Forecast Errors
Volume 2, Issue 1, January 6, 2015

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A forecast error is the difference between the actual and predicted value. The consequences are expensive inefficiencies that can be resolved with lean manufacturing technology.

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Forecast Errors Rarely Solved by S&OP

Few sales and operations planning modules offer more exception-based planning, enabling organizations to focus on the product areas where they can realize the greatest return and eliminate forecasting errors.  There are idiosyncrasies that require industry-specific functionality to plan and execute supply chain strategies faster and more effectively for achieving business growth. Visualization of complex business data at a glance, would allow users can quickly read, predict and react to information, make faster, more proactive business decisions, and achieve strategic objectives that maximize profit. “Business intelligence is a tool set that continues to evolve from backwards looking to predictive and proactive,” said Steve Banker, service director for supply chain management at ARC Advisory Group. “The greater the visibility into the supply chain, the easier it is for a company to make course corrections toward business success.” View video here.

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Forecast Errors
Volume I, Issue 5, December 15, 2014

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A forecast error is the difference between the actual and predicted value. The consequences are expensive inefficiencies that can be resolved with lean manufacturing technology.

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Forecast Errors Eliminated with Sourcing Roadmap  

Being armed with clean and accurate data regarding suppliers, costs, risks and other important criteria, is absolutely crucial as a first step in understanding where to focus sourcing efforts. Developing a sourcing roadmap that generates the greatest results requires additional analysis of organizational goals, market conditions, and spending patterns.  Correcting forecasting errors requires implementation of best practices for collecting and classifying spend and supplier data for effective spend analysis, how to use this analysis to build a long term sourcing strategy and sourcing roadmap, and how to identify an organization's data that can be most helpful in making sourcing decisions.  View video here.

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Forecast Errors
Volume I, Issue 4, December 8, 2014

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A forecast error is the difference between the actual and predicted value. The consequences are expensive inefficiencies that can be resolved with lean manufacturing technology.

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Forecast Errors Reduced by Loops Not MRP

Loops not MRP create a result which is a unique architecture that treats material flow as a series of interconnected loops instead of the MRP architecture of linear process. MRP starts with the finished goods planned demand and then extracts the bill of materials and sends schedules both to the plants and its suppliers. Any change in demand causes supplier to react which has an impact on the upstream value chain. Loops are independent, yet interconnected with each other. One loop is between the supplier and warehouse. This may have a long lead time and lot size can be in pallets. The next loop could be between the warehouse and supermarket locations in the plant.  The lead time could be one day and lot size could be boxes. The next loop may be between supermarket and assembly lines. The lead time could be hours and the lot size could be in small bin quantities. Read more here.

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Forecast Errors
Volume I, Issue 3, December 1, 2014

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A forecast error is the difference between the actual and predicted value. The consequences are expensive inefficiencies that can be resolved with lean manufacturing technology.

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Forecast Errors Must Be Eliminated According to Gartner

Ben YoKell reported in Supply Chain Management Review in the current environment of proliferating product portfolios and increasing demand volatility, there is a great deal riding on getting demand planning right. Inaccuracies in demand forecasts generate costly waves felt throughout the entire supply chain. As such, Gartner recently positioned forecast accuracy at the very top of its metrics hierarchy.  Regular analysis of detailed transactional data from many enterprises combined with process information on exactly how demand planning is performed within the enterprise(s) is needed. Individualized results from new analytical approaches have created previously unavailable insights. Read more here.

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