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Introduction to the Oracle SCM Cloud: Global Order Promising 2020 Implementation Essentials (1z0-1079-20) Exam

The Oracle SCM Cloud Global Order Promising 2020 Implementation Essentials certification, designated by the code 1z0-1079-20 Exam, represents a significant credential for professionals working within the supply chain management domain. This certification is specifically designed to validate the skills and knowledge required to effectively implement and manage Oracle's Global Order Promising (GOP) solution. It targets individuals such as implementation consultants, business analysts, and supply chain planners who are responsible for configuring and utilizing the GOP module to provide accurate and efficient order fulfillment promises. Achieving this certification demonstrates a deep understanding of the application's architecture, configuration, and business processes.

Passing the 1z0-1079-20 Exam signifies that a professional can translate complex business requirements into a functional and optimized GOP solution. The exam covers a broad spectrum of topics, ranging from the initial setup and data collection to the configuration of sophisticated sourcing and promising rules. It confirms an individual's ability to navigate the Oracle SCM Cloud environment, manage supply and demand, and leverage the powerful features of GOP to improve customer satisfaction and operational efficiency. For organizations, having certified professionals on their team ensures that their investment in Oracle SCM Cloud is maximized, leading to more reliable delivery commitments and a more resilient supply chain.

The value of this certification extends beyond individual recognition. It serves as a benchmark for competency, providing a clear indicator of an individual's expertise in a highly specialized area of supply chain technology. As businesses increasingly rely on sophisticated systems to manage global operations, the demand for skilled implementers and administrators of these systems grows. The 1z0-1079-20 Exam curriculum is carefully curated to align with the real-world challenges faced during an implementation project, making it a highly relevant and practical qualification. It equips candidates with the foundational knowledge needed to build a successful career in Oracle SCM Cloud consulting and management.

Preparing for this exam requires a structured approach that combines theoretical knowledge with hands-on practice. Candidates should aim to understand not just the "how" of configuration but also the "why" behind different setup options. This deeper understanding is crucial for tackling the scenario-based questions that are a hallmark of Oracle certification exams. Throughout this series, we will break down the key concepts and components covered in the 1z0-1079-20 Exam, providing a comprehensive guide to help you master the material and achieve certification success. This journey begins with understanding the core fundamentals of the exam and the GOP module itself.

Understanding the Exam Structure and Format

A crucial first step in preparing for the 1z0-1079-20 Exam is to develop a clear understanding of its structure and format. This knowledge allows candidates to manage their time effectively during the test and approach the questions with a well-defined strategy. The exam typically consists of a series of multiple-choice questions designed to assess both theoretical knowledge and practical application skills. Candidates are allotted a specific amount of time to complete all the questions, and it is essential to be aware of this duration to pace oneself appropriately throughout the examination. Knowing the total number of questions helps in calculating the average time that can be spent on each one.

The passing score for the 1z0-1079-20 Exam is a predetermined percentage that candidates must achieve to earn the certification. This threshold is set by Oracle and reflects the level of competency expected of a certified implementation specialist. It is important to aim for a score well above this minimum to account for any challenging or unexpected questions. The questions are carefully crafted to cover all the major topic areas outlined in the official exam guide. These topics include Global Order Promising architecture, data collection, sourcing rules, assignment sets, and various promising methodologies. A thorough review of these topics is non-negotiable for success.

The question format is predominantly multiple-choice, but this can include several variations. Some questions may require selecting a single best answer from a list of options, while others might ask for multiple correct answers. It is vital to read each question carefully to understand exactly what is being asked. Scenario-based questions are also common, where a brief business case is presented, and the candidate must choose the most appropriate configuration or solution. These questions test the ability to apply knowledge to solve real-world problems, which is a key skill for any implementation consultant preparing for the 1z0-1079-20 Exam.

To prepare for this format, candidates should engage with practice exams and sample questions. This not only helps in reinforcing knowledge but also in becoming comfortable with the style and pressure of the actual exam environment. Analyzing the results of these practice tests can highlight areas of weakness that require further study. By familiarizing yourself with the exam's structure, timing, and question types, you can eliminate surprises on test day and focus entirely on demonstrating your expertise in Oracle Global Order Promising. This strategic preparation is a key differentiator between passing and failing.

