A Life-Cycle Based Approach to the Remanufacturing Printing Supplies in Shanghai

With the fast growing of printing supplies industry in Shanghai, it creates a large waste stream of obsolete printing supplies and causes great pressure on the environment. The information of remanufacturing printing supplies in Shanghai is gathered and waste stream is analyzed. As a case study of original toner cartridge and remanufacturing toner cartridge is compared in the context of life cycle methodology. The results show raw materials, energy consumption and pollution emission of the remanufactured toner cartridge are less than these of original toner cartridge. Environmental loads equitant including global warming potential, acidification potential, photo-oxidant formation potential, solid waste and fume from remanufacturing toner cartridge are cut 3.61%, 3.84%, 15.24% and 22.52% comparing to original toner cartridge. Its significance is that remanufacturing printing supplies can be more profitable and less harmful to the external environment than conventional manufacturing process. It also discusses strategies for lowering the environmental burden to promote the remanufacturing printing supplies in Shanghai.

A Study on Explosion Characteristics of Toner Powder

Toner powder is an important constituent of a toner cartridge for a laser printer and has potential hazards of combustion and explosion because of its physicochemical characteristics. In this paper, physicochemical, combustible and explosive characteristics of hpQ2612A toner are investigated experimentally. The mean size of the toner powder is about 2.45 μm and it has several organic groups such as carboxyl group, hydroxyl group and carbonyl group which make the toner dust much easier to combust. The TG-DSC curves show that the toner dust is combustible and the combustion process starts at about 368 . The dust explosion characteristics of the toner dust were, respectively: MIE=5~30 mJ, Pmax=0.74 MPa, (dP/dt)max=100.5 MPa/s, LEL=40~50 g/m3. These results reveal that the toner powder is a dangerous industry dust and has the possibility to make tragedy such as combustion or even explosion.

Forecasting Supply Chain Demand Using Machine Learning Algorithms

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.

Forecasting Supply Chain Demand Using Machine Learning Algorithms

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.