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What is SAP IBP | Step By Step Guide On SAP IBP


Introduction To The SAP IBP

The SAP IBP (integrated business planning) is a cloud-based business planning software used in supply chain management. The SAP IBP uses real-time integrated information that enables companies to respond quickly. This tool offers integrated, demand and inventory planning, unified planning for sales and operations, and an analytical dashboard for monitoring. 

The SAP IBP is known for its new user experience solution because it offers the following business integrated solutions:

  • Integrated business planning for the sales and operations.
  • Integrated business planning for the demand.
  • Integrated business planning for inventory management.
  • Integrated business planning for supply.
  • Integrated business planning for response planning.

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SAP IBP Integration Architecture  :

Here we will be explaining how does SAP IBP works, and what are the basic components that it incorporates to perform data integration.

The below SAP IBP architectural image adheres you to know the overall workflow;

IMAGE

There are two possible ways the SAP IBP can integrate the data.

  • File based integration (Open API)
  • Table integration (ABAP)

The SAP smart data integration (SDI) approach integrates the data between the SAP OBP and SAP H/4 HANA.  The SDI approach process the data through back jobs .while the non-supported real-time integration causes the planning and execution issues in the SAP H/4 HANA environment.

After the 2111 version of the SAP H/4 HANA, real-time data integration happens on both SAP on-premise and cloud environments. This version type also supports the master and transactional data integration from source to target systems and also loads the delta data into the source system.

Advantages Of SAP IBP :

We have decided to explain the SAP IBP advantages based on the solution which it offers to integrate the real-time information.

Solution designed for the S &OP Process that supports ;
  • Process modeling.
  • Scenario planning.
  • Easy-to-use user interface both excel and web-based.
  • Social collaborations.
  • Consolidated information from different organizations, especially financial data ex. Price data.
  • Real-time analysis.
Demand planning solutions :
  • S&OP with basic forecasting methods.
  • IBP for demand with advanced statistical forecasting methods including ARIMA models, and gradient boosting or machine learning.
  • IBP for demand for short-term forecasting using demand sensing logics.
Supply planning :
  • S&OP supports technical planning through the heuristic with infinite capacity planning.
  • Supports operational supply planning with response algorithms based on order models.
Monitoring and decision support and reporting :
  • Control tower as a purpose-built solution.
  • Dashboards and customer alerts.
  • case management and social collaborations.
  • Real-time analytics.
  • Performance and management analytics.
Inventory optimization :
  • Multi-stage inventory optimization.
  • Same algorithm as the on-premise solution EIS (enterprise inventory and service level optimization).
  • Fully integrated into IBP.
Available to promise :
  • IBP responses algorithms can generate allocations that are used in ATP.
  • IBP response algorithm can re-schedule and confirm sales orders (response planning).
  • IBP for response can be used additionally to ATP tools (basic ATP ECC and S/4 ), APO -gATP, and S/4 ATO.

SAP IBP Training

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  • Real-world & Project Based Learning

Key Features Of The SAP IBP :

The following are the key features of the SAP IBP;

Let me discuss them one by one:

  • Easy to use interface for planners to review and quickly modify plans.
  • S & OP powered by HANA provides real-time web-based analytics on the entire supply chain model.
  • Data integration: easy integration between any SAP or Non-SAP system and IBP HCI is included as part of the cloud license.
  • Offers real-time S&OP calculations: modeling of the supply chain in real-time during the S&OP cycle.
  • Social collaborations: SAP JAM brings together all functions of the business to solve business-critical problems and drive rapid results. 
  • Rapid simulations: the ability to run what-if scenarios in near real-time to analyze demand, supply, and changes.
  • IBP offers advanced demand sensing, analysis, and predictive forecasting.
  • IBP also provides an embedded social collaboration and MS-Excel-based planning.

Application areas of SAP IBP :

We already know that the benefits of using SAP IBP, now it’s time for us to know which are all the industrial applications adapt SAP IBP:

  • Product profitability 
  • Customer profitability 
  • Capital expenditures 
  • Manufacturing operations.
  • Supply chain operations
  • Business process (information and human-based)
  • Business policy 
  • Market demand curves
  • Competitive strategy

The below image illustrates the overall business planning of Integrated business planning ;

IMAGE

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Why technology is important in the SAP IBP?

SAP IBP offers various industry-driven business solutions across the world. Let’s see how SAP IBP changes the business to deliver high-quality outcomes.