Core Concepts of Global Order Promising (GOP)

Before delving into the specific configurations within Oracle Cloud, it is essential to have a solid grasp of the fundamental concepts of Global Order Promising (GOP). At its core, GOP is a sophisticated business process, enabled by technology, that provides a reliable delivery promise to a customer at the time of order placement. It answers the critical question: "When can you deliver this product to me?" To do this, the system performs a real-time check across the entire supply chain network to determine the earliest possible fulfillment date, considering material availability and capacity constraints.

A central concept within GOP is Available-to-Promise (ATP). ATP represents the quantity of an item that is available to be promised to a customer on a specific date. The calculation goes beyond simply looking at the on-hand inventory. An effective ATP calculation considers current on-hand stock, scheduled incoming supplies (like purchase orders or manufacturing work orders), and existing demand commitments (like other sales orders). By netting these supplies and demands over a time horizon, the system can accurately project future availability. Understanding how to configure the rules that govern this calculation is a major part of the 1z0-1079-20 Exam.

Expanding on ATP is the concept of Capable-to-Promise (CTP). While ATP focuses on the availability of finished goods, CTP goes a step further by checking the availability of key components and resources required to build a product. This is particularly relevant for assemble-to-order or manufacture-to-order environments. If a finished product is not in stock, a CTP process can determine the earliest date it can be manufactured and made available by examining the bill of materials and the capacity of critical work centers. This provides a more comprehensive and realistic promise date.

Another key concept is Profitable-to-Promise (PTP), which adds a financial dimension to the promising decision. Instead of simply finding the fastest fulfillment option, PTP analyzes the costs and revenues associated with different sourcing options to identify the one that maximizes profitability while still meeting customer requirements. This could involve sourcing from a slightly more distant warehouse if the overall cost, including transportation, is lower. The 1z0-1079-20 Exam requires candidates to understand these different promising methods and know when and how to apply them within the Oracle SCM Cloud solution to meet diverse business objectives.

Navigating the SCM Cloud Environment

Proficiency in the 1z0-1079-20 Exam requires not only theoretical knowledge but also practical familiarity with the Oracle SCM Cloud user interface. A candidate must be comfortable navigating through the various screens, menus, and work areas to perform the necessary configurations and analyses related to Global Order Promising. The user experience is built around a modern, intuitive interface that uses a combination of a navigator menu, task lists, and interactive dashboards. The starting point for most users is the home page, which displays a series of icons, often called infolets, that provide quick summaries and links to key functional areas.

The primary navigation tool is the Navigator, accessible from the top-left corner of the screen. This menu provides a hierarchical list of all the modules and work areas a user has access to. For GOP configuration, a key destination is the 'Setup and Maintenance' work area. This area is the central hub for all implementation tasks, allowing consultants to manage setup data, define configurations, and deploy changes. Within Setup and Maintenance, tasks are organized by functional areas, such as 'Order Management' or 'Supply Chain Planning', making it easier to locate the specific setup pages for GOP.

Within the Supply Chain Planning functional area, you will find the specific tasks related to Global Order Promising. These include managing sourcing rules, ATP rules, and assignment sets. Each of these tasks opens a dedicated page where you can create, view, and edit the respective configurations. Familiarity with the layout of these pages, including the placement of buttons for creating new records, searching for existing ones, and saving changes, is essential. The ability to efficiently move between these different setup screens is a practical skill that will save valuable time during an implementation project and is implicitly tested in the 1z0-1079-20 Exam.

Beyond setup, it's also important to know how to access the analytical and transactional parts of the application. For instance, the 'Order Management' work area is where users would typically enter sales orders and see the results of a GOP check. The 'Supply Chain Planning' work area provides access to tools for reviewing promising results and managing the planning data. Being able to trace the flow from setup to transaction and analysis demonstrates a holistic understanding of the application, which is a hallmark of a proficient implementation specialist. Hands-on practice is the best way to build this navigational muscle memory.

Initial Setup and Configuration for GOP

The journey to a functional Global Order Promising system begins with a series of foundational setups. These initial configurations create the basic structure upon which all the detailed promising logic will be built. One of the very first steps in the Setup and Maintenance work area is to ensure that the necessary offerings for Supply Chain Planning and Order Management are enabled. This makes the relevant setup tasks and features visible and available for configuration. Without this, you will not be able to proceed with the more specific GOP settings required for the 1z0-1079-20 Exam.

