Прогнозирование - Лекция 7. Oracle Demand Management
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Курс "Компьютерная поддержка прогнозирования" Лекция 7. Система планирования спроса Oracle Demand Management
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Прогнозирование - Лекция 7. Oracle Demand Management
- 1. Курс «КомпьютернаяКурс «Компьютерная поддержкаподдержка прогнозирования»прогнозирования» Заходякин Глеб Викторович, кафедра Информационных систем и технологий в логистике e-mail: postlogist@gmail.com
- 2. 2 Система планирования спросаСистема планирования спроса Oracle Demand ManagementOracle Demand Management o Процесс планирования спроса: входы, выходы, этапы и роли o Системы планирования спроса o Основные компоненты Oracle Demand Management – Архитектура системы – Модель спроса – Основные задачи, связанные с прогнозированием o Реализация
- 3. 3 Прогнозирование спросаПрогнозирование спроса и управление цепью поставоки управление цепью поставок Размещение продукции Управление цепью поставок Управление спросом Управление поставками Маркетинг Продажи Распреде ление Производс тво Закупки Прогнозирование спроса Планирование продвижения Планирование спроса Планирование потока товаров Планирование продаж и операций Исполнение цепи поставок
- 5. 5 Что влияет на план спросаЧто влияет на план спроса План спроса: 1-18+ месяцев $ и штуки Статистический анализ Планы маркетинга Планы продаж Планы развития продукта/бренда Бизнес-план и стратегия
- 6. 6 Планирование спроса и прогнозированиеПланирование спроса и прогнозирование Обновленные стратегические и тактические планы продаж и маркетинга История продаж, факторы Статистический прогноз (Baseline) Неограниченный прогноз (Unconstrained forecast) Совещание по планированию спроса (consensus forecast meeting) Согласованный прогноз (Consensus forecast) Планирование продаж и операций (S&OP) Сценарное планирование Предположения отделов продаж и маркетинга Прогнозы отделов продаж и маркетинга Ограничения цепи поставок Детальное планирование Финансовая оценка Межфункциональное согласование План поставок
- 7. 7 Входы и результат прогнозированияВходы и результат прогнозирования Процесс прогнозирования • История продаж • Существующие клиенты • Новые клиенты • Новые продукты • Ценообразование • Заявки • Точки продаж • Внутренний спрос • Продвижение • Конкуренты • Экономика • Указания руководства • Ошибки прогноза • Другие факторы Прогноз • разумный • реалистичный • регулярно пересматриваемый • отражающий общий спрос • возможно, неточный Лучшие процессы дают лучшие результаты! • использование всей доступной информации • документирование предположений • измерение ошибки прогноза и изучение причин • автоматизация для снижения трудоемкости и создания общей среды для планирования спроса • точная информация о спросе есть только в точках продаж
- 9. 9 Объемный и детальный уровниОбъемный и детальный уровниТочность Детализация Объемный прогноз для планирования спроса и мощностей Детальный прогноз для планирования материалов и производства Средний горизонт Короткий горизонт Тип продукта, уровень качества, размеры, бренд, сегмент рынка, канал сбыта, клиент всего ~6-12 групп Потребность в детализации прогноза спроса должна быть обоснована
- 10. 10 Программное обеспечение дляПрограммное обеспечение для решения задач прогнозированиярешения задач прогнозирования o Виды ПО – Статистические пакеты общего назначения – Программы для автоматического прогнозирования – Системы планирования спроса o Типичный набор инструментов в пакете статистического прогнозирования: – преобразование и подготовка данных – описательные статистики и графический анализ данных – инструменты анализа структуры данных – автокорреляции, спектральный анализ – построение однофакторных и многофакторных моделей регрессии, оценка значимости коэффициентов моделей и факторов – построение моделей сглаживания временных рядов – построение однофакторных или многофакторных моделей АРПСС – инструменты для автоматического применения моделей и контроля результатов o Обзор систем 1 и 2 типа – Forecasting Software Survey (журнал OR/MS today) http://lionhrtpub.com/orms/surveys/FSS/fssmain.html
- 11. 11 Задачи ИТ-систем планирования спросаЗадачи ИТ-систем планирования спроса Статистическое прогнозирование Планирование спроса Взаимодействие при планировании Декомпозиция прогноза на составляющие (уровень, тренд, сезонность) Представление данных в нужном разрезе и с нужной детализацией - дерево прогнозов Общая среда работы для разных подразделений и участников ЦП, workflow Автовыбор лучшей модели (регрессия, временные ряды, ARIMA) Многофакторные модели Преобразование единиц Визуализация прогноза Сценарное планирование Сбор информации из разных источников, включая партнеров в ЦП Оценивание точности прогноза Возможность документировать изменения Согласование планов закупки, продаж, маркетинга Моделирование событий и интервенций Хранение всех версий прогнозов Прогноз запасов, пополнений, взаиморасчеты и т.п. Возможность коррекции истории / прогноза, отслеживание изменений Мониторинг точности, KPI, обработка исключений Обмен данными с ERP/APS- системой
