An application of the discrete event simulation for efficient crop production supply chain redesign

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An application of the discrete event simulation for eﬀicient crop production supply chain redesign

Valeria Borodin, Faicel Hnaien, Nacima Labadie, Jean Bourtembourg

To cite this version:

Valeria Borodin, Faicel Hnaien, Nacima Labadie, Jean Bourtembourg. An application of the discrete

event simulation for eﬀicient crop production supply chain redesign. MOSIM 2014, 10ème Conférence

Francophone de Modélisation, Optimisation et Simulation, Nov 2014, Nancy, France. �hal-01166598�

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AN APPLICATION OF THE DISCRETE EVENT SIMULATION FOR EFFICIENT CROP PRODUCTION SUPPLY CHAIN

REDESIGN

V. BORODIN, F. HNAIEN, N. LABADIE J. BOURTEMBOURG

Laboratory of Industrial Systems Optimization (LOSI), Agricultural Cooperative Society Charles Delaunay Institute (ICD), in the Region of Arcis-sur-Aube (SCARA), University of Technology of Troyes (UTT), Industrial Zone of Villette,

12 rue Marie Curie - CS 42060, 10004 Troyes, France 10700, Villette-sur-Aube, France {valeria.borodin, faicel.hnaien, nacima.labadie}@utt.fr j.bourtembourg@scara.fr

ABSTRACT:Given the new challenges confronting agricultural sector, innovative production policies need to be devised and designed at the farms level. Due to its involved high cost, re-configuring experiments are not easy and circumspect to conduct at this level, a beforehand modelling is required for a scrupulous evaluation of multiple impacts of any eventual alternative re-configuration. In this sense, with a focus on crop production supply chain encountered at a typical French agricultural cooperative, this study investigates an alternative storage policy based-on the pooling of growers and cooperative resources and efforts during the harvest season.

By raising awarenesses on deterministic and stochastic components, a discrete event simulation modellings of both current and alternative crop supply chains are presented and confronted for an efficient crop streaming from growing fields to long-term storage facilities.

KEYWORDS: discrete-event simulation, crop production supply chain, storage policy, performance analyse, weather uncertainty

1 INTRODUCTION

To meet the challenges imposed by globalisation, changing market demands as well as prices insta- bility, the supply chain performance has become a pivotal issue within the agricultural sector. Due to global competitive pressures, many agricultural enterprises intend to rethink and redesign their production supply chain configuration for enhancing profitability and market competitiveness.

One of the main goals of agricultural supply chains management resides in holistic and efficient supervi- sion of the crop steering from growing fields to long- term storage facilities, which embeds gathering (col- lection), transporting and storage processes. The conjuncture becomes even more intricate and difficult to be analytically evaluated, due to the highly dynamic behaviour of agricultural supply chain, which embeds uncertainty in reception demand, meteorological conditions, variability in processing time of each operations, grower and customer satisfaction, etc. [4].

In particular, crop production systems are complex in nature, this makes the use of models significant in simulating many agricultural practices and envi- ronmental characteristics [6]. As such, crop produc-

tion simulation models turn out to be sound tools to help agricultural decision makers to propose alternative/innovative crop management systems, to exam- ine the impacts of climate change on crop yield, to assess the risks associated with various management strategies and to assist in decision making processes.

Moreover, these models can serve as complementary tools to in-field experiments and prototyping [6,11].

Motivated by a real-life case study encountered at a typical French agricultural cooperative, the purpose of this paper is to conceive and evaluate an alternative seed crop steering policy from growing fields to long-term storage facilities based on the pooling of growers and cooperative resources (for an eventual mutual gain). By setting up various performance indicators, two crop supply chains, current and alternative configurations, are evaluated and confronted in order to derive a better reconfiguration, improve its performance and reduce overall cost for both individual growers and cooperative.

The structure of the remaining paper is as follows:

the upcoming section is dedicated to review the litera- tures regarding agricultural supply chain and its principal issues. Section 3 delineates the problem statement and the objectives which are addressed in this

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MOSIM14 - November 5-7-2014 - Nancy - France

study. Section 4 provides simulation modellings of the both current and proposed alternative crop supply chains. In Section 5, several computational experiments are conducted and discussed. Finally, Section 6 presents draws conclusions and suggests future research directions.

