During the second week of classes we were posed to discuss forecasting, this blog is an amalgamation of two articles that ties together to help further explain the key and significant role forecasting plays in Supply Chain management. Furthermore, the article provides some insight on how the forecasting methods differ depending on the size of the organization.
Supply Chain Management
Supply chain management is the process companies use to determine that all facets of their production, services and/or systems have adequate supply to meet their demand needs. "Supply chain management systems are integrated partnerships among all links in the flow of goods and services to the customer. They are created for the purpose of improving quality, reducing costs and achieving competitive advantage in a world where lean manufacturing and specialization force companies to rely on one another for valuable productive activities. All supply chain activities, including planning, sourcing, producing, delivering and providing for returns, are handled collaboratively within an integrated supply chain to ensure the maximum use of shared resources". A company with a good and highly effective SCM can use this as a strategic advantage and competitive advantage ( as it helps save costs).
Forecasting
Traditionally, many large companies used to vertically integrate supplier functions and distribution channels in order to maximize production, in modern days, companies have found it more profitable to outsource these services.When a company increases its dependence on suppliers, such as through outsourcing, it exposes itself to risks associated with the supplier's operations and expanded logistics. Forecasting techniques are frequently used to measure and control these risks. A strategic approach to supply chain management involves identifying and tracking factors that can adversely impact costs and and place constraints on capacity.
Forecasting demand, and coordinating activities to meet demand, are full-time jobs. Companies with global operations use sophisticated software and systems to forecast demand, but small businesses can forecast supply chain needs using simple techniques. The methods of moving averages and exponential smoothing seek to smooth out demand to allow for seasonality in the results. With moving averages, you drop the oldest sales numbers and add newer numbers, making the average move over time. For example, to calculate sales over a four-week moving average, add weeks two through five, drop the sales from week one and divide by four. Exponential smoothing is similar to moving averages except that older data receives progressively less weight and new data receives greater weight. When there is definitive trend, however, the moving averages and exponential smoothing forecasts might lag behind the trend.
reference
No comments:
Post a Comment
Note: Only a member of this blog may post a comment.