Tactical Procurement Planning under Uncertainty in Aromatic Coconut Manufacturing
Corresponding email: numorr@gmail.com
Published at : 16 Oct 2020
Volume : IJtech
Vol 11, No 4 (2020)
DOI : https://doi.org/10.14716/ijtech.v11i4.3897
Deepradit, S., Ongkunaruk, P., Pisuchpen, R., 2020. Tactical Procurement Planning under Uncertainty in Aromatic Coconut Manufacturing. International Journal of Technology. Volume 11(4), pp. 698-709
| Siraprapha Deepradit | Department of Industrial Engineering, Faculty of Engineering, Kasetsart University, 50 Ngam Wong Wan Rd, Lat Yao, Chatuchak, Bangkok 10900, Thailand |
| Pornthipa Ongkunaruk | Department of Agro-Industrial Technology, Faculty of Agro-Industry, Kasetsart University, 50 Ngam Wong Wan Rd, Lat Yao, Chatuchak, Bangkok 10900, Thailand |
| Roongrat Pisuchpen | Department of Industrial Engineering, Faculty of Engineering, Kasetsart University, 50 Ngam Wong Wan Rd, Lat Yao, Chatuchak, Bangkok 10900, Thailand |
This article presents a
tactical procurement plan for an aromatic coconut manufacturer located in
Thailand using a mathematical model. Procurement planning is complex because
many uncertainty factors are dependent upon the delivery schedule and season,
which affect the price of the coconut and the quantity of the order. There were
two alternatives for procurement: farmers and coconut collectors. The model
used in this research study compared four scenarios: three deterministic models
in which every parameter is constant under the worst-case, best-case, and
average-case scenarios and a stochastic model simulation. For the deterministic
models, the mixed-integer linear programming (MILP) was formulated in a
spreadsheet and solved using the Frontline Premium Solver in Excel. In the
second model, a Monte Carlo simulation-based MILP with three random
variables—demand, price, and the number of coconuts—was solved optimally. The
solution indicates how many coconuts will be purchased from farmers and
collectors and which truck will be used to deliver the order to maximize total
annual profit. The results were compared among four scenarios that could help
decision-makers consider the range of profit. The results showed that the stochastic
model resulted in less profits than the deterministic model. In the worst-case
scenario, profit was lost; in the best-case scenario, profit was gained. In the
stochastic model, profits were increasing, except in July. In summary,
procurement planning helps factories and farmers realize the price, supply
quantity, and demand uncertainties and organize to respond optimally. Proper
farm management could increase the productivity of the farm and lessen the
supply shortage, resulting in a higher profit. The findings of this research
could be applied to assist coconut processors’ supply planning efforts;
moreover, proper farm management could increase the farm’s productivity and
lessen the supply shortage.
Agriculture; Aromatic coconut; Mixed integer linear programming; Monte Carlo simulation; Procurement
Aromatic
coconut is a popular Thai export crop in the world market
because its smell and taste are unique. The number of consumers of aromatic
coconut is increasing rapidly due to its health benefits. Although the aromatic
coconuts are ranked ninth in exports worldwide (Pipatkanaporn, 2016), and there is an increasing
trend, supply still cannot satisfy the demand. The supply chain
planning for agricultural products is complex due to
Many
researchers have improved agri-food supply chain management using several
tools, of which mixed-integer linear programming (MILP) is the most popular. For example, Chueprasert et al. (2016) identified
and resolved the optimal raw milk blending problem for a milk collecting center
in Thailand. Ongkunaruk and Ongcunaruk (2015)
proposed using MILP to address the coconut sap pickup problem with time windows
for a coconut sap juice manufacturer. Dems et al.
(2016) studied a wood procurement planning problem in Eastern Canada
using MILP. Limpianchob (2017) integrated
the harvesting and production plan of aromatic coconut using MILP. Kostin et al. (2018) studied how to use MILP to
plan the Brazilian bioethanol supply chains and optimize the net present value
of the entire supply chains for the sugar and bioethanol sectors in Brazil. Vanzetti et al. (2019) used MILP to develop an integrated approach for
bucking planning, such as the number of stems to be harvested and sawmill
production planning, i.e. the used cutting patterns in the forest industry.