2 LITERATURE REVIEW

With the growing complexity of the agricultural man- agerial decisions, many research efforts have been commited for tackling different activities in the wider context of the whole supply chain network. As claimed in [16], two main areas of agricultural devoted applications can be distinguished: decision making support at farm-level [2,3,5,8,9,11,12,17] and support to policy decision making, realized as a rule through the simulation of farm reactions to various policy effects [7,13,14,15].

A relevant tool to support farm-level decision makers is provided by [5], which have developed a software library implementing models to simulate various aspects of rice quality: amylase, protein, lipids and starch content, viscosity profile, chalkiness, cracking and head rice yield. Alternate approaches for the simulation of the same quality property are included, to allow users to select the most suitable for specific modelling studies.

Another discrete event simulation model of the harvesting and transportation systems has been proposed in [3], for a sugar-cane plantation in Mexico that covers all processes from the burning of the cane to its unloading in the mill yard. More precisely, the model has built to solve a problem with the amorti- zation of machinery used in the plantation. As far as the sugar-cane industry is concerned, in [9] a simulation model for capacity planning has been developed as part of a whole-of-system modelling framework to assess scenarios for cost reductions in harvesting and transport activities. On the other hand, [10] has coupled a tactical supply planning model with a daily logistics model in order to explore the relationships between these two supply components in a more holistic manner.

Uncertainty is always a worsening factor in any decision support models [11]. Under various inherent sources of uncertainty, [12] has presented a simulation based methodology for analysing a production scheduling problem commonly found in many industrial processes that use agricultural products as raw materials. More broadly, the authors of [17] have presented an accelerated scenario updating heuristic to solve a real-life large-scale stochastic mixed- integer model, which corresponds to multi-period, multi-product production planning in sawmills with random yield and demand.

In the work [2], an operational model has been designed for providing decisions for harvesting, packing and distribution of crops with the view to maximiz- ing the revenues of the growers of perishable agricultural products. The proposed model in [2] integrates labour availability, price dynamics, the weather variable effects and plant agronomy through different functions and approximations. In order to deal with planning of field operations problem, in [8] an object oriented simulation has been developed, to monitor in detail in-field machine activities during the execu- tion phase. For more details about approaches and tools used to support farm decision makers, one can refer to the survey [1]. This paper has been reviewed the recent studies and outlined the main contribu- tions related to production and distribution planning models for different commodities of agricultural supply chains.

On the other hand, the area of policy support has attracted less attention in recent years. However, relevant studies exist, e.g. the authors of [14] have taken the beef sector as a representative of a food supply chain that has both food and environment related challenges and have developed a multi-objective linear programming model for a generic beef logistics network problem.

The authors of [15] proposed a new integrated approach towards with logistics, sustainability and food quality analysis. This study considered food quality models and sustainability issues embedded within logistics processes in discrete event simulation models. A case study concerning the import of pineapples from Ghana to the Netherlands is illustrated in order to improve the integrated decision making simulation model.

[13] have identified risk-efficient cropping strategies that allocate land and water between crop enterprises for a case study of an irrigated farm in Southern Queensland (Australia). In order to achieve this, the authors applied stochastic frontier analysis to the output of a simulation experiment which involved changes to the levels of business risk by systemati- cally varying the crop sowing rules in a bio-economic model.

More generally, in [7] the authors have analysed the case of a complex supply network in the agri-food sector and followed the research to answer the question why complex networks do not exploit the potentials from cross enterprise supply chain management.

3 PROBLEM STATEMENT

Let us consider an agricultural cooperative specialized in seed and cereal products that integrates and offers different customer services and storage provisions for

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a number of growers who have one or several parcels to gather.

Once the cereals have reached their ripeness, growers carry out to gather seed crops. In order to be more appropriately preserved, the grains are carefully stored in storage facilities (also named silos), specially designed for this purpose.

On its part, the cooperative must ensure the reception availability of each silo and the preservation of har- vested crop. For these purposes, many cooperatives use two types of silos: satellite and expedition ones.

The expedition silos are used for a long time period storage in order to ensure the intrinsic seed quality.

The satellite ones, respectively, serve as proximity facilities at time of harvest, which are then delivered to expedition silos.