A critical piece of the initial setup involves defining the organizational structure and the item master. GOP operates within the context of inventory organizations, which represent physical locations like warehouses or distribution centers. These organizations must be defined, along with the items that will be planned and promised. Item attributes play a significant role in how GOP functions. For example, specific attributes on the item master determine whether an item is subject to ATP checks and how it should be sourced. Ensuring that items are correctly set up is a prerequisite for any successful GOP implementation.

Defining calendars is another fundamental step. Calendars dictate the working and non-working days for various activities. For example, a shipping calendar for a warehouse defines which days it can dispatch goods, while a receiving calendar for a customer site defines when they can accept deliveries. These calendars are crucial inputs for the GOP engine, as they ensure that the calculated promise dates are realistic and account for holidays and weekends. The system uses this information to accurately calculate transit times and schedule shipping and delivery events only on valid working days, a concept you must understand for the 1z0-1079-20 Exam.

Finally, you must configure the basic data collection parameters. The GOP engine relies on a snapshot of supply, demand, and inventory data to make its calculations. The initial setup involves defining the source systems (e.g., Oracle Fusion or a legacy system) from which this data will be collected. You will also need to set up the collection schedule and scope, determining how often the data is refreshed. An accurate and timely data set is the lifeblood of Global Order Promising, and establishing a robust data collection process from the outset is a key factor for implementation success. These foundational steps pave the way for more advanced rule configurations.

Data Collection and Architecture in SCM Cloud

The accuracy and effectiveness of Oracle Global Order Promising are entirely dependent on the quality and timeliness of the data it uses. The 1z0-1079-20 Exam places significant emphasis on understanding the data collection process and the underlying architecture. The GOP engine does not interact directly with the live transactional tables. Instead, it operates on a dedicated data repository that is populated through a process called 'data collections'. This architecture is designed to ensure high performance for promising requests without impacting the performance of transactional systems like Order Management or Inventory Management.

The data collection process involves pulling relevant information from various source systems into the GOP planning repository. This data includes on-hand inventory balances, open sales orders (demand), purchase orders, transfer orders, and work orders (supply). It also collects master data such as item information, sourcing rules, calendars, and transit times. Candidates preparing for the 1z0-1079-20 Exam must understand the different types of data that need to be collected and their purpose in the promising calculation. The process can be run for a complete refresh or on a targeted, net-change basis to improve efficiency.

Oracle SCM Cloud provides a comprehensive framework for managing this process. From the 'Plan Inputs' work area, administrators can launch and monitor collection jobs. It is crucial to define the collection parameters correctly, specifying the source instance and the entities to be collected. For example, you might choose to collect supply data more frequently than master data, which changes less often. The success of these collection jobs must be monitored regularly, as any failures can lead to the GOP engine working with stale or incomplete data, resulting in inaccurate promise dates for customers.

Understanding the distinction between the transactional database (OLTP) and the planning repository is key. The planning repository is optimized for the complex queries and calculations performed by the GOP engine. This separation allows the engine to analyze vast amounts of data across the entire supply chain network very quickly. When a user requests an availability check for an item, the request is sent to the GOP engine, which queries its own data repository to find the best possible fulfillment option and returns a date. This architectural design is fundamental to how Oracle GOP achieves its high performance and scalability.

Understanding Sourcing Rules and Assignment Sets

Sourcing rules are the cornerstone of the Global Order Promising configuration, as they define how demand for an item can be fulfilled. For any given item, a sourcing rule specifies the valid sources of supply and their priority. The 1z0-1079-20 Exam requires a deep understanding of how to create and manage these rules. A source can be an inventory organization where the item is stocked (a 'Transfer From' source), an external supplier who provides the item (a 'Buy From' source), or the organization where the item is manufactured (a 'Make At' source).

When creating a sourcing rule, you define one or more sources and assign a rank to each. This rank determines the order in which the GOP engine will evaluate the sources. For example, the system might first check for on-hand inventory at the primary local warehouse (Rank 1). If the item is not available there, it will then check a regional distribution center (Rank 2), and finally, it might check if the item can be purchased from a preferred supplier (Rank 3). This hierarchical approach allows businesses to model their preferred fulfillment strategies and cost priorities within the system.

Sourcing rules can also specify a split percentage for supply. This means that instead of sourcing 100% of the demand from a single source, the demand can be split across multiple sources based on predefined percentages. This feature is useful for dual-sourcing strategies or for load balancing across different facilities. The flexibility of these rules allows for the modeling of very complex supply chain networks and business policies. A thorough understanding of how to configure ranks and splits is essential for anyone preparing for the 1z0-1079-20 Exam.