In real-world situations, stochastic planning is more appropriate than
deterministic planning because many factors are
uncertain. Govindan and Cheng (2018) studied
the advances in stochastic programming and robust optimization for supply chain
planning, and Jareonkitpoolpol
et al. (2018) determined the optimal blending problem of
organic-chemical fertilizer using Monte Carlo simulation. Aziz et
al. (2018) used Monte Carlo simulation to improve concrete composites for enhanced
X-ray/gamma-ray radiation shielding. Wicaksono et
al. (2019) presented the Monte Carlo simulation of net present value
based on natural gas price uncertainty and production reservoir capacity. Yatmo et al. (2018) analyzed the occurrence
of airborne transmission in healthcare waiting areas. Simulation
is also a useful process improvement tool in agro-industry; for example, Pisuchpen and Ongkunaruk (2016)
simulated the production of a large-sized frozen chicken manufacturer and
proposed a new process for line balancing. Doungpattra
et al. (2012) reduced the cost of pallet transport for a pet food
manufacturer by simulation the process.
The aromatic coconut supply chain of a factory
consists of farmers, harvesters, collectors, manufacturers,
retailers, wholesalers, and domestic consumers and importers for
international trade, as shown in Figure 1. Supply chain management
includes supply chain planning, procurement, transportation management from
harvesting areas to the factory, production, and
transportation for the product, distribution, and customer management.
Procurement planning is an important activity because it addresses the operation costs and helps a factory reduce costs. For aromatic coconut supply chains, procurement sources include harvesting at contracted farms and purchasing from local collectors. Each source has different advantages and disadvantages.
Figure 1 An aromatic coconut supply chain in Thailand
Purchasing costs are lower when coconuts are procured
from contracted farms instead of collectors, but this also
entails transportation and harvesting costs. Although purchasing
from collectors results in the highest purchasing prices, it is essential to do
so when there is high demand and the supply from contract farms is
insufficient. Four-wheel trucks and six-wheel
trucks are used to transport
coconuts from contracted farms. A factory must plan when and how
many coconuts to harvest from contracted farms located near its facility.
Moreover, the factory needs to decide whether to purchase coconuts
from collectors during the peak season. The challenge is that the number of available coconuts from
contracted farms and collectors and the purchasing
prices fluctuate. At present, the harvest plan is made based
on experience, and it is impacted by unbalanced supply and demand. Thus, our
objective is to solve the make-or-buy decision with the truck allocation
problem to maximize profit under conditions of uncertainty.
This
paper extends the existing results from the deterministic model of Kanjanarat and Ongkunaruk (2014) by using the MILP
and the simulation model planning of Deepradit et
al. (2017, 2018) who studied the harvest plans for aromatic coconut
manufacturing. In our study, we used the Monte Carlo simulation to determine
the appropriate harvest age. The model sought the maximum annual profit for
fixed period and varied period constraints. The objective was to improve the
procurement planning methods under uncertainty factors for aromatic coconut
manufacturing in the Ratchaburi Province of Thailand. The solution maximized
the total profit. A deterministic model using the Frontline Premium Solver and
the stochastic model using Oracle Crystal Ball were presented and compared.
At present, factories lack stochastic environment
planning, which does not conform to real-world situations. In this research
study, we proposed an optimal tactical procurement plan for aromatic coconut manufacturing using the MILP
under the uncertainty simulation model. Aromatic coconut manufacturing faces
planning problems due to supply shortages and unbalanced supply and demand. The
procurement plan for aromatic coconuts is complex because it had some
uncertainty factors, such as price, demand, and supply quantity. This study’s findings impact factory decision-makers and helps then manage
uncertainty factors. These factors also significantly impact
farm production planning. From May to July, factories face coconut shortage because the supply is
insufficient. A factory needs to procure from both contracted farms and
collectors. It
should also extend the contracted areas to satisfy demand and
reduce the risk of shortage, which results in a higher
cost. In the future, the stochastic model will use an extended time horizon to
plan procurement. The increasing time horizon will increase the variables,
scenarios, and processing time. Therefore, future research will attempt to
improve stochastic programming by separating it into two-stages or multiple
stages (Sawik, 2017; Dillon et al., 2017; Ioannou
et al., 2019) to solve larger problems and address more complex systems.
