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Process management can assist business leaders and analysts to develop a new product or service, complete a process such as a product order, manage customer service, and educate a new employee.

Although process management is a beneficial practice, implementing it can be challenging for business analysts might confuse it with other, slightly similar process, such as process improvement, process workflow management, process intelligence, process planning and process design.

We aim to describe process management, explain its differences with process improvement and workflow management and explore 5 BPM stages.

What is process management?

Business process management, BPM, is the method for standardizing and aligning company processes with their strategies and goals for effective management of business operations. BPM includes the analysis of firm’s processes to ensure efficient and smooth cross-departmental operations and exchange of information. The processes can be relevant to customers, partner companies, systems or suppliers.

Process management can automatically measure processes, check processes workflows, and manage them effectively with Business Process Management (BPM) Software or with a process intelligence tool, such as process mining.

BPM vs workflow management vs process improvement

Process management and workflow management are distinct concepts because process management deals with business processes as a whole. Workflow management, on the other hand, deals with the definition, analysis, and coordination of a single process’ workflows, unlike workflow management where a single process’s workflows (e.g., invoice register).

These two concepts are related to each other because process management and workflow management ultimately fuel process improvement. Process improvement detects inefficiencies and improves the process performance for a better customer experience. Explore other benefits of process improvement through real life examples and case studies.

What are the 5 stages of process management?

Business Process Management (BPM) contains five main steps:

1. Analyzing

At this pre-step to management, business analysts gather data, analyze it, and model it to understand the processes and to identify the ones to improve. The analysts prepare performance metrics that assess whether the process is efficiently executing the operations and adding value to the company.

Quick tip:

The analysts should dedicate some time to choose a relevant set of tools to analyze their data. For example, data science tools can be limited for the companies that to leverage their process event log data. In this case, these firms should look for a process mining and task mining software which apply data science techniques to process data.

2. Designing & Modeling

After understanding the current situation, the analysts should design or modify already created models to map and document ideal end-to-end processes. The documents can include information about the time, duration of each task, employees involved in the workflow.

Quick tip:

The analysts should ensure that the designed or updated processes are accurate and worthy of implementation. Also, once it is designed, analysts should plan the process by diving into the process workflows visualization to identify steps and activities.

3. Implementing

At this stage, analysts apply the formerly designed and planned process. Implementing the process is often required to update procedures, change the resourcing, train employees, and look for the relevant technology.

Quick tip:

Applying and monitoring the process at a small scale is recommended to measure the impact and revise it before using it.

4. Monitoring

At this stage, the entire process is executed at a broader scale, and analysts monitor and collect data to measure the process performance. With this phase, business analysts can see if the process is effective and fulfills the main goal or if it requires more improvements.

Quick tip:

It is recommended to compare the ideal model against the actual data to calculate the exact return on investment. Business analysts can generate relevant metrics or leverage process intelligence tools such as process mining or a digital twin of an organization to assess and compare the process models.

5. Optimizing

In this final step, business analysts should monitor and refine their processes based on data-driven models. Some analysts can identify process automation opportunities to avoid repetitive or manual activities. In some cases, business analysts need to update their processes due to a market or internal change.

Quick tip:

It is important to remember that sometimes processes may need to be re-design because they do not correspond to the changes happening, or they might require innovation.

6 Best Practices Of Process Mining In Automotive Industry In ’23

Digital transformation has shifted the understanding of car manufacturing towards a more software-oriented approach, resulting in disruptive changes (e.g. smart mobility and autonomous driving) in the automotive industry. For example, latest DX trends in automotive predict that autonomous cars will hold 15% market share of vehicles in 2030, leading to partnerships among digital giants and car manufacturers.

The trends and challenges in the automotive industry increase the necessity to understand and transform the operational processes. Business leaders can leverage process mining to enable and facilitate the automotive industry’s transformation projects.

In that light, this research goes in-depth into process mining use cases in the automotive industry.

Discover automation opportunities

Automotive manufacturers can transform their business by automating their processes to maintain high quality while reducing production costs.