Once sourcing rules are created, they must be activated. This is done through assignment sets. An assignment set links a specific sourcing rule to a particular item, a category of items, an organization, or a global level. This assignment hierarchy is critical. For example, you could have a general sourcing rule for a category of items but a more specific rule for a single high-demand item within that category. The system will always use the most specific assignment it can find. Mastering the creation of sourcing rules and their strategic application through assignment sets is a fundamental skill for a GOP implementation specialist.

Introduction to Available-to-Promise (ATP) Rules

While sourcing rules tell the GOP engine where to look for supply, Available-to-Promise (ATP) rules tell it what to consider when it gets there. ATP rules are a critical configuration component that defines the supply and demand components, time horizons, and promising logic used in the availability calculation. A deep understanding of ATP rules is absolutely necessary to pass the 1z0-1079-20 Exam. Each rule is a collection of settings that dictates how the promising calculation is performed for the items to which it is assigned.

A key component of an ATP rule is the promising mode. There are several modes available, each serving a different business purpose. 'Supply Chain Availability Search' is the most common mode, where the system performs a detailed search for available supply across the sources defined in the sourcing rule. 'Lead Time Based' promising, on the other hand, does not check for availability but simply calculates a date based on predefined lead times. 'Infinite Availability' assumes the item is always available and promises it immediately. Choosing the correct promising mode is a fundamental decision in the setup process.

ATP rules also control which specific types of supply and demand are included in the calculation. For example, an administrator can configure a rule to include purchase orders and work orders as incoming supply but to exclude unapproved or past-due purchase orders. Similarly, on the demand side, the rule specifies whether to consider sales orders, transfer orders, or even forecasts as commitments against supply. The rule also defines a time fence, which is a horizon within which to search for supply and demand. These granular controls allow the promising logic to be tailored precisely to a company's business processes.

Furthermore, ATP rules manage settings related to latency and offsets. For example, you can build in a picking and packing time buffer, known as processing lead time, to ensure that the promise date accounts for internal warehouse activities. The ability to correctly configure these elements within an ATP rule demonstrates a sophisticated understanding of the system. Like sourcing rules, ATP rules are assigned to items or organizations to take effect. A well-configured ATP rule ensures that the promise dates generated by the system are not just fast, but also realistic and achievable, building customer trust.

Key Terminology for the 1z0-1079-20 Exam

Success in the 1z0-1079-20 Exam hinges on a fluent understanding of its specific terminology. A strong vocabulary is essential for interpreting questions correctly and selecting the right answers. One of the most basic terms is 'Sourcing Hierarchy', which refers to the logic the system uses to find the correct sourcing rule to apply. It typically checks for an assignment at the item-organization level first, then the item level, then the category level, and finally the global level. Understanding this hierarchy is key to troubleshooting why a particular sourcing rule is or is not being used.

'Promising Mode' is another critical term. As discussed, this setting within an ATP rule dictates the fundamental logic for calculating a promise date. Candidates must know the difference between 'Supply Chain Availability Search', 'Lead Time Based', and 'Infinite Availability'. Each mode serves a distinct business need, and exam questions will often present a scenario requiring you to choose the appropriate mode. For example, a low-cost, non-critical item might use lead-time-based promising, while a critical, high-value product would require a full availability search.

The terms 'Time Fence' and 'Lead Time' are also frequently encountered. A 'Time Fence' is a specified number of days into the future within which the GOP engine will consider existing supply and demand. Any requirements beyond this fence might be treated differently. 'Lead Time' refers to the duration required to perform an action. This can include manufacturing lead time, transit lead time between locations, or supplier lead time for purchased items. Accurate lead time data is crucial for the system to calculate realistic delivery dates, especially when current on-hand inventory is not available.

Other important terms include 'Assignment Set', which is the mechanism for linking sourcing and ATP rules to items and organizations, and 'Data Collection', which is the process of gathering transactional and master data for the GOP engine. You should also be familiar with 'Backlog Management', the process of monitoring and re-promising previously scheduled orders, and 'Capable-to-Promise (CTP)', the process that checks for component and resource availability for manufactured items. Building a glossary of these key terms and reviewing them regularly is an effective study technique for the 1z0-1079-20 Exam.

Developing a Study Plan for Success

Passing the 1z0-1079-20 Exam requires more than just reading documentation; it demands a structured and disciplined study plan. A well-thought-out plan will ensure comprehensive coverage of all exam topics and help you manage your preparation time effectively. The first step is to download the official exam objectives from the Oracle certification website. This document is the blueprint for the exam, detailing the specific topics and sub-topics that will be covered. Use this as a checklist to track your progress and identify areas where you need to focus more attention.