Below are the few listed reasons which will explain why high tech companies looking for the SAP IBP solution:

The rapid pace of change : 

The factors include are;

  • A process led by senior management that evaluates and revises for demand, supply, product, and portfolio changes, strategic projects, and the resulting financial plans 
  • Realign the technical plan for all business functions in all graphics to support the company’s business goals and targets.
  • Drive a single consequences operating plan, deploying resources effectively to satisfy customers in a profitable way.
Increasingly complex networks : 
  • Monitor, analyze, and troubleshoot gaps between the business plan, current performance, and projected future performance.
  • Continuously monitor the performance of finance, product portfolio, sales, and operations against projections in real-time.
  • Identify and analyze gaps, and predict future performance.
  • Sense and predict market direction and customer needs quickly.
Global competitions :
  • React and execute a dynamic strategy.
  • Ability to make make and execute decisions in hours and days, not weeks and months.
  • Manage business risks and take advantage of market opportunities by quickly aligning operations plans.
  • Ability to develop deep customer insights, segment customers, and align service levels to maximize profitability.
  • Manage the complex supplier and partner network with real-time visibility, enabling agile operations with system-assisted decision support and management by exceptions.

Differences between the SAP IBP and traditional S&OP :

The major differences between the SAP IBP and traditional S &OP are as follows;

We are going to explain the differences according to their features:

Business objective :

  • Traditional S &OP offers supply and demand balancing.
  • Whereas IBP is not about matching demands and customer needs. They also consider several plan alternatives and choose one that best represents the business drivers, the objective is revenue and profit

Process :

  • Traditional S&OP offers processes in a Rigid and perspective way.
  • Whereas the IBP process is more rules and exception-based.

Technology :

  • Traditional S&OP provides a weak and non-integrated technology.
  • Whereas in SAP IBP, the technologies enable the process through the workflow.

Frequency :

  • The frequency of the traditional S&OP available on a monthly or quarterly basis.
  • SAP IBP is still available on a monthly basis in a lot of cases but with the ability to rapidly handle exceptional situations.

Focus :

  • Traditional S&OP provides an inward focus.
  • The SAP IBP offers a collaborative and outward focus.

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Final Words :

In this SAP IBP blog, we have tried to explain the concepts in such a way that even non-SAP programmers can also read our blogs. We adhere to all our SAP community people to stay tuned to our website to know more about SAP updated posts.

Related Articles:

SAP Supply Chain Management



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Cyber Security vs Data Science| Which to choose as a career


Last updated on
Jan 19, 2024


Saritha Reddy


Hrushikesh


  • Reviewed by


  • Hrushikesh


    (Ethical hacker, Certified Security Analyst)

Cyber Security VS Data Science – Table of Content

What is cyber security?

The cyber security industry is a fascinating field in the IT sector and apt for those who are ready to accept the challenges. The term cyber security can be defined as it is a type of IT application that designs and implements secure network solutions specially designed to act as a shield against hackers, persistence attacks, and any cyber-attacks. The cyber security market is diverse that is ranging from a cyber professional service endpoint to mobile security. It has a diverse range of applications from financial service, retail, health care, infrastructure, and transport. There is huge demand has been created for cyber security professionals, and the companies looking out to hire cyber security engineers. The companies we would like to mention are PWC, Deloitte, Telesoft technologies, VMware, Intel, and many more.

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What is Data Science?

 Data science is also known as data-driven science and is also defined as a data tool that helps to solve complex data-related problems using patterns, models, and analytics. It is also an interdisciplinary field about scientific methods, processes, and systems to extract knowledge or data insights in various forms, either in structured or unstructured formats or you can define it as data mining

Cyber Security VS Data Science:

Here we thought to list out the major differences between cyber security and data science based on professional categories.

.Most IT professionals one or some other day think about a kick start their career as a cyber security engineer or data scientist. This section clears all your doubts related to choosing the right career path.

Cyber security engineer roles and responsibilities:

 Cyber security engineers are those who involve in designing and implementing security solutions to defend against various threats, cyber-attacks, and malware attacks. They are also involved in testing and monitoring the system devices to make us assure that all the system devices are up-to-date and ready to defend against any type of attack.

Data scientist roles and responsibilities:

A data scientist is responsible for collecting, analyzing, and also interpreting a large volume of data. The data scientist role is a combination of mathematician, scientist, statistician, and computer professional.

Cyber security engineer job description:

Here is a list of cyber security engineer job descriptions:

  •  Implementing security firewalls to networking systems.
  • Determining the access authorizations.
  • Securing the information technology infrastructure.
  • Involve in monitoring the network for signs of cyberattacks.
  • Eliminate the potential threats or attempted breaches.
  • Identifying the cyber attackers.
  • Informing the organization’s workers about security policies.

Data scientist job description:

Here is a list of data scientist job descriptions:

  • Designing the data modeling processes or applications (for ex: Denodo).
  • Building the machine learning algorithms or models.
  • Developing and maintaining the databases.
  • Assessing the quality of datasets.
  • Cleansing the unstructured/ unpatterned data.
  • Preparing the data reports for the executive and project team.
  • Proposing solutions to the executive team.
  • Creating data visualizations to present information.
  • Collaborating with other teams.
  • Combining models through ensemble modeling.

