Essay About Supplier Relationship Deterioration Poor Forecasting And Result Of The Sales Data

ForecastingEssay Preview: ForecastingReport this essayForecasting is about predicting future events based on a foreknowledge acquired through a systematic process or intuition (Armstrong JS, 2001). Forecasting, if done accurately provides many advantages such as better financial planning, better staffing, targeted markets etc. (The Advantages of Accurate Forecasting. , n.d.)..Forecasting where on one hand can provide a business advantage on the other hand a poor forecasting can have a serious business implication. Forecasting is difficult (if not impossible) to do accurately.  Key assumptions required for good forecasting are not always evident, and key metrics are not always clear (Score ,n.d.). In the absence of a clear path by which a forecast might be developed, many entrepreneurs gloss over it. I will try to focus on supply chain management poor forecasting implication to the business. Inventory/working capital is the most direct casualty of poor forecasting efforts. Because forecasting is typically a result of the sales data driven estimates, and overestimating sales can often cause high resulting levels of inventory (working capital), and therefore, reduce the company’s profit in the end. Expedites / Missed Revenues, If the sales forecasts are too conservative on components for use in manufacturing, it might result in missing revenues altogether. Expediting costs can also arise during up markets where growing demand outpaces the supply chain’s ability to deliver parts on time.

Supplier Relationship Deterioration Poor forecasting is not exclusively an internal issue. Relationships with suppliers can be significantly deteriorated by poor forecasting and can have a further impact on a company’s supply chain down the line. Internal Administrative Costs, the forecasting efforts comes at a cost as there are dedicated teams allocated for the purpose. Failing to predict/forecast properly can result in admin overhead and can also impact the company employee values propositions.References Armstrong JS (2001). Principles of forecasting: a handbook for researchers and practitioners. Norwell: Kluwer Academic Publishers;.The Advantages of Accurate Forecasting. (n.d.). Retrieved January 26, 2017, from

A key limitation to this paper is the lack of an ‘independent’ method to generate forecasts. Instead, researchers are offered “a common (but, in my opinion, rather limited) method” for predicting production of a commodity, rather than a system based on an independent method, and instead rely on a combination of other algorithms and the data analysis of the market. This is often seen as an issue that requires extensive research and funding.
In my opinion, this paper provides a better solution for analysts, practitioners and market analysts. It also provides more realistic and realistic forecasts as well as a comprehensive and comprehensive description of the system.
The method has several strengths:
It provides a realistic approach that is able to evaluate an initial market forecast (via a pre-built, standardized, pre-designed, pre-test, pre-updated, prior version, etc., as a whole) and generates an expected amount (to be calculated in a “numerical” manner before proceeding) – an estimate is not only reasonably drawn, but it is based on a fully credible, real-time forecast that can be applied to current production in the range as well as actual demand to produce.
A robust method to generate a pre-test or production forecast on any given day: this avoids waiting for the forecast to make its way through a complex regulatory process into the production data to be presented to buyers.
A detailed description of the system can be derived from the data and other sources, including forecasts for the period preceding the forecast, the pre-test, and the resulting forecast. This does not have to be a “single” system or an application of many of the same techniques/technologies across all markets or sectors.
A set of simple procedures to automatically calculate production data: this works for any product type in the industrial context. It is also well documented in the book Forecasting for Engineers and Scientists for which the authors provide some very detailed explanations and some of the most sophisticated statistical methods to predict production: http://www.fav-industry.info/article/1/12/17/fav-industry-analytics (although as mentioned elsewhere, not in the book).  The current version of this paper is in the Early Edition in the Fall 2016. 
The key to this paper is the use of a fully standardized, pre-built, pre-trained forecasting system.  This system can be used by almost any industry to generate a pre-test or production forecast and, given sufficient support, does not require special data management, although some manufacturers will be reluctant to use this system which is also heavily used in production. 
Conclusion
The basic idea behind this paper is that “the forecasting process was never entirely bad. It wasn’t always successful, it could certainly be even better”. There has been tremendous progress in forecasting through the years, and