Research Article Summary: “Control and Decision Support in Automatic Manufacturing Systems”
The new concept and structure of the Manufacturing operating system (MOS) can provide the structure for managerial support and help in managerial decision-making. The MOS system can control the complex environment of unstructured decision-making, thus relieving managers from complex tasks. Automated systems are rapidly increasing in different industries and organizations. The current automatic manufacturing systems pose several limitations because they do not consider the systems’ state and goals. The MOS has three main components; data management organizes and manages data used for the system’s operation. The logic management component is responsible for the retrieval, organization, storage, and execution of algorithms used in the system and the interfaces used to aid both the users and controllers. This paper summarizes the article titled “Control and decision support in automatic manufacturing systems” which explains the concept of MOS deeper.
The Problem Addressed and Its Significance
Automatic manufacturing systems are popular in many modern businesses and industries. Most are usually composed of complex process controls where a set of units such as machines are arranged systematically into one unit. Nonetheless, the current systems are faced with various limitations when it comes to providing managerial decision support as the systems cannot handle unstructured decision problems. The current Automatic Manufacturing systems cannot constantly review the changing state of the systems. Thus, computer systems help in deterministic and non-deterministic tasks such as scheduling, inventory control, and capacity planning.
A Computerized Integrated System (CIM) may be necessary to solve this problem. Show that Computerized Maintenance Repair Systems (CMMS) are more reliable than traditional systems.
The complex environments of the systems limit the Persons who handle these systems, and hence the tasks are delegated to the decision support systems. These systems are called MOS and control and guide all facets of the manufacturing process. This system is applied in various areas, including supply chain management, quality control, scheduling, and maintaining budgets. Unlike humans, these systems provide consistency, give data-oriented results, streamline communications, and unify the company culture. The simple concept followed by the MOS is the plan, do check, act, and steadily improve the performance. MOS is significant to the course under study because it enables the management to detect and correct mistakes before they become serious hence helping prevention of losses.
Background and Known Practice
The general architecture of a MOS model consists of the following parts. First, there is the user interface where users and controller give their input and receive their output. Then there is the MOS operating system, where information is processed. The database section deals with reference data, operational data, and decision logic (Nof et al., 1980). Lastly is the interface with the process controllers that help the system run efficiently. This article under review did get into the scope of the architecture of the MOS model. Four primitives: operators, operands, paths, and conditions were used in the paper to model the manufacturing system. An operator is an active element, such as a person who performs or indicates which action to perform. An operant indicates what items to apply the actions and the ones to avoid. Paths can be defined as the connectors between operators and operands (Nof et al., 1980). Conditions in the MOS are defined as relevant operators’ attributes that enable the activities. MOS can be modeled using Petri nets which are directed bipartite graphs that two different elements called places and transitions depicted as circles and triangles. Petri nets are handy in depicting asynchronous activities associated with MOS. Petri nets are also famous for modeling structures of hardware mechanisms in complex computer systems. The modeling used in the paper under review was a technique named Evaluation nets (E-net), which uses one particular variation of the Petri nets.
New Methods and Results
The control procedure of the MOS in the paper under review had four significant steps. First was the identification of operators. The operators that affected the status of the system are identified. Then all the possible actions performed by the affected operators are identified. Then the decision logic and implementation were done. The second step was to retrieve all possible actions and decision logic made. Thirdly, the action was sorted out, and recommendations for actions were set. If there is no recommendation, nothing was done. If there was one, it is implemented. If there is more than one, the fourth step is taken. The fourth step entails a higher-level evaluation is taken, and the decisions have to be made by the manager.
Strengths and Limitations of the Article
This article possesses many strengths, but it also has its fair share of limitations. The biggest apparent strength while reading the article is that the argument is explicit. The authors go a long way to ensure no vagueness or ambiguity in the paper by conducting and giving real-time examples and case studies. The authors have inserted numerous figures to help clarify their various arguments concerning MOS to confirm their arguments. Another strength of the article is that the paper is orderly and the topics are well defined. The paper’s aim is also well stated, and the conclusion gives a clear opinion of the authors. The evidence provided in the article is convincing contradictory arguments may be hard to produce.
This article has a weakness in that the keywords are not clearly defined. A quick look reveals that the article is written for only expert readers, and even they could have challenges understanding some terms. Arguments in the paper are more technical than logical and thus can be viewed by the majority as a weakness. The authors did not manage to capture the readers’ emotions in their work an argument of it being a scientific article can be made. Compared with other articles on the topic, the paper’s clarity is below average; hence, it can be considered less clear and understandable. The authors did not also take their time to consider secondary sources.
Evaluation
The readers’ understanding of various manufacturing systems, especially the MOS, is likely to improve upon reading the article. I differentiated critical terms used in the MOS and its related fields. I realized how these systems help relieve managers of complex monotonous work with their intervention. I saw how the previous manufacturing systems could not handle unstructured work and how the MOS one differed. My creativity and interest in manufacturing systems increased and made me wish to contribute to the field. In conclusion, though hard for an average reader to comprehend, the article offers great insight into the concept and structure of the MOS model and its implementation control.
