As the public becomes more aware of environmental issues and global warming, consumers are asking more questions about the products they are buying, with the result that a growing number of companies are considering the move to green manufacturing processes and supply chains. However, 'going green' can be a complex transition from an operational perspective, and not all companies are convinced that improved environmental performance will lead to financial gains. "Many companies are concerned that changing their established processes and implementing a green supply chain could result in lower quality products, delayed shipments, or even a loss of business," explained Dr. Sachin K. Mangla of the Indian Institute of Technology Roorkee and Graphic Era University - Dehradun.
A company's supply chain includes everything from purchasing, planning and managing the use of materials, to shipping and distributing the final product. Any delay or disruption can cause the supply chain to break down, which in turn can have a catastrophic impact on the business. This makes a company's decision to transition to a green supply chain (GSC) daunting, as it needs to identify, understand and evaluate all potential risks and consequences before 'going green' at an operational level. "Supply chain management is a key area of focus for businesses wanting to modernize and optimize their profits," said Mangla. "We wanted to build a risk analysis model that would help the increasing number of manufacturing companies that might be considering the move to a green supply chain."
With its headquarters in Sendai City in Miyagi Prefecture, Hitachi Solutions East Japan Ltd. is responsible for data and risk analysis of supply chain management, production, sales and inventory planning and management. They are also responsible for research and development of analytical methods and the creation of new products (including new packages and cloud-based services). For Hitachi, risk management means the analysis and repetition of potential risk factors, identification of risk, prioritization, and follow up action and monitoring.
There are a wide variety of risks in the manufacturing industry, including fluctuating variables such as demand, price, and foreign exchange. For demand fluctuation, for example, the amount of supply (production and procurement) is decided based on demand forecasts. If the forecast is wrong and there is a supply shortage, then the company will miss opportunities to increase sales. If there is too much supply, then there are inventory or disposal costs. Price and currency fluctuations are also important factors for risk management in the manufacturing sector, and the improvement of forecast accuracy through risk analysis is essential to mitigate these risks.
Dr. Sachin K. Mangla
Indian Institute of Technology Roorkee and Graphic Era University
A Multi-Stage Process
To create the green supply chain (GSC) model, Mangla and the team interviewed several senior managers, IT managers and supply chain professionals within plastic manufacturing to identify operational risks that could disrupt the supply chain. These risks included machine, equipment or facility failure, process design problems, lack of skilled labour, system or software failure, and green technology inadequacy. After generating time-based effects for each of these risks to help measure the potential delays and disruptions to the GSC, the team identified the consequences for each of these risks: delays in production (time), materials and labor (cost), brand impact, health and safety, and product quality.
Mangla and the team knew they would need to run Monte Carlo simulation to address the wide range of uncertainties associated with these risks and consequences. The team analysed the data using Palisade's @RISK software, using Triangular distributions to determine the minimum, maximum and 'most likely' potential disruptions to the GSC. "We also found @RISK's sensitivity analysis feature extremely useful, as it provided an easy and efficient way to determine the average potential disruption of materials and products for each of the identified risks," said Mangla.
The results generated a wide variety of possible risk scenarios, as well as associated probabilities. "@RISK enabled us to not only predict the type of risks that could happen, but also anticipate what risks were most likely to happen," said Mangla. "This enabled us to create a model that provides companies with visibility into the potential ecological-economic gains of a green supply chain, as well as recommendations to best manage the operational risks."