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Inventory Carrying Costs
Abstract
Four basic cost categories must be considered when calculating inventory carrying costs: (1) capital costs, (2) inventory service costs, (3) storage space costs, and (4) inventory risk costs.
... Inventory costs can divide into four main categories: capital costs, inventory service costs, storage space costs, and inventory risk costs, each of which fall into different types of costs. Figure 1 is based on (Lambert and LaLonde 1976) research which delineates different categories in inventory costs (Lambert and LaLonde 1976). ...
... Inventory costs can divide into four main categories: capital costs, inventory service costs, storage space costs, and inventory risk costs, each of which fall into different types of costs. Figure 1 is based on (Lambert and LaLonde 1976) research which delineates different categories in inventory costs (Lambert and LaLonde 1976). ...
... As a result, determining the inventory within the available space beside finding ways to reduce cost and selecting the best supplier according to some criteria is of high prominence (Lambert and LaLonde 1976). In this research, inventory costs such as opportunity cost, storage space cost, and ordering cost have been taken into account because the process of supplier selection heavily depends on the above-mentioned costs. ...
In this research, a supply chain network has been designed for inventory management using not only the project site storage facility but also an ancillary warehouse to keep materials. In order to make decision about the appropriate place for building the warehouse, multi-criteria decision-making techniques have been applied. Since the transportation sector, as the most important energy-consuming part, plays a significant role in global warming after power stations and the delivery of materials will have environmental impacts, this research tried to minimize the external cost of global warming caused by transportation. In this study, a mathematical formulation is presented to solve the problem of ordering the required amount to project site, while taking into account an ancillary warehouse. To quell the discussion, a numerical example has been demonstrated. The findings show that uncertainty considerations fortify the strict decision making and can increase the confidence level.
... To determine this, we must identify what level of forecasting accuracy increase is useful and this depends of the potential cost savings. An in-depth survey of six companies has identified the inventory carrying costs to be between 14% and 43% (Lambert and Lalonde 1976). A more recent survey by the Institutes of Management and Administration as reported by the Controller's Report (Anonymous 2005) has also identified most inventory carrying costs to be between 10% and 40% ( Figure 7) and represents an average holding cost of around 21%. ...
Managing supply chains in today’s complex, dynamic, and uncertain environment is one of the key challenges affecting the success of the businesses. One of the crucial determinants of effective supply chain management is the ability to recognize customer demand patterns and react accordingly to the changes in face of intense competition. Thus the ability to adequately predict demand by the participants in a supply chain is vital to the survival of businesses. Demand prediction is aggravated by the fact that communication patterns between participants that emerge in a supply chain tend to distort the original consumer’s demand and create high levels of noise. Distortion and noise negatively impact forecast quality of the participants. This work investigates the applicability of machine learning (ML) techniques and compares their performances with the more traditional methods in order to improve demand forecast accuracy in supply chains. To this end we used two data sets from particular companies (chocolate manufacturer and toner cartridge manufacturer), as well as data from the Statistics Canada manufacturing survey. A representative set of traditional and ML-based forecasting techniques have been applied to the demand data and the accuracy of the methods was compared. As a group, Machine Learning techniques outperformed traditional techniques in terms of overall average, but not in terms of overall ranking. We also found that a support vector machine (SVM) trained on multiple demand series produced the most accurate forecasts.
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