Utilizing an Intervention Forecasting Approach to Improve Reefer Container Demand Forecasting Accuracy: A Case Study in Indonesia
Corresponding email: pornthipa.o@ku.ac.th
Published at : 29 Jan 2020
Volume : IJtech
Vol 11, No 1 (2020)
DOI : https://doi.org/10.14716/ijtech.v11i1.3220
Pradita, S.P., Ongkunaruk, P.Leingpibul, T.D., 2020. Utilizing an Intervention Forecasting Approach to Improve Reefer Container Demand Forecasting Accuracy: A Case Study in Indonesia. International Journal of Technology. Volume 11(1), pp. 144-154
| Sintia Putri Pradita | Department of Agro-Industrial Technology, Faculty of Agro-Industry, Kasetsart University, 50 Ngam Wong Wan Rd. Lad Yao, Chatuchak, Bangkok 10900, Thailand |
| Pornthipa Ongkunaruk | Department of Agro-Industrial Technology, Faculty of Agro-Industry, Kasetsart University, 50 Ngam Wong Wan Rd. Lad Yao, Chatuchak, Bangkok 10900, Thailand |
| Thaweephan Duke Leingpibul | Haworth College of Business, Western Michigan University, 1903 Western Michigan Avenue Kalamazoo, MI 49008, USA |
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.
Intervention; Qualitative forecasting method; Reefer containers; Third party logistics providers; Time series forecasting
The growth of container use in marine transportation has steadily
increased in recent years due to economic development in Asia (Diaz
et al., 2011). As
the world’s largest archipelagic country, the distribution of agricultural and
food products in Indonesia is normally conducted through sea transportation (BPS-Statistics
Indonesia, 2018). According to the Indonesia
Cold Chain Association, the number of logistics providers in Indonesia is still
growing, especially third party logistics providers (3PLs), who provide shipping
services with reefer containers (Gandi, 2016). The delivery of food
products is
The findings showed that the MAPE values from
the intervention forecasting approaches outperformed the traditional time
series approach in this particular case. Specifically, the intervention
forecasting approach revealed benefits such as having data adjusted by human
insight twice. The first data adjustment acted as “a screening tool” to
identify and correct all irregular demand points prior to feeding this
“cleaned” data into the quantitative calculation. This step helped avoid the “garbage
in/garbage out” phenomenon. The second intervention adjustment from experts
acted as a “reality check tool”, which put human’s insights back into the
result from the mathematical models. Again, this step was comparable to the
seasonality index concept in time series forecasting, which adds a seasonal
characteristic back to de-seasonalized forecast demand to represent the reality
of each season. While the seasonal index might have static pattern the
intervention by expert is more event-specific and more intuitive. Given that the reefer demand forecast is very
sensitive to any weekly or even daily events, we can conclude that the
combination of human insight from the qualitative method and mathematical
precision from the quantitative method helped increase its forecasting
accuracy. Overall, with more reliable forecasting, container procurement
officers can then enhance both their circulation efficiency and customer
service levels.
In regard to the impact of different product
categories, the intervention forecasting method also yielded very similar
results for both the 20- and 40-feet containers. For both container sizes,
non-adjusted demand exhibited a higher forecast error than the adjusted demand
for both sizes. As a result, the adjustment forecast helped increase the forecast
accuracy by 42.39% and 39.42% for the 20- and 40-feet containers, respectively.
For implementation purposes, irregular events
can be defined as repeatable or non-repeatable.
Non-repeatable events can be disregarded as outliers, since they occur
accidentally; however, repeatable events should be investigated further since
they might happen again in a cyclical manner or should be included in
associated forecasting models (e.g. regression). For example, natural disasters
and subsidized projects from the government impact only impact forecast errors
once, since such events tend not to occur in the future; however, irregular
events, such as Ramadhan, in this case, will occur again. Thus, these social
events should be systematically recognized and integrated into decisions to
improve forecasting accuracy.
It is important to note that data limitations
existed in this study, such as, for example, a lack of available quantitative
data prior to 2016 as well as only having a single expert to provide insights
on the qualitative side. In order to
gain more power to apply these findings to other cases, repetitive research or
longitudinal research is required. In addition, future research could be
extended for comparison against other product categories, comparisons of
different product life cycles, or comparisons with other forecasting methods,
such as ANN or the regression method (Jaipuria and Mahapatra, 2014;
Dhini et al., 2015).
The authors would like to thank the company
for kindly providing information for this research. Also, we thank the anonymous reviewers who
gave significant suggestions to help us improve our manuscript.
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| R2-IE-3220-20200110211341.jpg | Figure 1 |
| R2-IE-3220-20200110211355.jpg | Figure 2a |
| R2-IE-3220-20200110211411.jpg | Figure 2b |
| R2-IE-3220-20200110211423.jpg | Figure 3a |
| R2-IE-3220-20200110211437.jpg | Figure 3b |
| R2-IE-3220-20200110211447.jpg | Figure 4a |
| R2-IE-3220-20200110211459.jpg | Figure 4b |
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