- 12. 12 КомпонентыКомпоненты Oracle Enterprise OneOracle Enterprise One Supply Chain PlanningSupply Chain Planning
- 13. 13 DASH КомпонентыКомпоненты Oracle DemandOracle Demand ManagementManagement Design Studio Forecast Studio Consensus Conference Room Business Modeler DB
- 14. 14 Модель спроса вМодель спроса в ODMODM o Модель спроса (Demand Model) включает: – Модель бизнеса (Business Model) – Данные прогноза (Forecast Data) – Версии прогноза (Forecast Versions) и назначения пользователей – Историю продаж и прогнозов o Модель бизнеса описывает: – Структуру бизнеса в разрезах продуктов, местоположений, каналов продаж (Product, Location, Channel + property names/values) – Точки спроса (Demand Points) – Единицы измерения и коэффициенты пересчета (Units of Measure) – Горизонт истории продаж: длительность и интервал (Sales Horizon) – Горизонт прогнозирования (Forecast Horizon) – Иерархию агрегирования (Aggregation Hierarchy) – Периоды активности продуктов (Effective Dates)
- 15. 15 Структура бизнес-моделиСтруктура бизнес-модели o Координаты в бизнес-модели: – Продукты (product) – отдельные товары или услуги – Местоположения (location) – географические точки, в которые отправляется продукт – Каналы продаж (channel) – компании, дистрибьюторы или физические лица, участвующие в движении товарного потока o Структура бизнес-модели формируется при помощи атрибутов (property) и значений (value) – Имена свойств (property names) общие для всех измерений модели – Значения свойств (property values) уникальны для продуктов, местоположений и каналов продаж – Имена свойств группируют данные на каждом уровне – Состав групп определяется уникальными значениями этих свойств
- 16. 16 Точки спросаТочки спроса o Точки спроса (demand points) – местоположения, в которых продукт может продаваться через определенный канал продаж – Пример: продажи пива баночного в Москве через розничную сеть X o На нижнем уровне находятся продажи отдельного продукта в конкретном местоположении и через конкретный канал продаж – «листья» (leaves) в иерархической модели спроса o Прежде чем продукт может быть включен в прогноз, должны быть созданы соответствующие точки спроса
- 17. 17 Единицы измеренияЕдиницы измерения o Данные могут быть представлены в нескольких единицах измерения (units of measure) o Пересчет производится на основе заданных коэффициентов o Выбирается базовая единица измерения, для остальных задаются коэффициенты пересчета
- 18. 18 Горизонты прогноза и историиГоризонты прогноза и истории o Горизонт прогнозирования и история продаж содержат заданное число периодов прогнозирования (time buckets) o Типы периодов прогнозирования: – Дни – Недели – Месяцы – 4 недели – Годы – 445 o При загрузке данные автоматически преобразуются к нужному периоду
- 20. 20 Этапы прогнозирования спросаЭтапы прогнозирования спроса вв Forecast StudioForecast Studio Создание нового сценария Инициализация новых точек спроса Отбор точек спроса для включения в сценарий Настройка параметров сценария (класс моделей, факторы) Сценарное планирование Коррекция прогноза и добавление примечаний Сопоставление и согласование версий прогноза Экспорт сценария в версию прогноза Дезагрегирование прогноза Контроль ошибки прогноза и исключений Экспорт прогноза в системы планирования Загрузка модели спросаЗагрузка модели спросаЗагрузка модели спроса
- 21. 21 Согласование прогнозов вСогласование прогнозов в ODMODM Создание отчета об исключениях Анализ и устранение причин расхождений Рассмотрение примечаний и комментариев Дезагрегирование прогноза Экспорт согласованного прогноза
Editor's Notes
- Основные причины прогнозирования спроса: 1. Выполнение заказов клиентов в сроки, определенные стратегией позиционирования продукта компании (производство на заказ, сборка на заказ, производство на склад) 2. Эффективное управление операционными ресурсами (персонал, оборудование, склад, транспорт и пр.) 3. Осуществление финансового планирования Качественные прогнозы способны улучшить эффективность операций Необходимо разделять процессы прогнозирования и планирования Цель прогнозирования – наиболее точное и беспристрастное предвидение будущего, проекция прошлых фактов на будущее с учетом данного набора условий среды Цель планирования – принятие решений о реалистичных целях и о способах их достижения Прогнозирование снабжает информацией ключевые процессы планирования Управление спросом – это деятельность маркетинга и продаж для стимулирования спроса: Брендинг, промо-акции, реклама Процесс прогнозирования – основан на обработке данных и формирует данные для принятия решений, но само принятие решений – функция планирования.