Research Article Summary: “Artificial Intelligence in Manufacturing Planning and Control”
Bullers, Nof, and Whinston’s (1980) study explored specific issues of concern in manufacturing systems planning and control. Their research focused on the problems relevant to automatic operations. According to the authors, they wanted to establish how artificial intelligence can be incorporated into the manufacturing environment to provide a solution to the possible challenges experienced. They based their argument on well-researched work elaborating their findings using different sets of data. They further explained the points using illustrative problems to indicate how glitches can be handled through a decision support system, especially when there is conflict occurrence. The researchers have cited several scholars who have contributed to different aspects such as computer information that is closely related to manufacturing and operations that aids managers in decision-making. The article summary will explore the significance of artificial intelligence in facilitating decision-making in manufacturing planning and control settings.
The Problem Addressed and Its Significance
Based on Bullers et al. (1980) study, the research problem is the decision-making process in manufacturing planning and control that is complex and makes the management and control of manufacturing activities complicated. According to the authors, manufacturing environments are mainly controlled by shop-floor computers and other process controllers with predetermined system range functionality. Since the machines are operating in an interdependent manner, they require timely decisions at different levels of operations. From the view of writers, irrespective of the computer’s ability to process large information from the provided data, there is a need for human intervention to sift through the data to command them to execute some crucial actions. The inability makes them able to make proper planning and control decisions at the manufacturing level.
The problem addressed by the researchers is crucial to the technological world. In the current generation, most businesses are applying digital technology to enhance their business operations. Without a proper understanding of the shortfalls that the management may experience if they completely rely on the decision made by the process control computer, the planning activities may face dire challenges. The issue covered by the authors elaborates shows the need to have a deep understanding of how computers operate to increase their output efficiency. The problem allows me to explore more areas involving automatic machine system operations related to my project.
Background and Known Practice
According to the work of Bullers et al. (1980), there are three main levels of activities involved in managerial decision-making in manufacturing planning and control. They include tactical manufacturing, production and inventory, and operational process of material flow. The authors argued that the manufacturing environment is dynamic and entails various factors that significantly influence all the levels that require decision-making. The article states different machine systems have been manufactured to enable managers to make constructive conclusions concerning the manufacturing activities they encounter.
The authors added that in settings where computers control the operations of different facilities automatically, supervisors face complicated roles, which is critical to the respective organization. This is because most of the systems are operating interdependently, thus requiring properly timed decisions in various stages of manufacturing. According to the researchers, the manufacturing environment is controlled by shop floor and process controllers. The systems are programmed in prior, thus making them incapable of adapting and processing planning decisions that contain unstructured data.
New Methods and Results
Bullers et al. (1980) formulated a new approach that can enable managers to have productive decision support without relying on computer operators to execute actions for the system to process the available data. Based on the article, the authors stated that artificial intelligence technology is the ultimate solution to the problems associated with the inability of the system to provide effective planning and control. They continued to say that the technology will facilitate automatic manufacturing, thus increasing the productivity of the management.
The article states that having the technology will allow the transfer of human intelligence needed to make crucial operational decisions to the computer. This approach will ensure most of the judgments are made automatically by the machines without human intervention. According to the authors, the procedure will speed the process of decision-making, thus preventing delays that can tamper with manufacturing activities. Artificial intelligence can handle a large volume of unstructured data and undertakes logical manipulation to resolve possible machine conflicts.
Based on the article, the artificial intelligence technique assists computerized machines in solving primitive problems by methods. The approaches use the built-in predicate to assess and evaluate the probable issues. According to the authors, the problems are categorized concerning the procedure type applied to examine the predicate. They include a primitive database management system (DBMS), which applies the retrieval methods and primitive subroutine procedures that assess predicate and provide either true if the value of x
Bullers et al. (1980) state that assessing predicates enables the machines to have a varied number of elements for the given symbol of the predicate. The article defines a predicate as P (al=xl, a2=x2), where P indicates a class of predicates that uses different combinations of two characters. The approach allows the system to reduce the predicate symbol used in representing the problem. It forms the basis for resolving issues in the static time sphere of management operations.
Strengths and Limitations of the Article
Some of the article’s strengths are generalizability because the authors included other peoples’ findings in their research to provide a wider perspective of the problem question. It is also reliable in that the study challenge is posted accordingly so that users of the paper can easily grasp the purpose of the search. Despite the fortes of the research, it has some weaknesses, such as the use of complex language and statistical formulas that are not easy to comprehend.
Conclusion
The article has enhanced my understanding of artificial intelligence and how it can influence and improve the field of technology. From the analysis provided, I can now resolve computer problems based on the procedures outlined in the paper. Generally, the study has contributed magnificently to the challenges most managers face during planning and control. Based on the methods used in reducing the problem, the logic language should be simplified to allow an easy understanding of the procedures by different computer operators.
In summary, the article “Artificial Intelligence in Manufacturing Planning and Control” has provided significant insight into how the productivity of the manufacturing environment can be effectively improved by adopting the use of artificial intelligence. The technology allows managers to have access to reliable information from data processed by the systems. Furthermore, the paper elaborates on how the engineering operates, thus making it easier for users to understand how the computers produce the details needed for operational activities.
References
Bullers, W. I., Nof, S. Y., & Whinston, A. B. (1980). Artificial intelligence in manufacturing planning and control. AIIE transactions, 12(4), 351-363.
Nof, S. Y., Whinston, A. B., & Bullers, W. I. (1980). Control and decision support in automatic manufacturing systems.AIIE Transactions, 12(2), 156-169.