The authors would like to thank the case study company
representatives who generously provided us with details about the farmers’
processes and input data.
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| R2-IE-3897-20200625112902.jpg | Figure 1 |
| R2-IE-3897-20200625112916.jpg | Figure 2 |
Andersen, N.J.H., Brandstrup,
J., 2008. Monte Carlo Simulation in
Crystal Ball 7.3. AARHUS University, Denmark: Analytic Group
Aromatic Coconut Price.
Available Online at http://www.taladsimummuang.com, Accessed on December 31, 2019
Aziz, F., Panitra, M., Rivai, A.K., 2018. Synthesis and Monte
Carlo Simulation of Improved Concrete Composites for Enhanced X-Ray/Gamma Ray
Radiation Shielding. International Journal of Technology, Volume 9(4),
pp. 695–706
Bahinipati, B.K., 2014. The Procurement Perspectives of Fruits and Vegetables Supply Chain
Planning. International Journal Supply Chain Management, Volume 3(2),
pp. 111–131
Banks, J., Carson, J.S.,
Nelson, B.C., Nicol, D.M., 2005. Discrete-Event System Simulation. 4th Edition. London:
Pearson Education
Chepkesis, K., Keitany, P., Kiplel, M., 2018. Effect of
Procurement Planning on Suppliers Performance in Public Institutions: A Case of
Moi University. European Journal of Logistics, Purchasing and Supply Chain
Management, Volume 6(2), pp. 1–9
Chueprasert, M., Ongkunaruk, P., Ongcunaruk, W., 2016.
The Study of Business Process and Decision Support of Raw Milk Blending for a
Collecting Centre in Thailand. International Food
Research Journal, Volume 23(3), pp. 1233–1238
Deepradit, S., Ongkunaruk, P., Pisuchpen, R.,
2020. The Study of Forecasting Techniques for Aromatic
Coconut Monthly Prices using Individual and Hierarchical Forecasting. Thai
Journal of Operations Research,
Volume 8(2),
pp. 15–26
Deepradit, S., Pisuchpen, R.,
Ongkunaruk, P., 2017. The Harvest Planning of Aromatic Coconut by using Monte Carlo Simulation. In: The 2017 4th
International Conference on Industrial Engineering and Applications (ICIEA),
Nagoya, Japan, pp. 116–120
Deepradit, S., Pisuchpen, R., Ongkunaruk, P., 2018. The
Harvesting Planning of Aromatic Coconut Manufacturing by Considering
Uncertainty Harvest Age. In: The 2018 AS Conference, Bangkok, Thailand, pp. 193–202
Dems, A., Rousseau, L., Frayret, J., 2016. A Hybrid
Constraint Programming Approach to a Wood Procurement Problem with Bucking
Decisions. Constraints, Volume 21,
pp. 303–317
Dillon, M., Oliveira, F., Abbasi, B., 2017. A Two-stage
Stochastic Programming Model for Inventory Management in the Blood Supply
Chain. International Journal of Production Economics, Volume 187, pp.
27–41
Doungpattra, N., Jarupan, L., Ongkunaruk, P., 2012.
Simulation for Transport Pallet Cost Reduction in Pet Food Manufacturing: An
Empirical Case Study. Packaging Technology and Science, Volume 25(6),
pp. 311–319
Gajasuta, P., Aungsuratana, A., Saridnirun, P., 2017.
Production Capability Enhancemence on Young Coconut Supply Chain for
Exportation in Ratchaburi Provincial Areas. Veridian E-Journal, Volume
10(2), pp. 531–545
Govindan, K., Cheng, T.C.E., 2018. Advances in Stochastic Programming and Robust Optimization for Supply Chain
Planning. Computers & Operations Research, Volume 100, pp. 262–269
Ioannou, A., Fuzuli, G.,
Brennan, F., Yudha, S.W., Angus, A., 2019. Multi-stage Stochastic
Optimization Framework for Power Generation System Planning Integrating Hybrid
Uncertainty Modelling. Energy Economics, Volume 80, pp. 760–776
Jareonkitpoolpol, A., Ongkunaruk, P., Janssens, G.K.,
2018. Determination of the Optimal Blending Problem of Organic-Chemical
Fertilizer under Uncertainty. Soil Use and Management, Volume 34(4), pp.