- After much debate over the last forty years, there appears to be some consensus that an organization should operate from one sales forecast. That forecast has to be comprehensive because so many different constituencies must use it. If a central forecasting process is not meeting all the organization’s needs, “pirate” forecasting will often spring up to meet some of those needs. Особенности функционального взгляда: У меня есть «особое» мнение о том, будет ли исполнен план Я самостоятельно могу оценить влияние этого намерения Я предприму меры, чтобы компенсировать возможное влияние на мою функцию Я предприму все необходимые действия, чтобы защитить/обезопасить свою функцию
- Information Consumers Executives typically fall into the information consumer category. They are busy people with a lot of responsibilities and never have enough time in their day to sift through reams of paper or analytics. They need the capability to constantly monitor the health of the business and when alerted take immediate action to resolve the issue. Dashboards, scorecards, alerts, and high-level reports deliver the information they need. What’s going wrong? What’s going right? Am I on track to meet my commitments to the business? The demand-driven forecasting process flow model requires that information consumers have an easy-to-use interface that allows them to quickly gain access to the information they need to monitor operations, stay ahead of emerging issues, and make fact-based decisions. Such tools and applications allow for immediate BI reviews daily, weekly, and monthly. This is particularly important during the monthly consensus forecast review meeting. Process Managers Process managers are in daily contact with their teams throughout the monthly consensus forecasting process and report to executive management the results of the final consensus forecast. Process managers are key in delivering the consensus forecast at the monthly S&OP meeting. Any changes in the corporate product hierarchy are their responsibility, as are any new product introductions. They are responsible for making sure that all changes are incorporated in the process. Process managers are measured not only on forecast accuracy but also on those KPIs directly affected by the consensus forecast, such as inventory carrying costs, obsolete slow moving products, inventory turns, forward forecast coverage, and others. Process managers are knowledgeable about all aspects of the process, as they most likely were originally planners/forecasters. They also understand how the forecast affects all downstream planning areas. As a result, process managers are in weekly contact with all the downstream planning teams. They are experts in using Excel spreadsheets and internal legacy system interfaces. Process managers like to view all the departmental forecasts and assumptions in conjunction with the original statistical baseline forecast prior to committing them to the data warehouse for final review with executives. They also monitor the forecast on a regular basis (daily, weekly, or monthly) to ensure that they can provide feedback to senior management as to progress. Finally, they are responsible for writing a monthly variance reconciliation report that is viewed by all process participants and executive management. This report outlines the variances between the monthly consensus forecast and actual demand supported by a brief executive summary highlighting any severe deviations (±20 percent or more). Normal errors within a designated tolerance range do not need to be reconciled with explanations. Statistical Modelers Statistical modelers are responsible for modeling and forecasting unit volumes for several key customers, channels, categories, brands, and SKUs (up to 10,000 total). They are responsible for creating statistical baseline forecasts by week or month for a 12- to 18-month horizon. Forecasts are updated on a weekly/monthly basis and measured on a frozen horizon that typically corresponds to supply planning lead time. For example, a manufacturer that must lock in production plans three months in advance would measure forecasting performance against forecasts frozen three months in advance. Since the unit volume from these key customers (accounts) and SKUs has such a large impact on what is available to manufacture and ship, there is a great premium on accuracy. Significant focus is placed on the development and accuracy of the statistical baseline forecast—particularly how it relates to the planned activities of sales and marketing and the company’s revenue and profit position within the industry. Therefore, modelers spend a lot of time trying to refine the statistical baseline forecast models, tweaking parameters, and adding predictive variables (such as price, advertising, promotional events, marketing activities, competitor activities, and others stored in an enterprise data warehouse). However, they do not try to change the model parameters on a weekly/ monthly basis because this is not considered good practice. They recalibrate the models only on an exception basis when