Process mining can indicate the areas that should be automated primarily. Car manufacturers can automate their back-end tasks with process mining, such as invoicing or billing of materials (BOM). Manufacturers can also automate their inventory management to place orders, track the flow of materials in the process cycle and generate receipts using RPA bots.

Read the top 8 use cases & benefits of RPA in manufacturing for more insights.

Increase operational efficiency

The entire process of a vehicle’s production, sale, and after-sale maintenance are long and complex. It usually involves various sub-processes, different parties, and interdepartmental interactions. Therefore, vehicle manufacturers often lack an understanding of their organizational processes. As a result, they cannot discover areas for improvement and automation.

Process mining provides an end-to-end overview of the entire workflow, including steps, tasks, and sub-processes required, visualizing them as simplified diagrams. Thus, some process mining tools generate digital twins of an organization (DTO) to offer a better visual replica of the car manufacturing processes.

Essmann Automotive (Germany) utilized process mining to analyze their entire production cycle, including interactions with partners, such as tier or metal suppliers, to increase production efficiency. The business analysts found that they could improve production competitiveness by integrating suppliers earlier in the production process, which had shifted to a team-oriented partnership.

Improve after-sales services

The automotive industry offers after-sales services, including technical support, customer support, and product service. Manufacturers use after-sales services to obtain information, from the customers, about the product, and the services, to assess the performance.

Process mining can be helpful to discover all the existing tasks and operations in the after-sales services to drive insights from them.

For example, an automotive company in Italy deployed process mining to discover and analyze after-sales car maintenance. The company was able to view its operations through process mining, with all details and visualized comparison between the reference model and the data-driven model for these services. The firm predicted that it could decrease its costs by 70 % after automating the manual tasks in the car maintenance service department.

Detect inefficiencies in IT systems to maintain production

Planning and producing a car relies on IT systems and cloud apps. So it is essential to monitor and improve these systems periodically.

Process mining extracts the process data from these systems and analyzes them. Therefore, users are constantly updated on the functioning of these systems and applications. That enables the users to identify issues early before affecting the entire production cycle.

In a process mining case study, Dräxlmaier (Germany) utilized process mining to detect inefficiencies in their SAP (an Enterprise Resource Planning) and SRM (Supplier Relationship Management) systems, such as insufficient database space, risky authorization, slow performance or errors occurring while importing requests. The insights allowed the company to identify the optimum way for the order dispatch process.

Design and modify the manufacturing logistics processes

The automotive industry’s supply chain or logistics refers to managing thousands of incoming and outgoing materials, products, and services to sustain vehicle manufacturing operations. Therefore, the manufacturing supply chain must constantly collect data and generate accurate plans.

Process mining enables manufacturers to analyze the tasks and operations involved in manufacturing logistics. With process mining, manufacturers can assess the performance of their supply chain management and identify and modify the processes that suffer from deviations and errors. The manufacturers can also discover areas where a new process is required to design one based on the insights they obtained by process mining.

For instance, Automotive & Terberg (Netherlands) employed process mining to design a new chain process into their supply chain operations. The company also discovered that it had to improve its communication across departments before implementing the changes.

Improve design and production process by generating digital twin

The automotive design process includes several steps until the product is ready to launch, such as product planning, designing, concept generation, theme selection, 3D and computer model generation, and product testing, making the entire design process complicated. As the designing process, the vehicle production process is complex, with several steps to identify bottlenecks.

Process mining gathers data produced at each stage of the vehicle design and production and measures the performance of the machinery and programs used in designing or building the vehicles. As a result, producers can assess the performance level for each step in these processes and estimate the performance for each machine or the program used.

For example, Daimler in Germany used process mining to generate a digital twin of their shop floor where the car production occurs to separate sub-processes and gain user-specific information. The company created a digital replica of a manufacturing plant, spanning several locations, to develop a throughput rate analysis, which measures the number of cars produced in a certain period and explores production bottlenecks.