Allocate dedicated time blocks in your schedule for studying. Consistency is more effective than cramming. Aim for a certain number of hours each week and stick to it. Your study sessions should be a mix of theoretical learning and hands-on practice. Spend time reading the official Oracle SCM Cloud documentation and implementation guides for Global Order Promising. These resources provide the most accurate and detailed information about the product's features and configurations. Augment this with training materials or courses that can offer a more guided learning experience.

Hands-on experience is arguably the most critical component of your preparation. If you have access to a test or development environment, use it extensively. Try to replicate the configurations described in the study materials. Create your own sourcing rules, ATP rules, and assignment sets. Run data collections and analyze the results. Enter test orders to see how the system responds to different scenarios. This practical application will solidify your understanding of the concepts in a way that reading alone cannot. It will be invaluable for answering the scenario-based questions in the 1z0-1079-20 Exam.

Finally, incorporate practice exams into the later stages of your study plan. These tests help you assess your knowledge, get used to the pressure of a timed exam, and familiarize yourself with the question format. After each practice test, carefully review every question, especially the ones you got wrong. Understand the reasoning behind the correct answer. This process of active recall and analysis is one of the most effective ways to retain information and build the confidence needed to succeed on exam day. A methodical approach, combining theory, practice, and assessment, is the surest path to certification.

A Deeper Dive into Advanced Sourcing Rules

Building upon the foundational understanding of sourcing rules from Part 1, we now explore the more advanced configurations that enable sophisticated and flexible fulfillment strategies. The 1z0-1079-20 Exam expects candidates to master these nuances. Advanced sourcing rules allow an organization to precisely model its supply chain network and business priorities. One of the most powerful features is the ability to define different types of sources beyond a simple warehouse transfer. These include 'Make At', 'Buy From', and 'Transfer From', each with its own set of parameters and considerations that dictate how the Global Order Promising engine behaves when that source is evaluated.

For a 'Make At' source, the rule points to an organization where the item can be manufactured. When GOP considers this option, it can trigger a Capable-to-Promise check, which evaluates the availability of necessary components and resources to determine a feasible production completion date. For a 'Buy From' source, the rule specifies an external supplier. The system will then look at predefined supplier lead times and consider open purchase orders as future sources of supply. This is crucial for incorporating the procurement cycle into the overall customer promise. A 'Transfer From' source, the most common type, simply indicates another internal inventory organization.

The ranking system within a sourcing rule is fundamental to its logic. The GOP engine evaluates sources in the strict order of their assigned rank, starting with rank 1. It will only proceed to evaluate rank 2 if a fulfillment solution cannot be found at rank 1. This allows businesses to define a clear preference hierarchy. For example, a company might prefer to fulfill an order from a local warehouse first (Rank 1) due to lower shipping costs and faster delivery times. If inventory is unavailable locally, it might then look to a regional distribution center (Rank 2) as the next best option.

Another advanced feature is the use of split percentages. Instead of sourcing 100% of a demand from a single source, a sourcing rule can be configured to allocate the quantity across multiple sources. For example, a rule could specify that for a particular item, 60% of the demand should be sourced from Warehouse A and 40% from Warehouse B. This capability is highly valuable for risk mitigation strategies, such as dual sourcing, or for balancing inventory levels across the network. Mastering the combination of source types, ranks, and splits is essential for passing the 1z0-1079-20 Exam.

Configuring and Managing Assignment Sets

Sourcing rules and ATP rules, no matter how well-designed, are inactive until they are assigned. The mechanism for this is the assignment set. An assignment set is a collection of assignment definitions that link rules to specific levels within the business hierarchy. A comprehensive understanding of how to build and manage these sets is a core competency tested in the 1z0-1079-20 Exam. The power of this model lies in its flexibility; you can have a single, company-wide assignment set or multiple sets for different regions, business units, or even for what-if analysis.

The most critical concept to grasp is the assignment hierarchy. The GOP engine searches for an applicable rule assignment in a specific, descending order of precedence. It first looks for a rule assigned to a specific item in a specific organization. If no assignment is found at this most granular level, it then looks for a rule assigned to the item across all organizations. Failing that, it checks for an assignment at the item category level for the specific organization, then the category level across all organizations. Finally, if no other assignment is found, it will use the rule assigned at the global level.

