Indonesian consumer awareness to seek for quality fresh food products is increasing. Therefore, a cold chain logistics is required to deliver agricultural and fishery products across islands. The number of logistics providers in Indonesia are continuously growing, especially for cargo services. Our objectives are to identify the business processes and related activities in a cold chain logistics provider, to analyse problems and to provide recommendations for improvement. We conducted an in-depth interview and analysed the business process using Integration Definition for Function Modelling (IDEF0). The results showed that the major problems are lack of proper cold chain logistics and knowledge in supply chain management. Then, we recommended to improve efficiency in the major activities. These could be the direction for further development to enhance Indonesian cold chain logistics especially for other third party logistics providers to improve their competitive advantages.

The demand for reefer containers in Indonesia has been increasing due to both global and regional trade growth; however, logistics providers are still struggling with several related challenges, including a container shortage problem, which is due to ineffective forecasting practices. This study aimed to improve the accuracy of reefer container demand forecasting by introducing an intervention forecasting approach. This approach will help address the demand planning issue of reefer circulation. The intervention forecasting approach combines human insights from the qualitative approach with the mathematical precision of the quantitative approach in iterative sequences. This field study was conducted with an Indonesian third party logistic company in Eastern Indonesia. The training data set was analyzed to provide a pattern of demand as well as some initial forecasting parameters (such as trend and seasonal index). Then, an expert helped identify irregular demand points. The demand data was then adjusted by a sales and marketing manager according to related factors such as natural disasters, oil price increase, promotions. The selected models were then further verified using a testing dataset, and the forecast errors from various models using the raw and adjusted training data sets were compared with those of the testing datasets. The results revealed that the mean average percentage error (MAPE) after adjusting the demand was 5.43% to 6.22% for the training and 9.55% to 10.33% for the testing dataset, which is lower than that of the traditional forecasting method when there was no intervention. In summary, the adjustment forecast could increase forecast accuracy by 42.39% and 39.42% for 20-and 40-feet containers, respectively.

Globalization brought new opportunities to logistics providers worldwide, especially in the Mexico, Indonesia, South Korea, and Turkey (MIST) countries. However, this attractive opportunity also came with high risk due to the complexity of the global supply chain. Third-party logistics providers need to be prepared to minimize the risks by utilizing risk management to ensure a smooth supply chain operation. This case was conducted with a major Indonesian third-party logistics (3PL) provider, utilizing the Supply Chain Risk Management Process (SCRMP) to control and monitor all risks that can arise in the company. The structure of the method can be divided into four phases: risk identification, risk measurement/risk assessment, risk evaluation, and risk mitigation/contingency plans. The purpose of this research is to validate how the SCRMP concept performs in challenging contexts such as the Indonesian 3PL industry. The study also bridged the theoretical-practical gap by helping practitioners gain valuable insights to manage risks in the company and provided appropriate risk mitigation. The results showed that there are seven unacceptable risks requiring risk mitigation and control. Risk mitigation strategies were then recommended, based on the risks that were categorized as the most critical and unacceptable. The recommendation is expected to reduce the risks that occur in the Indonesian cold chain.