Process Memory Management In Linux

Process memory management is a crucial aspect of any operating system. In Linux, memory management system is designed to efficiently manage memory usage, allowing processes to access and use memory they require while preventing them from accessing memory they do not own. In this article, we will discuss process memory management in Linux in detail, covering various aspects such as memory allocation, virtual memory, memory mapping, and more.

Memory Allocation

Memory allocation is process of assigning memory to a process or program. In Linux, kernel provides two main methods for memory allocation: static and dynamic.

Static Memory Allocation

Static memory allocation is done at compile-time, where memory allocation for a program is fixed and cannot be changed during runtime. memory is allocated in program’s data section or stack segment. data section contains global variables and static variables, while stack segment contains local variables.

Dynamic Memory Allocation

Dynamic memory allocation is done during runtime, where memory allocation for a program can be dynamically adjusted based on program’s requirements. kernel provides various system calls such as malloc(), calloc(), and realloc() to dynamically allocate memory. These functions allocate memory from heap segment of program’s address space.

Virtual Memory

Virtual memory is a memory management technique that allows a program to use more memory than is physically available in system. In Linux, virtual memory is implemented using a combination of hardware and software. hardware component is Memory Management Unit (MMU), which is responsible for translating virtual memory addresses to physical memory addresses. software component is kernel’s Virtual Memory Manager (VMM), which manages allocation and deallocation of virtual memory.

Memory Mapping

Memory mapping is a technique that allows a process to access a file’s contents as if it were part of process’s memory. In Linux, memory mapping is implemented using mmap() system call. mmap() system call maps a file into a process’s virtual memory address space, allowing process to read and write to file’s contents as if it were part of its own memory. Memory mapping is commonly used in applications such as databases and multimedia players, where large files need to be accessed efficiently.

Shared Memory

Shared memory is a technique that allows multiple processes to access same portion of memory. In Linux, shared memory is implemented using shmget(), shmat(), and shmdt() system calls. shmget() system call creates a shared memory segment, shmat() attaches shared memory segment to a process’s address space, and shmdt() detaches shared memory segment from process’s address space. Shared memory is commonly used in inter-process communication, where multiple processes need to share data efficiently.

Swapping

Swapping is a technique that allows kernel to move pages of memory from RAM to a swap space on disk when system’s memory is low. In Linux, swapping is implemented using a combination of hardware and software. hardware component is disk, which is used as swap space. software component is kernel’s Swapping Manager, which manages swapping process. When system’s memory is low, Swapping Manager selects pages of memory to swap out to disk, freeing up memory for other processes.

Some additional concepts to consider include −

Kernel Memory Management

The Linux kernel itself also requires memory management, and it uses a separate set of memory management techniques to manage kernel memory. Kernel memory is used to store data structures and code required by kernel to operate. kernel uses techniques like memory mapping, page caching, and memory allocation to manage kernel memory.

Memory Protection

Memory protection is another critical aspect of memory management in Linux. Memory protection techniques prevent processes from accessing memory they are not authorized to access. MMU implements memory protection by using page tables, which map virtual memory addresses to physical memory addresses and track permissions for each memory page.

Memory Fragmentation

Memory fragmentation occurs when available memory is divided into small, non-contiguous chunks, making it difficult to allocate larger blocks of memory. Memory fragmentation can lead to performance issues and even crashes if system runs out of memory. Linux kernel uses several techniques to manage memory fragmentation, including memory compaction and defragmentation.

Memory Leak Detection

As mentioned earlier, failing to release dynamically allocated memory can result in memory leaks, where memory is not returned to system and can eventually cause program to crash due to insufficient memory. Detecting and fixing memory leaks is crucial for maintaining system stability and performance. Linux provides several tools for detecting memory leaks, including valgrind, which can detect memory leaks and other memory-related issues.