new activities are generated by sales and marketing and/or if a model does not meet the forecast accuracy requirements and business rules. Planners/Forecasters Planners/forecasters work in the sales, marketing, financial, or operations planning departments and are responsible for making adjustments (overrides) to the statistical baseline forecasts. They contribute critical domain knowledge that increases the accuracy of the forecasts that make up the final consensus forecast. They normally have limited statistical backgrounds but have bachelor’s degrees in a specific discipline or area of expertise. When planners first take forecasting jobs, they know very little about statistical forecasting methods but have a good feel for the dynamics of the industry. However, after becoming forecasters, they normally attend courses on forecasting methods and understand the importance of starting the consensus forecasting process with a good statistical baseline forecast. Planners like the graphical interactive interfaces of modern statistical forecasting software, because these interfaces do not require them to deal with any arcane code. However, they prefer the flexibility and feel of Excel spreadsheets, which is the standard for sharing information across the company. Planners also understand how the different model classes are appropriate for different kinds of data and so can troubleshoot and provide the modelers with some guidance on input variables relevant to their area of responsibility. However, planners do not know enough to be able to custom-build statistical forecast models. They understand that it is possible for advanced software to take into account special events. So the planners provide the modelers with information regarding sales promotions and marketing events. The planners trust the modelers’ statistical understanding of how to build models, and they trust the software to execute the models. As such, the planners never second-guess what the modeler or software is actually doing as long as it seems reasonable and practical from a domain knowledge perspective. The measure of success of any process is a strong commitment to it among the process members. This is true as well for the demand-driven forecasting process. Also, as in most process design flow models, the burden falls on the shoulders of the demand forecasting champion to gain acceptance of the new process, because in many cases it will be radically different from what was used earlier. The demand forecasting champion will need the ability to directly influence participant behavior and set the proper expectations. As such, he or she will need to be a member of the senior management team, directly reporting to the chief executive officer, president, or senior vice president responsible for supply chain management.
- Прогноз должен быть приблизительно верным, достаточным для планирования закупок и производства на коротком горизонте и планирования мощностей на среднем. Точность – это редкое свойство прогноза. Статистические приемы прогнозирования, основанные на истории продаж стандартны и обычно выступают отправной точкой. Применяются простые модели, типа экспоненциального сглаживания, или модели, разделяющие временной ряд продаж на компоненты – Винтерса, сезонной декомпозиции. Обычно программные пакеты имеют стандартные алгоритмы для моделей сезонных временных рядов, оценки трендов или множественной регрессии (для учета факторов). В статистический прогноз вносятся изменения, отражающие действия коммерческих подразделений и эффект от них. Необходимо управление процессом прогнозирования и эффективная среда для разработки прогнозов (ИТ). “Прогнозирование имеет гораздо большее отношение к созданию и поддержке рационального процесса прогнозирования, использующего все имеющиеся знания, чем к специальным статистическим приемам и программному обеспечению” Типичный диалог между прогнозированием и производством - Маркетинг: почему ты не использовал данные прогноза как основу для производственного плана? - Производство: потому что я не доверяю прогнозу - М:но прогноз –это то, что по мнению маркетинга и продаж, хотят купить наши клиенты - П: по-прежнему не верю этим цифрам, они всегда ошибочны
- it was a bit of a conceptual breakthrough when software vendors first presented an aggregation triangle, with SKU/locations at the bottom and highly aggregated product groups at the top — and the ability to manually edit the forecast at any level and have the software automatically aggregate or disaggregate up or down from there. Now we view the forecast as a pyramid with multiple faces, and hence multiple triangles with a different product aggregation scheme on each face for some constituency The forecast should not be “aligned” with any particular plan, of course, since the forecast supports all kinds of plans. On the other hand, most planning processes will begin with some type of forecast transformation logic to format the forecast for input to that process or software module. A deal code is a discount promotion code that you may receive via an advertisement or a promotional activity.
