Web-based System Links Retailers with Suppliers to Improve Forecast Accuracy.
Demand Forecasting, S&OP, CPFR
Demand Forecasting is a process of estimating the service or product quantity required by end customers. Demand forecasting techniques include the use of current or historical sales data to anticipate future demand. Other factors taken into consideration in make the final forecast are seasonal trends or 'events' trends. Data from finance, sales & marketing and production are all usually considered. The demand forecast that is arrived at is based on this combined information once fully assessed. Other software functionality elements include Sales & Operations Planning (S&OP). This is a business management process that takes into consideration data from both the business/finance and operational aspects of a company to reach determine a forecast or plan that best serves the whole organisation, rather than a segment of it.
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Industrial Data & Information, Inc. announces the third edition of a new valuable resource for professionals in the supply chain field. This resource is the "Glossary of Supply Chain Terminology For Logistics, Manufacturing, Warehousing, & Technology".
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Inventory Optimization Company Recognized for Addressing Food Industry Needs for Product Freshness and Smaller Inventories.
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Turning Global Service Supply Chains into Profit Centers.
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Combination creates one of the largest enterprise software companies in the world.
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FCI Automotive Austria, GmbH, manufactures automotive electrical connectors used by Volkswagen, General Motors, Renault-Nissan, Ford, DaimlerChrysler and BMW. More than one million parts are produced daily at the company's Mattighofen site, where the annual consumption of raw plastic amounts to roughly 2,300 tons.
7ELEVEN, INC. SELECTS KHIMETRICS CUSTOMER DEMAND SOLUTIONS TO OPTIMIZE PRICES, PROMOTIONS AND FORECAST DEMAND
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Khimetrics solution allows 7-Eleven to model customer needs, analyze market-basket data, optimize price and promotions, and construct more precise demand forecasts.
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Latest Addition to DS One Helps Simplify Collaboration Between Buyers and Sellers, Making Demand-Driven Supply Networks a Reality.
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Lori Mitchell-Keller, Manugistics Senior Vice President of Global Marketing and Product Management , shares her insight on the value of implementing and maintaining an optimal forecast accuracy chain.
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ACR Logistics and Mobiltron have today announced a partnership to provide an innovative and groundbreaking extended Supply Chain Service for the Wireless markets of Europe, Middle East and Africa.
Forecasting demand is an important task for just about any type of business. Accurately projecting the demand for specific goods and services helps companies to order raw materials and schedule production of those products in a timely manner, making it possible to fill consumer orders quickly and efficiently without the need to build up a large inventory that adds to the tax burden of the business.
Demand Forecasting is the activity of estimating the quantity of a product or service that consumers will purchase. Demand forecasting involves techniques including both informal methods, such as educated guesses, and quantitative methods, such as the use of historical sales data or current data from test markets. Demand forecasting may be used in making pricing decisions, in assessing future capacity requirements, or in making decisions on whether to enter a new market.
Collaborative Planning, Forecasting and Replenishment (CPFR, a trademark of the Voluntary Interindustry Commerce Standards (VICS) Association), is a concept that aims to enhance supply chain integration by supporting and assisting joint practices. CPFR seeks cooperative management of inventory through joint visibility and replenishment of products throughout the supply chain. Information shared between suppliers and retailers aids in planning and satisfying customer demands through a supportive system of shared information. This allows for continuous updating of inventory and upcoming requirements, making the end-to-end supply chain process more efficient. Efficiency is created through the decrease expenditures for merchandising, inventory, logistics, and transportation across all trading partners.
Demand forecasting is used to determine the number of products or services that will be purchased by consumers in the future. Numerous methods can be used when integrating demand forecasting into any business.
Most demand forecasting methods fall under four basic categories or methods –
3) Time series methods
4) Casual method.
Quantitative Methods: These methods forecast demand levels based on analysis of historical time series i.e quantities sold in the past to forecast how much will be sold in the near future. This is usually a forecast that will provide numbers for the next sales year. Some examples of quantitative forecasting methods include last period demand, multiplicative seasonal indexes, and simple and weighted moving averages. Each of these use quantities sold in different types of mathematical formulas to determine how many products or services will be sold at the same times in the future year’s sales that is being predicted.
Qualitative Methods: These methods rely on experts who try to quantify the level of demand from the available qualitative data.
The two most widely followed methods are:
- Jury of execution opinion method: Opinions of a group of experts is called for and these are then combined to arrive at the estimated demand.
- Delphi Method: In this method a group of experts are sent questionnaires through mail. The responses received are summarised without disclosing the identities. Further mails are sent for clarification in cases of extreme views. The process is repeated till the group reaches to a reasonable agreement.
Time Series methods: Demand forecasting typically does use strategies in the time series method to forecast the demand of products and services. The time series method can be split up into two different types of methods. These include frequency domain methods and time domain methods. Even though the frequency domain method is classified as a time series method, it is not based on time, but on frequency of the occurrence happening or a product being bought. Time domain will show quantities purchases with respect to time.
Casual methods. These methods work under the assumption that underlying incidents can affect sales numbers of products and services. Examples of casual methods include seasonal activities such as ice cream sales or sales of gardening products. These casual methods also may use linear relationships between sales and another components that remain consistent over time. If the linear relationship remains consistent, then it is a safe prediction.
Demand forecasting encompasses many types of methods and is not limited to those listed here. This forecasting helps those in businesses to determine projected quantities of products or labour needed to provide services for future sales. In addition, demand forecasting can be an effective tool for those new to certain business industries. These methods can assist in writing business plans and obtaining the funds needed to fund a new business venture.
Sales and Operations Planning (S&OP) provides an updated forecast that leads to a sales plan, production plan, inventory plan, customer lead time (backlog) plan, new product development plan, strategic initiative plan and resulting financial plan. Variations in frequency of the plan depend on the type of manufacturing industry. Short product life cycles and high demand volatility require a tighter S&OP planning than steadily consumed products