Conclusion

In conclusion, process memory management is a crucial aspect of any operating system, and Linux is no exception. Linux kernel provides a robust and efficient memory management system, allowing processes to access and use memory they require while preventing them from accessing memory they do not own. In this article, we discussed various aspects of process memory management in Linux, including memory allocation, virtual memory, memory mapping, shared memory, and swapping. Understanding these concepts is essential for any Linux developer or administrator to efficiently manage memory usage in their systems.

It Process Automation: Definition, Benefits & Outlook In 23′

Whether you are considering the incorporation of ITPA in your business framework or simply seeking a more profound understanding of this technology, this article offers the essential insights to successfully navigate the domain of automation.

What is IT Process Automation (ITPA)?

IT Process Automation (ITPA), also known as IT automation, uses software to create repeatable instructions and processes to replace or reduce human interaction with IT systems. Automation software works within the confines of those instructions, tools, and frameworks to carry out the tasks with little to no human intervention. ITPA tools, such as workload automation tools, monitor applications and IT workflows. They provide insights to align IT processes across multiple functions.

While IT process automation is often associated with larger enterprises with complex IT infrastructures, it’s also increasingly relevant for smaller businesses. ITPA is a key component in digital transformation strategies, facilitating improved data accuracy, increased productivity, and cost reduction.

Examples of ITPA in action include:

Automated system monitoring and event management: Systems and applications are continually monitored, with any anomalies or errors automatically flagged and, in some cases, resolved without human intervention.

Automated backups and disaster recovery: Routine data backup processes and disaster recovery actions are automated, ensuring business continuity.

Patch management: Regular system and application updates are automated, ensuring the business operates on the most current and secure software.

DevOps automation: This involves automating software development and delivery processes to enhance collaboration between development and operations teams and enable more efficient deployment.

IT service desk automation: Automated ticket routing, prioritization, and even resolution of standard issues, often improving customer service response times and satisfaction rates.

What are the differences between ITPA and RPA?

The choice between using ITPA systems and RPA often depends on the specific use case. ITPA is more suitable for complex, backend IT processes, while RPA is ideal for simpler, rule-based tasks that involve interacting with software in the same way a human user would. In many organizations, both types of automation are used in tandem to achieve maximum efficiency and productivity.

QualitiesITPARPA ScopeITPA focuses on automating IT tasks and workflows. It’s used primarily in IT for tasks such as automating system alerts, data backups, server maintenance, network monitoring, and chúng tôi is more generalized and is used to automate any repetitive, rule-based tasks that were previously performed by humans, often involving interaction with the user interface of a software application. It’s often used in areas such as customer service, finance, HR, and other business processes. Integration ITPA is usually deeply integrated with the IT infrastructure. It can interact directly with the system and application APIs, database connections, and other backend chúng tôi interacts with software at the user interface level, mimicking how a human user interacts with an application. It doesn’t require deep system integration, which makes it easier to implement but also somewhat limited in the depth of tasks it can perform. ComplexityITPA can handle complex processes, like disaster recovery, that often involve multiple systems, decision points, and conditional paths. RPA is generally used for more straightforward, rule-based processes. While it can handle multi-step processes, it’s less suited to tasks with many decision points or complex logic. Decision Making ITPA can include more sophisticated decision-making capabilities, often leveraging artificial intelligence and machine learning to make predictive decisions based on complex criteria and historical data.While some RPA tools are starting to incorporate AI and machine learning capabilities, traditionally, RPA has been less capable of sophisticated decision-making compared to ITPA. It typically follows predefined rules and steps.

How does IT process automation work?

IT Process Automation (ITPA) works by automating routine and repetitive tasks through the use of software scripts or specialized process automation tools. This can reduce the time taken to perform these manual tasks, eliminate the possibility of human error, and free up IT staff for more complex, strategic tasks.

The steps of the business process for implementing and operating ITPA typically involve the following:

Creating a Process Map: Once the tasks have been identified, the next step is to map out the process for each task. This involves understanding the sequence of steps that need to be followed, the decisions that must be made, and the potential exceptions that need to be handled.

Designing the Automation Workflow: With the process map in hand, the next step is to design the automation workflow using an automation tool or scripting language. This involves translating the steps of the process map into rules and instructions that the automation software can understand and follow.

