Forecasting for Building Construction Prices - Essay Example

Demand Forecasting YourFirst YourLast Demand Forecasting Demand forecasting for any product is paramount to any manufacturer, supplier or retailer. Future demand or forecasts will determine the exact quantities of products that should be shipped, produced or purchased. Demand forecasting is essential since the fundamental operations of production, right from the manufacturers or producers raw material stage to the client as a finished good is a process that indeed takes a lot of time, sometimes years to complete. A majority of firms execute demand forecasting because sometimes it is not logical to react to demand and satisfy it, given that production takes time to complete. Instead, these companies plan and anticipate for future demand to enable them satisfy the demand as soon as it emerges from the market. Most producers, therefore "make to stock" instead of "making to order" meaning they make plans and position inventories of finished products into the market. Therefore, immediately a client shows up, the demand created thereof is satisfied immediately, rather than having the client wait through production period for the process to complete. Different products have different product cycles, a vehicle may take a week or two for the assembling process to complete, and take another two or three weeks for shipment and clearance to be successfully done while it would take years for a maritime vessel to complete a similar process. Sustainable Construction: Reliable Forecasting for Building Construction Prices Forecasting and modeling prices in the building construction market are vital for associating construction data in resource application, analysis and cost management, as the construction prices variation could influence the decisions made by construction investors, property managers, construction clients, developers and financial institutions. More essentially, the capability of forecasting construction prices sequences and trends can lead to bids that are more exact and can evade overestimations or underestimations in building construction projects. According to Jiang, Xu and Liu (2013), the price fluctuation comes because of the deviations and variances of market factors in the supply and demand of construction works. Variances in prices for building and construction materials are not only affected by the changes of condition in the market but also influenced by other elements, for instance, global special events, and government policies. The authors note special events such as 1997 Asian economic crunch and the outbreak of the swine flu influenced prices and demand in Singapore and Hong Kong (Jiang, Xu, & Liu, 2013). Turkeys, Forecasting and Supply Chain Risk Management Steve Banker, the author, introduces a narrative about Gob, a hypothetical self-educated turkey. The article explains that the turkey had a strong penchant for statistics, and, therefore, he took note of the rations of the grain the farmer was feeding him, as he grew bigger. He reckons that the meals got bigger as time passed. Therefore, based on historical trends, it is safe for Gob to assume the next week; there is a high likelihood that he will get a bigger meal than the previous week. Unfortunately, the following day, Joe – the farmer – decides that it is time for harvesting and that all turkeys would have to be harvested in good time for Thanksgiving (Banker, 2010). The analogy gives a perfect description of how companies are so dependent on historical data, more than they ought to as Gob – the turkey would attest. The author argues that statistical forecasts and mathematical models can make people be surer of the future than they should be. Banker states that forecasting is dependent on historical data. Much financial and supply chain forecasting systems are founded on "bell curves" the curves tails signifying highly doubtful events that many companies safely overlook, with regard to the process of forecasting. Nonetheless, the author argues that an investigation into historical events would demonstrate that rude shocks and surprises, for instance, the 2009/2010 global financial recession might interrupt. All predictions – as good as they might be – are discarded when such disastrous happenings take place. The author, therefore, recommends that organizations need to apply supply chain risk management practices to product demand forecasts and not just to IT systems, suppliers, and factories. In line with this view, Steve Banker explains the need for organizations to go beyond the traditional historical shipment data when carrying out demand forecasting. Banker claims that when matters go south in supply chain management, company executives state that had better demand forecasting would have prevented most challenges taking place. Banker, however, claims that most companies are not affected by this and that it is not the main factor affecting the operations of the organizations. In the past, demand forecasting has been mainly focusing on the application of demand management elements that have applied statistics and other mathematical techniques to evaluate historical shipments. In addition, in the past, when people tasked with organizing supply chain operations contemplate on demand management, banker claims that they have a tendency to think about the "business to consumer" supply chain, for instance, an organization trading in consumer products estimating what (SKU) Stock Keeping Units a retailer will ask for. Banker (2013) explains how modern companies are combining non-traditional methods of demand forecasting to estimate inventory levels for their products in the market. He laments that demand forecasting management in some of these industries involves collaborating with sales teams in the field, precise estimations of the likelihood that a deal will come through and what goods the deal will comprise of and the combination of demand forecasting management and opportunity management processes. Banker cautions that historical information can have challenges related to it, and companies are going beyond historical data to improve their demand forecasting (Banker, 2011; Banker, 2013) respectively. Analytics Operation Engineering Accurate demand forecasts are essential to making sound business decisions. A good forecasting undertaking influences arithmetical methods but also takes note of their shortcomings by combining the companys collective knowledge to attain the best achievable approximation. Analytics is equipped with statistical and mathematical capabilities to create an early forecast, and the process knowledge to empower organizations to attain a cultured accord forecasting process to steer the business. Analytics methods can obtain a more consistent and coordinated decision-making approach. This enables people to make good decisions founded on their objective observational estimates of future demand. Using this method enables people to execute more accurate forecasts, and the forecasts are said to improve with time because using the analytics methods creates a standard through which all the future projections are estimated, compared and improved (Nltx, 2012). References Banker, S. (2010). Turkeys, Forecasting, and Supply Chain Risk Management. Logistics Viewpoints. Retrieved 11 November 2015, from http://logisticsviewpoints.com/2010/08/04/turkeys-forecasting-and-supply-chain-risk-management/ Banker, S. (2013). Forbes Welcome. Forbes.com. Retrieved 11 November 2015, from http://www.forbes.com/sites/stevebanker/2013/09/16/demand-forecasting-going-beyond-historical-shipment-data/ Jiang, H., Xu, Y., & Liu, C. (2013). Sustainable Construction: Reliable Forecasting for Building Construction Prices. School of Architecture and Built Environment. Retrieved 11 November 2015, from http://www.coventry.ac.uk/Global/Faculty%20events/SB13/SB13-11-Sustainable-construction-reliable-forecasting-for-building-construction-prices.pdf Nltx.com,. (2012). Analytics Operations Engineering: Forecasting. Retrieved 11 November 2015, from http://www.nltx.com/expertise/forecasting.html Read More