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Cyber security engineer skills

To become a cyber security engineer, the following are the mandatory skillsets anyone must have:

  • Secure coding practices, ethical hacking, and threat modeling.
  • Proficiency in programming languages like python, C++, Java, Ruby, Go, and Power shells.
  • IDS/IPS penetration and vulnerability testing.
  • Firewall and intrusion detection and prevention protocols.
  • Have basic knowledge on how to use various operating systems such as Windows, Linux, and UNIX.
  • Virtualization technologies and MYSQL database server.
  • Application security and encryption technologies.

Data scientist skill:

To become a data scientist, you should have these mandatory skill sets.

  • Data scientist professionals must have strong foundation knowledge in mathematics and statistics.
  • Additionally, they should have strong programming knowledge in Python or R programming and later use them for performing various operations like data mining, manipulations, calculations, graphical display, and also running embedded systems.
  • Data scientist professionals should have additional knowledge in data statistical modeling software such as SQL database and the Hadoop platform.
  • In addition to the above-mentioned skill sets, data scientists must have strong communication, problem-solving, collaboration, and out-of-the-box thinking capabilities.

Cyber security career path:

  • Cyber security engineers must hold a bachelor’s degree in computer science, and IT system engineering.
  • They should possess a minimum of two years of work experience in cybersecurity-related roles such as incident detection, responses, and forensics.  
  • . Should have experience with the functionalities, operations, and maintenance of firewalls and various forms of endpoint system device security.
  •   Must have proficiency in languages and tools such as C++, Java, Node, Python, Go, Power shells, and Go.
  •  They should have the ability to work in fast-paced work environments, often under some work pressure.

Data scientist career path:

  • The basic education qualification required to become a data scientist is an undergraduate or bachelor’s degree in computer science. 
  • Senior-level data scientist professionals must have a master’s degree with a few years of work experience.
  • Taking some certification exams also boosts up their professional career.

 Cyber security engineer salary:

As per the indeed.com job portal, the basic salary for any cyber security engineer professional ranging from $77,000, and an experienced cyber security engineer earns more than $135,000 depending on the individual’s experience, and knowledge.

Data scientist salary:

As per the indeed.com job portal, an average salary for any data scientist ranges from $80,000 and an experienced data scientist earns more than $145,000 depending on an individual’s experience, and knowledge.

Cyber security engineer certification:

Below is the list of major cyber security engineer certifications:

  • COBIT 5 control objectives for information and related technologies.
  • COBIT 5 Professional certification.
  • CompTIA security+certification -SYO-601.
  • CISA certification and training
  • CND – certified network defender
  • CHFI – Computer hacking forensic investigator certification
  • CISSP certification

Data science certification

  • SAS Certification. 
  •  SAS Certified Big Data Professional. 
  •  SAS Certified Advanced Analytics Professional. 
  •  Senior Data Scientist. 
  • Principal Data Scientist.
  • Microsoft Certified: Azure Data Scientist Associate. 
  •   IBM Data Science Professional Certificate.

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Benefits of Cyber Security:

Once you know the definition, you will start thinking about the key benefits of this domain. This section is dedicated to fulfilling your requirements. The following are the key benefits of using Cyber security:

  • Cyber security will defend us from critical attacks.
  • It helps us to browse the safe website.
  • Internet security processes all the incoming and outgoing data on your computer.
  • Security will defend from hacks and viruses.
  • The application of cyber security used in our PC needs update every week.
  • The security developers will update their database every week once. Hence the new virus was also detected.

Benefits of Data Science:

Here also we are going to make a list of key benefits of data science:

  • Empowering management and officers to make better decisions.
  • Data scientists direct the actions based on trends which in turn help in defining goals.
  • Data scientist challenge the staff to adopt the best practices and focus on issues that matter.
  • Identifying opportunities and decision making with quantifiable, data-driven evidence.
  • Improving fraud detections in financial institutions and also identifying the best delivery routes.

Key features of Cybers Security:

Below are the key features of cyber security:

  • Identify management unique IDs for personal and products for authentication.
  • Access control specifies the role and other constraints for authorization.
  • Agree on cryptographic details for securing network protocols.
  • Validate the source and integrity of the software and framework.
  • Validate the integrity of the process data. 
  • Validate the integrity of the OT settings.

Key features of Data Science:

Below are the key features of data science:

  • Responsive data construct and flexible to manage.
  • Easily trainable and parallel neural networking.
  • Opens source and feature columns.
  • Availability of statistical distribution.
  • Layered components and feature columns.

frequently asked Cyber security Interview questions and Answers !!

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Final Words:

In this Cyber security VS data science post, we did not concentrate not only on explaining basic things but also tried to explain the professional differences too. Both data science and cyber security are the hottest domains, to become a master or expertise in these technologies is a dream of many people. The main purpose to develop these kinds of articles are to help our readers to enhance their skill sets with appropriate domains and also choose the right career. We are hoping that you people enjoy reading our blogs. Stay tuned for more updates.

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