Testing the Automation: Before the automation is put into production, it must be thoroughly tested to ensure it works as expected. This could involve running the automation on a test system, using test data, and verifying the results.

Deploying the Automation: Once the automation has been tested and validated, it can be deployed into the production environment. This could involve scheduling the automation to run at certain times, or configuring it to be triggered by certain events or conditions.

Monitoring and Improving the Automation: Even after the automation has been deployed, it’s important to monitor its performance and make improvements as necessary. This could involve adjusting the rules or instructions, expanding the scope of the automation, or troubleshooting any issues that arise.

Which are the leading IT process automation vendors?

Here are some of the leading vendors that provide ITPA solutions for businesses:

ActiveBatch

Redwood RunMyJob

AutomationEdge

BMC TrueSight Orchestration

Microsoft System Center

Micro Focus Operations Orchestration

OpCon

Resolve

ServiceNow Orchestration

Vmware

What are the top benefits of IT process automation? 1-Increased efficiency

Automating routine tasks can dramatically boost the speed at which they’re performed. For instance, a task like resetting passwords, which can take a service desk agent a few minutes to do manually, can be automated in seconds. This increased efficiency can result in significant time savings, especially when scaled across hundreds or thousands of similar incidents.

Automation can do the same amount of work with fewer resources, leading to substantial cost savings. For example, automating network monitoring tasks could negate network engineers’ need for 24/7 shifts, reducing labor costs.

Automating IT tasks can lead to improved response times and service levels. For instance, an automated incident response system could detect an issue (like a server going down), initiate the appropriate remedial actions, and notify the relevant personnel—all within seconds.

By automating routine tasks, IT staff can focus on more strategic, complex tasks that add more value to the organization. For instance, instead of spending time manually provisioning new servers, IT staff could work on optimizing the company’s cloud strategy or improving application performance.

2-Reduced errors

Manual processes, especially repetitive ones, are prone to human errors. Automation reduces this risk by performing tasks exactly as programmed every single time. For example, in a manual system update or asset management process, there’s a risk of missing one or more systems, leading to potential security vulnerabilities. But an automated patch management process would ensure all systems are updated consistently, minimizing the risk of error and exposure.

3-Increased compliance

Automation can enforce adherence to regulations and standards. For instance, automated compliance checks can ensure that all systems adhere to data protection policies and other regulations. A bank, for example, could use automation to ensure they remain in compliance with financial regulations by scheduling regular checks on their IT systems and producing necessary documentation for audits.

4-Scalability

Automation can make it easier to scale IT operations as the organization grows. For example, an e-commerce company experiencing a sudden surge in traffic due to a sale event could use automation to scale up its server capacity dynamically and handle the increased load, ensuring a smooth customer experience.

6-Reduced downtime

Automated systems can operate continuously and perform many tasks more quickly than a human operators, reducing downtime. For example, automated backups and disaster recovery processes can minimize downtime in the event of a system failure.

7-Enhanced security

Automation can improve the organization’s security posture by ensuring timely patching of vulnerabilities, continuous monitoring for security incidents, and rapid response to detected threats. For example, a healthcare organization might use automation to detect and respond to data breaches, reducing the risk of compromised sensitive patient data.

8-Improved decision making

Automation can improve decision-making by providing timely and accurate data. For example, an automated data collection and reporting process from various systems could provide a comprehensive view of the company’s IT infrastructure, helping management make informed decisions about upgrades, resource allocation, and other strategic issues.

What is the interest in IT process automation?

Figure 1: Interest in IT process automation software has steadily been increasing. Source: Google Trends

After 2023, we observe a slightly increasing interest in IT process automation. Google Trends predicts that interest will increase further in the second half of this year. A reason for the modest interest in the topic is that there are similar terms, such as Business Process Automation (BPA) and Robotic Process Automation (RPA) that may also be used by IT professionals looking for business process automation solutions.

What are the main IT process automation use cases?