This hierarchy allows for a powerful mix of general rules and specific exceptions. For example, a company could create a global sourcing rule that dictates all items should be sourced from a central distribution center. However, for a particular bulky item, a more specific item-level assignment could be created to source it from a regional warehouse to save on transport costs. For a newly launched product line, a category-level assignment could be used to source all items in that category from a specific manufacturing plant. This ability to override general rules with specific ones is a key feature of the system.

Managing assignment sets also involves understanding their lifecycle. An assignment set is defined, populated with various rule assignments, and then associated with the GOP profile options. Only the assignment set specified in the profile options is considered active by the promising engine during live transactions. This allows administrators to build and test a new set of sourcing logic in a new assignment set without impacting the production environment. Once the new logic is validated, they can simply update the profile option to activate the new set, providing a controlled way to manage changes to the core promising logic.

Mastering Available-to-Promise (ATP) Rule Components

An ATP rule is the brain behind the availability calculation, and the 1z0-1079-20 Exam requires a component-level understanding of its setup. The promising mode is the single most important setting, as it defines the fundamental method of calculation. 'Supply Chain Availability Search' instructs the engine to perform a comprehensive check for available quantities across the sources defined in the sourcing rules. This is the most thorough but also the most processing-intensive mode. It is typically used for items where an accurate, inventory-backed promise is critical.

In contrast, the 'Lead Time Based' promising mode does not check inventory at all. Instead, it calculates a promise date based on predefined lead times. This could be a fixed number of days, or it could be the cumulative manufacturing or supplier lead time. This mode is useful for items that are always built or procured to order, or for low-value items where the cost of a detailed availability check is not justified. The 'Infinite Availability' mode is the simplest; it assumes the item is always in stock and provides an immediate promise date. This is often used for non-physical items like services or warranties.

Beyond the mode, the ATP rule's power lies in its detailed control over the supply and demand picture. Within the rule definition, a series of checkboxes allows an administrator to specify precisely which types of supply to include. Should the calculation consider on-hand inventory, open purchase orders, in-transit shipments, or work orders? You can also decide whether to include supply that is past its expected due date. This granularity ensures that the availability picture reflects the company's business policies regarding the reliability of different supply sources.

Similarly, the rule defines which types of demand should be treated as a commitment against supply. This always includes sales orders, but can also be configured to include internal transfer orders or even sales forecasts. By including forecasts as a source of demand, a company can ensure that it reserves some of its expected supply for future anticipated orders, rather than consuming it all on a first-come, first-served basis. The ATP rule also defines time fences, which limit how far into the past or future the engine looks for these supply and demand pictures, a key detail for the 1z0-1079-20 Exam.

Capable-to-Promise (CTP) and Manufacturing Integration

Capable-to-Promise (CTP) extends the promising process beyond simply checking for finished goods inventory. It is a critical function for businesses operating in a make-to-order or assemble-to-order environment. The 1z0-1079-20 Exam requires candidates to differentiate CTP from ATP and understand its integration with the manufacturing modules. While ATP asks, "Do we have it?", CTP asks, "If we don't have it, when is the earliest we can make it?" This involves a more complex check that delves into the production process itself.

When a CTP check is triggered for an item, the GOP engine communicates with the Oracle Manufacturing Cloud. It looks up the item's primary bill of material (BOM) to identify all the components required for production. It then performs an availability check for each of those critical components. Furthermore, it examines the item's routing to identify the manufacturing steps and the resources (e.g., machines, labor) needed. It then checks the capacity and calendar availability of those resources. CTP provides a holistic view of the entire manufacturing process to derive a realistic completion date.

The configuration for CTP is more involved than for a standard ATP setup. It requires that the item be correctly set up in both the planning and manufacturing modules. The item attributes must specify that it is a manufactured item. A valid and accurate BOM and routing must exist and be associated with the item. The components themselves must have their own ATP rules and sourcing rules defined. Any failure in this underlying manufacturing master data will result in an incorrect or failed CTP calculation, making data integrity absolutely paramount.

The process flow for a CTP order is seamless from a user's perspective but complex behind the scenes. When an order is placed, GOP determines that no finished good is available via ATP. It then, based on the sourcing rule, initiates the CTP check. It calculates a date based on component availability, resource capacity, and manufacturing lead times. Once the customer accepts this date, the system can automatically create a work order in the manufacturing system, linking it directly to the sales order demand. This tight integration ensures that the production plan is directly driven by confirmed customer orders.