- Прогнозы на уровне компании/бизнеса/рынка в целом не дают ничего для планирования операций Прогнозы на уровне номенклатуры слишком детальны, неточны в перспективе и трудоемки. Товарная группа (Family Goods) – группа индивидуальных товарных позиций, объединенная для целей прогнозирования спроса по признаку схожести производственных ресурсов.
- Most forecasting software falls into one of the following four categories: Spreadsheets are often used to generate forecasts—they shouldn’t be. Although it is easy to implement simplistic forecasting models on a spreadsheet, these models should be avoided (see discussion above). Spreadsheets and relational databases can provide convenient environments to store historical data, monitor forecast accuracy, and generate reports. In addition, most forecasting packages are designed to interface with these products. 2. Broad-based statistics packages are designed for statisticians. They offer a wide range of analytical tools. Forecasting is almost always available, usually as an optional module at extra cost. These packages assume a strong quantitative background on the user’s part and require that all model-building and validation decisions are made by the user. Examples of popular general statistical packages include SAS and SPSS. 3. Business forecasting packages are designed for business people. They tend to incorporate the forecasting methods best suited to corporate data such as exponential smoothing, Box-Jenkins, Croston’s intermittent demand model, event models, and dynamic regression. They offer a high degree of automation in terms of model selection and validation, allowing the non-specialist to use the packages effectively. Most offer the ability to judgmentally override the statistical forecasts. Some even offer the capability to define and reconcile hierarchical forecasts. Examples of popular business forecasting packages include Forecast Pro and Autocast. 4. Forecasting engines are designed to generate large numbers of forecasts automatically. Most forecasting engines can operate either independently or integrated with some type of planning system (ERP, MRP, supply chain, and so forth). Though similar to business forecasting packages in terms of functionality, they are designed to handle large-scale jobs. Both of the business forecasting packages identified above offer “batch editions” (forecasting engines). Some planning systems, particularly those encompassing supply chain management, include built-in forecasting capabilities. Some are based on simple methods, others on statistical models. The good ones feature an array of statistical models, automatic model selection, hierarchical forecasting, forecast adjustment, forecast performance monitoring, and the ability to maintain multiple forecast scenarios. There are scores of these systems available. They run the gamut from fairly simple demand management packages costing less than $10,000 to all-inclusive corporatewide solutions priced at millions of dollars. Examples of economically priced demand-management systems with integrated forecasting include OptiPlan and Demand Solutions. Examples of higher-cost all-inclusive forecasting and planning systems include i2 Technologies, Logility, and Manugistics.
- Shutt Statistical techniques for sales forecasting are well established and are usually used as a starting point. There are multiple mathematical models available, e.g., exponential smoothing, and software tools designed to figure out the best models to use for each part of the product line. The most intuitive, easily understood forecasting models are of the Winters type, which break the components of a forecast into base, trend, and seasonal components (to which adjustments for planned promotions are then added [or subtracted] by the software for businesses with sales promotion). Good forecasting software packages typically have time series–type logic to estimate trend and seasonality and regression logic of some type to separate the impact of specific “events” such as promotions or price changes. The profile of event impact is then added into future forecasts when a similar event is planned. The statistically generated model should normally just be the starting point for a forecasting process that incorporates manual adjustment from members of the organization who have specific information to contribute. Defining this human adjustment process requires a fair amount of thought about where knowledge resides in the organization and can be efficiently applied to the forecast, and sometimes recognition that part of the organization is too biased to make accurate contributions to the forecast. It also requires a full-time forecasting process management staff that not only attends the statistical tools and manages the data, but also supervises the human revision process. Sometimes it requires the use of collaborative planning software to support interaction between a field sales force and central forecasters. Indeed, an enterprise may appropriately spend as much dedicated staff time on forecasting as it does on planning. Forecasting is more about creating and maintaining a rational forecasting process that assembles all the knowledge than it is about specific statistical techniques or software packages. Mature forecasting processes carefully measure forecast accuracy. Understanding our errors and whether we have systematic