The technology is highly flexible and can be adapted to meet the needs of a wide variety of IT workflows and automate business processes anywhere. As AI and machine learning technologies evolve, IT process automation’s potential will only increase. Here are some of the IT process automation use cases:

Managing service requests automatically

Automating routine IT tasks

Automating asset management

Automating IT-related onboarding and offboarding tasks

Managing IT security and compliance

Digitizing process and supporting digital transformation

Feel free to read our IT process automation use cases article to learn more about these complex business processes and use cases and discover more ITPA applications.

Transparency Statement

Numerous emerging tech vendors, such as Redwood and ActiveBatch, are sponsors of AiMultiple.

For more on IT automation

If you believe your IT department can benefit from an IT workflow automation tool, scroll down our data-driven list of workload automation tools to get a comprehensive look at the ecosystem and vendors.

If this article interests you, feel free to read our related articles below:

Here are some RPA-related articles if you want to learn more about automation:

To gain a more comprehensive overview of workload automation, download our whitepaper on the topic:

If you have questions about how IT process automation tools (ITPA) can help your business, we can help:

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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Sap Mm Module: What Is, Material Management Process Flow

What is SAP MM Module?

SAP MM (Material Management) Module is a SAP ERP component that helps organizations with material management, inventory management, and warehouse management in the supply chain process. It is a part of SAP ECC’s logistics functions which consists of several components and sub-components. The most prominent and widely used components are Master Data, Purchasing, and Inventory.

SAP MM Module

All of these components have their subcomponents that are essential in specific business processes, and all of the processes are executed by using transactions in SAP MM process flow.

Transaction (in SAP) means processing of certain information in order to complete business process requirement. For example, if you have purchased 10 pieces of litter buckets, you can perform particular transaction code (t-code) that will reflect those changes in SAP. Most of the business processes involve multiple SAP transactions to be accomplished and are spread over one, two or more modules.

Now in this SAP MM basics tutorial, let’s briefly talk about the various sub-modules in SAP Materials Management:

SAP MM – Master Data

Data stored in SAP R/3 is categorized as

Master Data and

Transactional Data.

Master data in SAP materials management is the core data that is used as a base for any transaction. If you are producing, transferring stock, selling, purchasing, doing a physical inventory, whatever your activity may be, it requires certain master data to be maintained.

Example of Master Data

Material master data

Customer master data

Vendor master data

Pricing/conditions master data

Warehouse management master data (storage bin master data)

here

Purchasing

Purchasing is a component of SAP MM module, and its process can be roughly depicted in below diagram.

MRP (material resource planning) creates procurement proposal and later gets converted into Purchase Requisition. Next step is assigning a source to Purchase Requisition, and release of Purchase Requisition. The PR gets converted to Purchase Order, and upon goods receipt, an invoice receipt can be done to complete the purchasing process. Additionally, payment is processed (in FI module).

Learn more about Purchasing here

Inventory Management

Inventory management is used to manage the inventory of the goods. It is based on several key processes like

Definition of movement types

Reservations

Goods issue

Goods receipt

There are a number of functions and transactions used in the Inventory management processes.

Learn more about Inventory Management here

Pricing Procedure

Pricing procedure in MM module is a way to determine prices in purchasing documents. It gives us functionality to assign different calculation types for different needs. Defining a pricing procedure can be done by creating an access sequence, and assigning it to condition types. Access sequence tells the system where to look for the condition values.

Learn more about Pricing Procedure here

Features of SAP MM

It can deal with both material management and inventory management.

Ensures no shortage of material in the supply chain process.

Helps to speed up material management and procurement activities.

It helps to accelerate productivity and reduce costs while maintaining accommodations for frequent changes in a business environment.

It helps organizations to deal with various business aspects like Procurement, Master data, Material valuation, Inventory management, Invoice verification, Material requirement planning, etc.

Summary

SAP MM meaning: SAP Material Management module is a SAP ERP component that helps organizations with material management, inventory management, and warehouse management in the supply chain process.