Profit-Based Promising Concepts

Profit-Based Promising, sometimes referred to as Profitable-to-Promise (PTP), represents one of the most advanced capabilities within the Global Order Promising suite. While traditional promising focuses on meeting a delivery date, PTP introduces a financial dimension to the decision-making process. It seeks to find a fulfillment option that is not only feasible but also the most profitable for the organization. Understanding this concept is important for the 1z0-1079-20 Exam as it showcases a deeper, strategic understanding of the application's capabilities. It moves the conversation from pure logistics to financial optimization.

The core idea of PTP is to evaluate the total landed cost and the potential revenue associated with each possible fulfillment option. When a sales order is placed, the PTP engine analyzes all valid sourcing options. For each option, it calculates the associated costs, which can include the material cost, production cost, transportation cost from the source to the customer, and any applicable overheads or duties. It compares this total cost against the revenue from the sales order line to determine the profit margin for each option. The system then recommends the option that yields the highest profit.

The setup for PTP is considerably more complex than for standard GOP. It requires the organization to define and maintain a wealth of cost and revenue data within the SCM Cloud. This includes defining detailed transportation costs between all potential shipping and receiving locations, associating production costs with manufacturing processes, and ensuring that sales order prices accurately reflect revenue. The accuracy of the PTP engine's recommendations is directly proportional to the quality and completeness of this underlying financial data. Without it, the engine cannot perform a meaningful analysis.

In practice, PTP can lead to counter-intuitive but highly effective decisions. For example, it might recommend sourcing a product from a warehouse that is further away from the customer if the transportation costs are offset by lower inventory holding costs or a lower material cost at that location. It can also be used to make decisions during periods of constrained supply, prioritizing the fulfillment of orders from more profitable customers or channels. PTP transforms the order promising function from a purely operational task into a strategic lever for improving the company's bottom line.

Order Promising with External Suppliers (Buy Orders)

An effective Global Order Promising system must have visibility into all potential sources of supply, including those from external partners. Integrating the procurement process and considering supply from suppliers is a key part of a holistic fulfillment strategy and a topic covered in the 1z0-1079-20 Exam. When an item is designated as a purchased item, GOP can be configured to promise against future supply expected on approved purchase orders. This allows a company to sell products that are not yet physically in their warehouse but are confirmed to be in transit from a supplier.

The configuration begins with a 'Buy From' sourcing rule, which specifies the approved supplier(s) for an item. Crucially, this setup also involves defining the supplier lead time. This lead time represents the total time from when a purchase order is placed with the supplier to when the goods are received at the company's warehouse. This data is critical, as it allows GOP to calculate a realistic availability date if it needs to source from a new purchase order. The system must also have access to accurate supplier calendars to account for their non-working days.

When an availability check is performed, and the sourcing rule directs the engine to a 'Buy From' source, the system first checks for any open purchase orders for that item. It treats the quantity on an approved PO, scheduled for a specific due date, as a firm source of future supply. If a sales order can be fulfilled by one of these incoming receipts, GOP will promise a date based on that receipt date plus any internal processing time. This provides accurate promise dates based on confirmed inbound supply.

A common and important scenario involving external suppliers is drop shipping. In a drop ship model, the company takes an order from a customer but arranges for the supplier to ship the product directly to the end customer. This is configured in GOP using a drop ship sourcing rule. When this rule is invoked, the promise date is calculated based on the supplier's lead time to deliver to the customer's location. The system then automates the creation of a purchase order with the customer's address as the ship-to location, streamlining the entire end-to-end process.

Understanding and Configuring Transit Times

An accurate customer promise date is not just about when an item is available to ship; it is equally about how long it takes to travel from the shipping point to the destination. Transit times are a critical piece of master data in any GOP implementation, and the 1z0-1079-20 Exam requires an understanding of how they are defined and used. The GOP engine adds the defined transit time to the available-to-ship date to calculate the final available-to-deliver date for the customer. Inaccurate transit times are one of the most common causes of incorrect promise dates.

Transit times are defined within the shipping network configuration in Oracle SCM Cloud. They are typically defined between two specific locations, such as from a shipping warehouse's location to a customer's ship-to location. The system allows for a high degree of specificity. You can define a transit time for a particular shipping method or carrier, recognizing that shipping by air is much faster than shipping by ground. For example, the transit time from a warehouse in California to a customer in New York could be set to 2 days for an express air method and 5 days for a standard ground method.