The activities required to supply crop products from its parcel (i.e. growing fields) to a long-term storage facility are the following:

• at grower level: harvesting, in field handling, transport to an expedition or satellite silo;

• at cooperative level: crop transfer from satellite silos to expedition ones.

Due to their high sensitivity to humidity conditions, cereal harvest is possible only on days without rain.

Moreover, delayed harvest increases the risk of yield and quality degradation. Consequently, crop harvesting must be completed as soon as possible, and cereals must be maintained and adequately stored.

In order to enhance crop supply chain performance and reduce the overall costs, the aim of this paper is to model and evaluate the direct crop streaming from parcels to expedition silos by pooling growers and cooperative resources. This alternative crop supply policy can lead to an improvement of overall profitability of both agricultural cooperative and growers in particular, by federating their efforts and resources for an eventual mutual gain.

4 SIMULATION MODELS DESIGN

In this section, we present the simulation-based modellings of the current crop supply chain, as well as, the alternative mutualised crop supply chain. The crop supply chain can be modelled by the following types of data:

• parcel, theirs grain varieties and ripeness dates;

• equipment and machinery fleet composition of each individual grower (or if so, grower associ- ation);

• distances between parcels and storage facilities (or geographical information system related to case study territorial organizations);

• characteristics of each storage facility: localisa- tion, number of receiving pits and its associated flow rates;

• cooperative truck fleet;

• schedule of opening time of farmers reception of each storage facility;

• daily harvest availability discrete distribution in terms of weather conditions.

For a detailed harvest operations description under stochastic conditions, refer the work of [4]. The authors of [4] have presented an application of stochastic discrete-event simulation modelling for harvesting, transportation and storage activities of one typical cereal cooperative.

4.1 Current crop supply chain configuration The current crop supply chain configuration is depicted in the figure 1.

Figure 1: Current crop supply chain

Therefore, current supply activities (including harvesting, transportation, transfer and storage operations) are formally illustrated in the figures 2 and 3. More specifically, figure 2 shows the harvesting and crop transportation from the growing fields to the nearest silos. All of these operations are made once the cereals have reached their biological maturity and when the meteorological conditions became favourable to gathering.

Decisions related to storage and control of inventory level of buffer silos, represent one of the challenges in supply chain management. The aim of inventory control is to find the trade-off between conflicting goals, such as: to provide a high level reception service to customer at all silos, to have a down stock level of satellite storage facilities and not to perturb reception process of expedition silos. Hence, during whole harvest season, the inventory control level and crop transfer operation of each satellite silo is designed in the figure 3. If there are some level of stocks, crop transfer is triggered from satellite silos to expedition

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Figure 2: Simulation modelling flowchart of current crop supply chain: harvesting and crop delivery from growing fields to storage facility

Figure 3: Simulation modelling flowchart of current crop supply chain: satellite silo inventory control and transfer operation to expedition storage facility

ones, in order to maintain buffer silos empty and, also, to guarantee a high reception service and appropriate

cereal crop storage. The sub-model illustrated in the figure 3 corresponds to each satellite silo.

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4.2 Alternative crop supply chain configuration

The current configuration of the crop supply chain, where growers can improve a continuous process of harvesting, can be enhanced by organizing a buffer crop storage (work-in-process) directly in the growing fields, as depicted in the figure 4. By federating cooperative and growers efforts and resources, this alternative crop supply policy presents the following advantages:

• synchronization between in-field agricultural activities and silo reception area;

• continuous process of harvesting without daily combine harvester breaks, which are considered as the most onerous growers expenditure. Note that, for the sake of labour legislation requirements, silo working time is as a rule limited to 8 hours per day.

• satellite silos closure, which will bring significant gains to the both cooperative and individual growers.

Figure 4: Alternative crop supply chain Before proceeding to the simulation modellingper se, let us consider moreover the following assumptions:

• no limited in-field storage capacity is imposed.

Often, growers agricultural equipment and machinery fleets are over-dimensioned, that allows him to organize a work-in-process storage directly in their supplementary trucks at head- lands.

• no in-field crop handling techniques are discussed in this paper.

• no in-field crop security and protection storage against eventual rainfall requirements are imposed. We anticipate and prohibit any crop storage during the rainy days, by taking into account meteorological aspects.

