| Περίληψη : | Environmental consciousness has become very critical in the design and thefunctionality of the supply chain. In recent years, interest regarding carbon footprintmonitoring of business operations tends to be the center of attention. The constantlyemerging new technologies, in combination with the increasing energy demands, haveturned companies on searching more efficient and effective ways for reducing theircarbon emissions and by extension their daily costs. Obviously, due to globalwarming, green supply chain management has become a new trend. The appearanceof RFID-based and unique instance identification mechanisms, have triggeredbusinesses’ and researchers’ interest on using upgraded techniques of supply chainmanagement. Many benefits of the unique instance product identification mechanismswere found to be associated with the distribution and the transportation area of thesupply chain and the same applies at the retail domain. Most researchers concentrate,though, more on the technological effect of these innovations and less on their effecton the quality attributes of the available data. Companies deal with problems of poordata quality, especially since the complexity and the amount of data becomes biggerand bigger.This research focused on the information accuracy of products’ carbon footprint inwarehouses. This thesis contributes with a framework for estimating carbon emissionsof products at warehouses by using two kinds of product monitoring: unique instanceand item level product identification. On the one hand, unique instance productidentification gives the opportunity of gaining as much analytic information aspossible regarding products’ movement. For example, products identified with uniqueinstance mechanisms are recorded at their precise time of entering and leaving thewarehouse. On the other hand, item level product identification treats products as agroup of instances usually sharing the same code (e.g. barcode) and gives a moregeneral insight regarding their movement. An empirical study was performed in thetwo basic sections of a retailer’s warehouse. Specifically, CO2e calculations wereconducted for the ambient and the refrigerated-frozen section of the warehouse, basedon a sample of products. The average CO2e of each product was calculated and thenaggregated results of carbon emissions were computed based on clustering (fast8moving, slow moving), items per case, volume and category. Different CO2e valueswere found for unique instance and item level product identification, and then, theinformation accuracy of the real (unique instance) and the estimated (item level) valueof energy data (CO2e) was, also, calculated. Measurements have shown that there is adifference between the two different ways of identification and that the accuracy ofthe estimated value (item level identification) reaches, in most cases, 50% of the realvalue (unique instance product identification).Further research could be conducted regarding statistical differences of the two waysof monitoring carbon emissions, so as to estimate their effect in businesses. Moreover,it would be of great interest to calculate CO2e emitted from other sources such aslightning, air conditioning etc. (overhead emissions) and CO2e emissions ofwarehouses in combination with the carbon emissions of transportation procedures.There is indisputably a respectable amount of carbon emitted. Finally, CO2emeasurements regarding products’ volume could be also realized.
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