Short-Term Engineering and Job Specialization (Term Paper Sample)

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Short-term Engineering and Job Specialization
Job specialization holds definite advantages for employees in any sector. It creates more opportunities for workers to be able to specialize in their chosen fields, which is something that can result in greater productivity. Job specialization also allows workers in various industries to be able to accomplish more tasks over a shorter period. They will inevitably gain pride in their work due to their ability to operate through specialized skills.
Why it is hard to design jobs in business settings
In the present economy, it is quite difficult for jobs to be designed and factored into business settings. This is because the conditions in the business sector are not conducive for the creation of additional jobs. In the present business scene, various industries are seeking to create high-tech systems that can run without the input of workers. If they have to create room for more functions, business owners or stakeholders today prefer to subcontract these operations to other firms (Soroor, Tarokh, and Shemshadi 637). There are, at present, more incentives to do away with unnecessary positions, than to generate them. The economic recession that was experienced on a global scale some years ago resulted in many organizations cutting costs. This trend still exists at present, even though many industries that were negatively affected by the recession are on their way to recovery.
The Demand Forecasts. All inventory studies are determined by forecasts of material consumption. Managers use techniques such as strategic, statistical, and operational tactics to evaluate methods used to forecast demand. Managers have to take into consideration factors such as fluctuation of demand for a company’s products, and the need to make the necessary changes to reschedule production processes depending on the existing demand. In a department store, managers will use inventory methods for the purposes of improving demand forecast, or changing them to back orders ((Soroor, Tarokh, and Shemshadi 638). There are business organizations that seek to make back orders out of their forecasts. Many firms overlook forecasts so as to be able to predict demand, and make plans regarding when to start production processes.
Example of a Department Store.
In the clothing industry, like other manufacturing industries, predictive analytics can affect operational risk. In the absence of a dependable guide in regards to the products to be purchased, it is likely that there will be a considerable stockpile of raw materials. Predictive analytics make it possible for any existing capacities to be maximized (Chen and Xiao 227). Business organizations can effect needed changes in the process of carrying out production modeling. In clothing department stores, in particular, experience has shown that the flow of products tends to be seasonal. In such circumstances, the organization in question can benefit from the storage of operational information (Zhang, Fu, Hongyi, and Xu 69). Moreover, there could be a risk in the way knowledge is extracted from corporate logs for the purpose of directing continuous operations improvement.
The department in question utilized the time series model for its demand forecast. This is based on the non-linear trend which is representative of shrinkage or growth over a selected period of time. The time series model usually comprises of a noise component, as well as a trend component. Basically, the extent and nature of the trend in question could be obscured at times. In such instances, there are particular time series processes that are generated to explicitly duplicate the trend activity that is represented in demand data. Although the data in the time series model tends to demonstrate random fluctuations, it may also portray gradual movements or shifts to relatively lower or higher values over an extended period. If a time series model demonstrates this kind of behavior, it could be said that a trend pattern exists (Bannerman 2131). This trend pattern is the result of the existence of factors like changing demographic characteristics, population decreases and increases, and the development of technological gains that affect the market.
In the clothing industry, seasonality is used to describe the fluctuations of products during periods of high and low demand. Seasonality creates a pattern that produces marked differences in demand for consumer goods. It is normal for the stock keeping units’ level to fluctuate between 35% and 40% in different seasons. For instance, one of the periods of peak ...