error such as bias high or low in the forecast or a forecast adjustment process that increases error instead of reducing it are usually the keys to more accurate forecasting. We are particularly interested in forecast accuracy metrics to support product flow planning, because some of the decisions we have to make, such as inventory safety stocks, depend directly on forecast accuracy. Advanced forecasting approaches will likely track sales for each individual major customer, and then forecast sales by those individual customers (and often in collaboration with those customers). Historic sales information is becoming an increasing part of contemporary customer relationship management systems, and these systems track sales by customer. These forecasts must of course then be correctly combined with more aggregate forecasts for “all the other customers.” This kind of customer-specific forecasting has been used for some time by consumer goods companies, and they have had considerable success with it. Collaborative planning, forecasting, and replenishment (CPFR) was introduced several years ago by the VICS manufacturer–retailer consortium (Voluntary Inter-Industry Commerce Standards Association). It includes a specific methodology for collaborative planning and forecasting. Its greatest successes have come in collaborative forecasting, which has proven particularly valuable in the industry where CPFR was invented: consumer goods manufacturing and retailing. Part of the reason for this is that in this industry, promotions are originated at both the retailer and manufacturer levels, and to really create an accurate forecast the promotion impacts must be combined. While collaborative forecasting has achieved documented successes in increasing supply chain efficiency, it is quite difficult to take to scale, that is to a large number of SKU/locations, because of the fairly manual way it has generally been implemented. chase Considering the large amount of enabling forecasting technology on the market today, selection of a software solution should be made based on components that enhance the effectiveness and maximize the impact of demand forecasting in support of the S&OP process. The demand forecasting process can benefit from an enabling solution that incorporates these capabilities: • Extract, transform, and load data intelligence capabilities • Scalability to create product hierarchies for thousands of products • Advanced analytics with optimized model selection • What-if analysis to shape demand • The ability to support collaborative/consensus forecasting and planning • Exception forecasting, monitoring, and reporting with alerts • Reduced forecast cycle times A good demand-driven forecasting process will enhance the S&OP process by providing a consensus-forecasting environment that incorporates statistical methodologies, dashboarding capability, and workflow to create a more accurate unconstrained demand forecast. Shutt: While some planning approaches attempt to minimize their dependency on forecasting by focusing on quick reaction to changes in operations, it is impossible to eliminate the need for forecasting. However, in situations where there is very little real information to drive a forecast — particularly very short-term forecasts over the next few weeks of sales— it may be appropriate for operations planning to assume a constant average rate of demand rather than try to guess about essentially random ups and downs. It should also be noted that in forecasting, like planning, the enterprise chooses how much money it wants to spend to do it well. Not every organization can afford to spend a lot on it, and smaller enterprises may find that an elaborate forecasting process is not cost effective.
- Интеграция SC Business Modeler перенос данных из SCP систем в DF EO SCM World Manufacturing Mgmt EO S&OP EO SNO SNO оптимизирует уровни запасов выбор поставщиков EO Production & Distribution Planning чистые потребности развертывания чистые потребности производства EO Order Promising EO Production Scheduling discrete process оптимальные расписания для достижения целей производства EnterpriseOne Supply Chain Management Systems EnterpriseOne Supply Chain Management systems such as Enterprise Supply Chain Management, EnterpriseOne Supply Chain Management, and World Manufacturing Management, provide EnterpriseOne Supply Chain Business Modeler with the supply chain data that EnterpriseOne Demand Management uses to generate accurate forecasts. The data includes information about items, branches, inventory policies, and manufacturing processes. After EnterpriseOne Demand Management creates optimal forecasts using the data, you can transfer the forecasts through EnterpriseOne Supply Chain Business Modeler to a EnterpriseOne Supply Chain Management system for further refinement or implementation. EnterpriseOne Sales and Operations Planning Using EnterpriseOne Supply Chain Business Modeler, you can transfer supply chain data from EnterpriseOne Demand Management to EnterpriseOne Sales and Operations Planning. EnterpriseOne Strategic Network Optimization Using EnterpriseOne Supply Chain Business Modeler, you can transfer demand forecasts from EnterpriseOne Demand Management into EnterpriseOne Strategic Network Optimization. You can then use Strategic Network Optimization to create inventory build targets and sourcing recommendations