the exact number of products to be tested; lot size (N), number of product in the sample (n), and lastly the acceptable amount of nonconforming product (c) within the sample to ascertain quality. The test results in a number of nonconforming products, r. However, as long as r is less than or equal to c, the whole lot (N) is accepted based on the sample size (n).
Q1; Part B
The American National Standards Institute (ANSI) and American Society for Quality (ASQ) developed for ANSI/ASQ Z1.4 standard for Acceptance quality limit (AQL) sampling plan table. Ideally, important set different AQL levels for critical, major, and minor defects (Ge, 2020). Put merely, AQL represents the maximum number of nonconforming products beyond which the whole lot is rejected. There are five main parts to fully utilize the single-sampling ANSI/ASQ Z1.4 table: lot size, inspection levels, sample size code letters, acceptable quality levels and acceptance and rejection points (Kenton, 2019).
Determining the Lot Size
The lot size refers to the total order quantity. A lot size of 2000 is given. In the Z1.4 table, a lot of size range with the desired lot size is considered.
Determining Inspection level
The first ANSI/ASQ Z1.4 table is categorized into simple inspection level and general inspection level. Shmueli reports that an inspection level chosen is critical because it determines ANSI sampling plan that will be adopted and also the scope of in-site product inspection (2016). The GII level is the default level for most companies involved in inspecting consumer goods. The lot size (2000) and inspection level (GII) gives a sample size code letter K (See Appendix A). The special inspection level is ideal for special products and only conducted on a small sample (Ge, 2020). Once the sample size code letter is ascertained, the sample size is obtained. Letter K has 125 sample size.
Acceptable Quality Level
In the question, an AQL of 1.5 is given. The sample size code letter K with sample size of 125 enables a quality control professional to ascertain acceptance points and rejection points. Accordingly, the acceptance point represents the max number of defects that is acceptable within the sample size chosen while the rejection point is the min number of nonconforming products need to reject the entire lot size. An AQL of 1.5 and sample size code letter K with 125 sample size corresponds to the acceptance point of 5 defects and rejection point of 6 defects (See appendix B).
Question 3: Explain why is human-factors engineering important? Discuss by giving an example.
According to Holstein & Chapanis, human factors engineering also called ergonomics, aims to utilize information about psychological and physical attributes to design devices and systems that are safe for human use (2020). Human-factors engineering is a profession, a body of knowledge, and a process. As a process, ergonomics is important in the design of work methods, machines, machine systems, and environmental design to guarantee safety, productiveness, and comfort of human users and operators (Holstein & Chapanis, 2020). On the other hand, as a body of knowledge, ergonomics involves the collection of data and principles concerning human behavior, capability, and limitation with regards to operating machines, tasks, and the environment (Kusiak, 2018). As such, ergonomics aims to optimize industrial processes where humans are involved.
Lastly, as a profession, ergonomics involves different professionals who work together to optimize processes and improve quality. Human engineering relies of anatomy, toxicology, psychology, industrial design, environmental medicine, engineering and research operation (Holstein & Chapanis, 2020). Even though these specialized groups have different emphasis, they share the same aims and objectives (Caputo et al., 2019). It is important in optimizing human well-being by design of a safe working environment. An examples of ergonomics is where a company rolls out hand truck to assist worker involve in lifting and hauling heavy loads. A hand truck eliminate chances of back pains, makes work easier, reduces risk of injury, and also increases performance.
In conclusion, a sampling plan aims at giving a company insurance that a given shipment contains products that conform to the agreed contract and quality standards. The ANSI/ASQ Z1.4 table is a useful statistical sampling plan that helps a QC professional ensure that sample size selected gives a reasonable representation of the quality of inventory received.
References
Attribute Sampling Plans – Inspection by Variables & Attributes Z1.4 & Z1.9 | ASQ. Asq.org. (2020). Retrieved 24 June 2020, from https://asq.org/quality-resources/sampling/attributes-variables-sampling.
Caputo, F., Greco, A., Fera, M., & Macchiaroli, R. (2019). Digital twins to enhance the integration of ergonomics in the workplace design. International Journal of Industrial Ergonomics, 71, 20-31.
Ge, C. (2020). What Do the Parts of the ANSI ASQ Z1.4 AQL Table Mean? Intouch-quality.com. Retrieved 24 June 2020, from https://www.intouch-quality.com/blog/anatomy-of-the-ansi-asq-z1.4-industry-standard-aql-table.
Holstein, W., & Chapanis, A. (2020). Human-factors engineering | Definition, Ergonomics, & Examples. Encyclopedia Britannica. Retrieved 23 June 2020, from https://www.britannica.com/topic/human-factors-engineering.
Kenton, W. (2019). Acceptance Sampling: The Quick Fix Quality Control Method. Investopedia. Retrieved 23 June 2020, from https://www.investopedia.com/terms/a/acceptance-sampling.asp.
Kusiak, A. (2018). Smart manufacturing. International Journal of Production Research, 56(1-2), 508-517.
Shmueli, G. (2016). Practical acceptance sampling: A hands-on guide. Axelrod Schnall Publishers.
APPENDIX
Appendix A
Appendix B