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It is a common phenomenon especially in the motor industry. For instance in 2009, General Motors recalled approximately one million vehicles from showrooms and consumers due to defects in their braking system. Statistical control plays a role in reverse logistics by ensuring process of recalling goods back to a manufacturer is conducted quantitatively to avoid financial losses to a manufacturer.
Several parameters are considered before reverse logistics can be actualized, statistical control serve to moderate the said parameters to ensure that the process is conducted in a controlled manner that will be to the advantage of the organization financially. Some group view reverse logistics as an important aspect of the supply chain while other do not. Statistical control applies its measure in the situation by depicting the advantages that will be gained by successfully implementing reverse control for instance if a company is responsible for all its inventory returns chances are it will retain consumer loyalty and at the same time protect the brand of the product in question. Conversely, whenever decisions are being made on whether to return a product or not various considerations should be explored before a final decision can be done. Statistical control is applied to provide the analysis of the trade-off thus making informed decision concerning reverse logistics (Keller, 2011).
Before an organization can make, a decision on reverse logistics various considerations have to be explored to ensure the decision is viable financially to the firm at the same time it is also beneficial to the customers (Dyckhoff, Fandel and Reese, 2004). Whenever a product is being recalled it is, of course, not in its original condition, it may be worn out or damaged. Reverse logistics will be viable to a product of high quality, since the cost of transporting the product back to the manufacturer can be met
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