By pooling agricultural resources and federating their efforts during the harvest season, crop supply chain can pass to a more industrial approach, where in-field agricultural and silo reception area activities are syn- chronized. As shown in the figure 5, no more satellite

silos are used. In this way, by sharing mutual economical gains, the alternative crop supply chain presents benefits for both agricultural cooperative and individual growers.

4.3 Crop supply chain performance measure- ment

In order to evaluate and investigate the crop supply chain for different system configurations, a number of performance metrics are set up. Thus, let us define the following relevant performance responses (also called attributes, indicators or metrics) of the studied crop production system:

• harvest season length;

• risk of crop quality degradation, expressed by the sum of gaps between the optimal physiological maturity and harvest dates;

• total operating cost of silos exploitation;

• rate of growers agricultural equipment utilisa- tion;

• etc.

Because the total operating cost is rather significant compared to the cost involved by transfer and logistics operations between satellite and expedition silos, the impact evaluation of closing/opening of one or more satellite silo(s) contributes to supply chain improvement and redesign. Moreover, through the prism of these performance indicators, an analytical view of the system re-configuration(s) and its changing over time can be obtained.

5 COMPUTATIONAL EXPERIMENTS Interest of this paper was raised by an Agricultural Cooperative Society, situated in the region of Arcis sur Aube (France). More specifically, this is a grain and oilseed agricultural cooperative, for which efficient crop production management is crucial since: on the one hand, it permits to preserve the grain yield and quality in order to sell profitably cereal, and on the other hand, it is able to propose a high level growers reception area service.

The simulation models presented in section 4, have been implemented by using simulation language SIMAN in the Arena Rockwell Automation environment (version 13.0) for instances with over 4000 parcels (geographically dispersed on more than 110 communes), 3 expedition silos and 11 buffer storage facilities. The computational experiments have been performed on an Intel(R) Core(TM) i7-2720QM CPU 2.20GHz workstation.

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Figure 5: Simulation modelling flowchart of alternative crop supply chain: harvesting and crop delivery from growing fields to storage facility

Figure 6: Computational results: performance analysis

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Figure 7: Computational results: silos reception quantities in the actual supply chain configuration

Typically, the harvest lasts about one month and a half. During this period, the climate forecasting data

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is provided by the meteorological stations, with an ac- ceptable reliability level. The information concerning variety ripeness days is determined by the technical competent organisations on agronomy and in particular, on crop agriculture. A simulation model was tested on the whole crop harvest, which means while the entire cereal raw material are gathered and prop- erly stored.

Without loss of generality and for the sake of confi- dentiality, let us discussed the results obtained for a reduced-size of case study data (1 expedition silo, 3 satellite silos, thereabouts 1500 parcels). Time of run is considered to simulate the discontinuous harvesting operations, by imposing week-ends and closing time schedules in the growers reception program of silos.

The figure 7 reports the crop quantity stock level evo- lution during the harvest season in the actual crop supply chain configuration. Such a reports offer sta- tistical results about daily crop storage.

Next, let us discussed the figure 7, which provides a spectrum of performance metrics of both actual and alternative crop supply chains. As the graphics depicted in the figure 7 highlight, directly crop steering policy involves various economical and crop quality benefits.

However,ex-anteimplemented, the proposed alternative crop supply chain must be made more effective and market competitive. On the other hand, we ar- gue that alternative crop supply chain redesign based on efforts and resources pooling deserves to be deeply studied for an eventual successful practical implemen- tation.

6 CONCLUSIONS AND PERSPECTIVES This paper addressed and discussed an alternative crop steering policy during the harvest season. As computational results pointed put, by pooling their agricultural resources and federating their efforts, the alternative crop supply chain presents benefits for both cooperative and individual growers.

However, this study presents a number of limitations, such as: (i) in-field crop handling techniques are not tackled; (ii) the relationship between crop quality degradation and in-field storage capacity are not discussed; (iii) in-field storage security and protection against eventual rainfalls are not treated, etc. All these represent principal future research directions of this study. Moreover, future work will be also dedicated to develop a multi-criteria optimization model, able to deal with crop quality degradation on the one hand and storage control, on the other hand, whilst considering meteorological uncertainty.

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