449–460
Jonkman, J., Barbosa-Póvoa, A.P., Bloemhof, J.M., 2019.
Integrating Harvesting Decisions in the Design of Agro-food Supply Chains. European
Journal of Operational Research, Volume 276(1), pp. 247–258
Kanjanarat, C., Ongkunaruk, P., 2014. Using Integer
Programming Optimization for Aromatic Coconut Supply Planning: A Case Study of
a Thai Manufacturer. In: 2014 International Conference on Economics and
Management (ICEM2014), pp. 560–564
Kostin, A., Macowski, D.H.,
Pietrobelli, J.M.T.A., Guillén-Gosálbez, G., Jiménez, L., Ravagnani, M.A.S.S., 2018. Optimization-based
Approach for Maximizing Profitability of Bioethanol Supply Chain in Brazil. Computers
and Chemical Engineering, Volume 115, pp. 121–132
Limpianchob, C., 2017. Integrated
of Harvesting and Production Planning in Aromatic Coconut Supply Chain using
Mixed-integer Linear Programming. International Journal of
Operational Research, Volume 30(3), pp. 360–374
Miriti, L.K., 2018. Effect of Procurement Planning on
Supply Chain Performance of Kenya Medical Supplies Authority. Journal of
International Business, Innovation and Strategic Management, Volume 1(4),
pp. 1–19
Ogwang, A.O.,
Waweru, L., 2017. Influence
of Procurement Planning on Performance of Kisumu Water and Sewerage Company
Limited, Kenya. International Journal of Economics, Commerce and Management,
Volume 5(5), pp. 767–789
Ongkunaruk, P., Ongcunaruk, W., 2015. Coconut Sap Pickup
Problem with Time Windows: A Case Study. International
Food Research Journal, Volume 22(5), pp. 2088–2092
Onyango, C.J., 2012. Effects of Procurement Planning on Institutional Performance: A Case
Study of Mombasa Law Court. International Journal of
Science and Research (IJSR), Volume 3(358), pp.
2319–7064
Pipatkanaporn, K., 2016. An Overview of Thai Aromatic
Coconut Exporting Industry. Master’s Thesis, Graduate Program, Thammasart
University, Thailand
Pisuchpen, R., Ongkunaruk, P., 2016. Simulation for
Production Line Balancing of a Large-sized Frozen Chicken Manufacturer. Actual
Problems of Economics, Volume 177(3), pp. 397–406
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
Ratchaburi Provincial
Agricultural Extension Office. Available Online at http://www.ratchaburi.doae.go.th,
Accessed on June 17, 2020
Richard, O., Koehler, A., 2005. Forecasting, Time Series, and Regression by Bruce
Bowerman. 4th Edition. USA:
Thomson Brooks Cole
Sawik, T., 2017.
Stochastic versus Deterministic Approach to Coordinated Supply Chain
Scheduling. Mathematical Problems in Engineering, Volume 2017, pp. 1–15
Make Versus Buy a Decision Framework. Available Online at
https://www.academia.edu/28619022/Make_Versus_Buy_A_Decision_Framework,
Accessed on December 25, 2019
Vanzetti, N., Broz, D.,
Montagna, J.M., Corsano, G., 2019. Integrated
Approach for the Bucking and Production Planning Problems in Forest Industry. Computers
& Chemical Engineering, Volume 125, pp. 155–163
Wicaksono, F.D., Arshad, Y.B., Sihombing, H., 2019. Monte
Carlo Net Present Value for Techno-Economic Analysis of Oil and Gas Production
Sharing Contract. International Journal of Technology, Volume 10(4), pp.
829–840
Willy, K., Njeru, A., 2014. Effects of Procurement Planning on Procurement Performance: A Case
Study of Agricultural Development Corporation, Nairobi. International
Journal of Business and Commerce, Volume 3(12), pp.
58–68
Yatmo, Y.A., Putra, N.,
Harahap, M.M.Y., Saginatari, D.P., 2018. Evaluation of Spatial Layout in Health
Care Waiting Areas based on Simulation of Droplet Movement Trace. International
Journal of Technology, Volume 9(5), pp. 888–897