The system uses a location-based hierarchy to find the appropriate transit time. It will first look for a transit time defined for the specific origin and destination location pair. If it doesn't find one, it may look for a more general transit time defined between the origin and destination regions or zones. This allows for efficient data maintenance, as you can set up general transit times at a zone level and only create specific ones for exceptions. This structured approach to defining transportation lanes and times is fundamental to building a scalable and maintainable GOP solution.

The calculation is further refined by the use of calendars. The GOP engine considers the shipping calendar of the source warehouse, the calendar of the selected carrier, and the receiving calendar of the destination. The transit time is calculated in terms of working days on the carrier's calendar. If the calculated delivery date falls on a non-working day for the customer (e.g., a weekend), the system will automatically push the delivery date to the next valid working day. This integration of transit times with multiple calendars ensures that the final date presented to the customer is realistic and achievable.

The Role of Calendars in Order Promising

Calendars are a deceptively simple yet profoundly important component of the Global Order Promising setup. They provide the context of time and working days for every logistical activity, and without them, any date calculation would be unreliable. The 1z0-1079-20 Exam will expect candidates to know the different types of calendars and how they interact to constrain the final promise date. An error in calendar setup can lead to promising deliveries on public holidays or assuming a warehouse can ship goods on a Sunday, leading to broken promises and customer dissatisfaction.

There are three primary types of calendars that directly impact the GOP calculation. The first is the Shipping Calendar, which is assigned to an inventory organization. This calendar defines the valid days and hours during which the warehouse or distribution center can perform shipping activities like picking, packing, and dispatching goods. If an item is available on a Friday but the warehouse's shipping calendar shows it is closed on weekends, the earliest the system can schedule the shipment is the following Monday.

The second type is the Receiving Calendar. This calendar can be assigned to a customer's ship-to location and defines the days and times they are able to accept deliveries. Many businesses, for example, do not have receiving staff available on weekends. Even if a shipment could physically arrive on a Saturday, the GOP engine will consult the receiving calendar and, if necessary, postpone the promised delivery date to the next valid receiving day, which would be the following Monday. This prevents failed delivery attempts and improves logistical efficiency.

The third and final piece of the puzzle is the Carrier Calendar. This is associated with a specific shipping method or carrier and defines their operating days. The transit time between two locations is measured in the working days of the carrier. For a transit time of '3 days', the system will count three days on which the carrier is actually moving goods. If this period includes a public holiday on which the carrier does not operate, the total transit duration will be extended. The GOP engine intelligently layers these three calendars—Shipping, Carrier, and Receiving—to calculate a final delivery date that is viable at every step of the journey.

Data Collection Strategies for Real-Time Promising

The Global Order Promising engine operates on its own dedicated data repository, which is a snapshot of the transactional and master data from source systems. The process of populating this repository is called data collection, and choosing the right strategy is a critical decision that impacts both data accuracy and system performance. The 1z0-1079-20 Exam may test knowledge of these strategies and their appropriate use cases. The goal is to keep the GOP data as fresh as possible without placing an excessive load on the source systems or the planning server.

There are three primary methods for data collection. The first is a 'Complete Refresh'. This method clears all existing data from the planning repository for the selected entities and reloads everything from the source system. While this ensures that the data is perfectly in sync, it is also the most time-consuming and resource-intensive process. A complete refresh is typically run infrequently, such as during the initial implementation or if there is a suspicion of major data corruption. It is not a viable strategy for keeping the system updated on a frequent basis.

The second method is 'Net Change Refresh'. This is a more efficient approach where the collection process only gathers data that has been created or changed since the last collection run. This significantly reduces the volume of data that needs to be processed and is the most common method for periodic updates, such as nightly or hourly data syncs. It provides a good balance between data freshness and system performance. Most scheduled collection jobs are configured to run in this net change mode to keep the GOP engine supplied with up-to-date information on inventory, supply, and demand.

The third and most advanced method is 'Targeted Refresh'. This process is often triggered automatically by events in the transactional system. For example, the creation of a large, high-priority sales order could trigger a targeted collection of just that document. This allows the GOP engine to be aware of critical new demand almost instantly, without waiting for the next scheduled net change collection. Implementing an effective data collection strategy involves a smart combination of these methods: an initial complete refresh, regular scheduled net change refreshes, and event-driven targeted refreshes for maximum accuracy.

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