SAP MM full form is SAP Material Management.

Transaction in SAP means processing of certain information in order to complete business process requirement.

Master data is the core data that is used as a base for any transaction.

Purchasing is a component of SAP MM module, and its process can be roughly depicted in below diagram.

Inventory management is used to manage the inventory of the goods.

Pricing procedure for MM module in SAP is a way to determine prices in purchasing documents.

Process Management In Os: Pcb In Operating System

What is a Process?

Process is the execution of a program that performs the actions specified in that program. It can be defined as an execution unit where a program runs. The OS helps you to create, schedule, and terminates the processes which is used by CPU. A process created by the main process is called a child process.

Process operations can be easily controlled with the help of PCB(Process Control Block). You can consider it as the brain of the process, which contains all the crucial information related to processing like process id, priority, state, CPU registers, etc.

In this Operating system tutorial, you will learn:

What is Process Management?

Process management involves various tasks like creation, scheduling, termination of processes, and a dead lock. Process is a program that is under execution, which is an important part of modern-day operating systems. The OS must allocate resources that enable processes to share and exchange information. It also protects the resources of each process from other methods and allows synchronization among processes.

It is the job of OS to manage all the running processes of the system. It handles operations by performing tasks like process scheduling and such as resource allocation.

Process Architecture

Process architecture Image

Here, is an Architecture diagram of the Process

Stack: The Stack stores temporary data like function parameters, returns addresses, and local variables.

Heap Allocates memory, which may be processed during its run time.

Data: It contains the variable.

Text Section includes the current activity, which is represented by the value of the Program Counter.

Process Control Blocks

PCB stands for Process Control Block. It is a data structure that is maintained by the Operating System for every process. The PCB should be identified by an integer Process ID (PID). It helps you to store all the information required to keep track of all the running processes.

It is also accountable for storing the contents of processor registers. These are saved when the process moves from the running state and then returns back to it. The information is quickly updated in the PCB by the OS as soon as the process makes the state transition.

Process States

Process States Diagram

A process state is a condition of the process at a specific instant of time. It also defines the current position of the process.

There are mainly seven stages of a process which are:

New: The new process is created when a specific program calls from secondary memory/ hard disk to primary memory/ RAM a

Ready: In a ready state, the process should be loaded into the primary memory, which is ready for execution.

Waiting: The process is waiting for the allocation of CPU time and other resources for execution.

Executing: The process is an execution state.

Blocked: It is a time interval when a process is waiting for an event like I/O operations to complete.

Suspended: Suspended state defines the time when a process is ready for execution but has not been placed in the ready queue by OS.

Terminated: Terminated state specifies the time when a process is terminated

After completing every step, all the resources are used by a process, and memory becomes free.

Process Control Block (PCB)

Every process is represented in the operating system by a process control block, which is also called a task control block.

Here, are important components of PCB

Process Control Block (PCB)

Process state: A process can be new, ready, running, waiting, etc.

Program counter: The program counter lets you know the address of the next instruction, which should be executed for that process.

CPU registers: This component includes accumulators, index and general-purpose registers, and information of condition code.

CPU scheduling information: This component includes a process priority, pointers for scheduling queues, and various other scheduling parameters.

Accounting and business information: It includes the amount of CPU and time utilities like real time used, job or process numbers, etc.

Memory-management information: This information includes the value of the base and limit registers, the page, or segment tables. This depends on the memory system, which is used by the operating system.

I/O status information: This block includes a list of open files, the list of I/O devices that are allocated to the process, etc.

Summary:

A process is defined as the execution of a program that performs the actions specified in that program.

Process management involves various tasks like creation, scheduling, termination of processes, and a dead lock.

The important elements of Process architecture are 1)Stack 2) Heap 3) Data, and 4) Text

The PCB is a full form of Process Control Block. It is a data structure that is maintained by the Operating System for every process

A process state is a condition of the process at a specific instant of time.

Every process is represented in the operating system by a process control block, which is also called a task control block.

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