based on the data. After creating supply chain plans in EnterpriseOne Strategic Network Optimization, you can use EnterpriseOne Supply Chain Business Modeler to transfer the plans to another EnterpriseOne Supply Chain Planning system for further refinement or to a EnterpriseOne Supply Chain Management system for further refinement or implementation. EnterpriseOne Production and Distribution Planning Using EnterpriseOne Supply Chain Business Modeler, you can transfer supply chain plans from EnterpriseOne Demand Management into EnterpriseOne Production and Distribution Planning. You can then use EnterpriseOne Production and Distribution Planning to create net deployment requirements and net production requirements based on the data. After creating supply chain plans in EnterpriseOne Production and Distribution Planning, you can use EnterpriseOne Supply Chain Business Modeler to transfer the plans to another EnterpriseOne Supply Chain Planning system for further refinement or to a EnterpriseOne Supply Chain Management system for further refinement or implementation. EnterpriseOne Order Promising Using EnterpriseOne Supply Chain Business Modeler, you can transfer supply chain data from a EnterpriseOne Supply Chain Management system to create a model in EnterpriseOne Order Promising. EnterpriseOne Production Scheduling - Discrete and EnterpriseOne Production Scheduling: Process Using EnterpriseOne Supply Chain Business Modeler, you can transfer supply chain plans from EnterpriseOne Demand Management into EnterpriseOne Production Scheduling - Discrete and EnterpriseOne Production Scheduling - Process. The EnterpriseOne Supply Chain Planning production scheduling applications can then produce optimal production schedules for meeting the production targets.
- EnterpriseOne Demand Forecasting enables you to plan for and meet future demand by creating on accurate business forecasts. Accurate forecasts ensure that you have sufficient stocks of products available when you need them. A forecast is based on a demand model and contains: • Historical information on sales - for example, sales at specific locations and during specific periods. • Historical demand for parts, components, or materials from suppliers. • Information about promotions, sales campaigns, or other initiatives that can affect demand. Forecast Studio The Forecast Studio is the EnterpriseOne Demand Forecasting workspace. You can use it to: • Create, customize, and maintain forecast data. • Generate forecasts that are based on various criteria. • Refine your forecasts by taking into account the effects of predictors, events, and interventions on those forecasts. • View the information in your forecasts either graphically or in tabular format. You can apply different statistical techniques to different forecast models; choose the forecasting algorithm that best suits your needs; or use the expert selection algorithm, which picks the algorithm that best suits your needs. In addition, you can apply different algorithms to different demand points to further refine your forecasts. Design Studio The Design Studio is a desktop workspace where you create, customize, and maintain demand model information. The Design Studio is connected to a database where model information and forecast data is stored. The demand model contains data for forecasting and for the business model. The business model contains specific structural information about a business. You can use the Design Studio to configure demand model information and allow the information to be used with other EnterpriseOne and external applications. You can create a new model or modify an existing demand model by sorting, classifying, and assigning properties. When the information about the demand model is defined, it is then configured into an aggregated hierarchical structure that defines the relationships between the units in the hierarchy. Consensus Conference Room The Consensus Conference Room is a Web-based collaborative application in which you can compare and reconcile forecasts from different users (called forecast versions) at different levels of abstraction and completeness. The Consensus Conference Room enables various users to compare forecast data for the same product, location and channel where potential exists for demand (called the demand point). When users compare multiple forecast data for the same demand point, they can correct with minimum effort any errors or miscommunications that might occur. As you use the Consensus Conference Room, you will be able to recognize when forecast data is inaccurate or flawed. Using the collaborative forecasting process, you access data easily, identify inaccuracies, and manage these inaccuracies in forecast data across multiple business units. Business Modeler Using EnterpriseOne Supply Chain Business Modeler, you can transfer supply chain data from EnterpriseOne Supply Chain Management systems into EnterpriseOne Demand Management. You can then use Demand Management to create enterprise demand forecasts and inventory safety targets based on the data. After creating forecasts in EnterpriseOne Demand Management, you can use EnterpriseOne Supply Chain Business Modeler to transfer the plans to another EnterpriseOne Supply Chain Planning system for further refinement or to a EnterpriseOne Supply Chain Management system for further refinement or implementation.