This section covers a literature review on multi-mode decision making in agricultural supply chain. The discussion in this sections gets started with definition of important terms that are commonly used in agricultural supply chain. It follows the factors, which lead to decision factor. Thereafter, the literature review section thus investigates the application of multi-mode decision making in agricultural supply chain. It is followed by investigating the research works, which have been conducted in term of agriculture mode. At the end, the literature review section identifies the gap.
Agro-Ecosystem: Agro-ecosystem, in general, can be referred to the ecosystem surrounding the lands being used for farming. Farming is an important because it may affect the entire food industry if not being able to produce the expected quantity of foods. On a different note, a surplus of production may create enough food resource to local people and wealth to the national economy by means of imports and exports. Therefore, agro-ecosystem must be maintained to avoid crops from getting damaged from kinds of natural calamities like flood, or drought (Pressler et al. 2017).
Agricultural Supply Chain: Supply chain is itself a major topic, which cannot be covered within just a unit. However, to be very specific, agricultural supply chain can be defined as a network of operations, which is responsible for carrying foods to its end-users. Farmers play a significant role in an agricultural supply chain. They are the ones who take care of the farming. If they are not supplied with adequate resources they won’t be able to justify their farming capabilities. Moreover, warehouses will not be able to fulfill the demands of food (Tian 2016).
Multi-mode Decision-Making: In general, this can be defined to be as related with decision-making for several related works of a supply chain. This goes similar to the agricultural supply chain. An agricultural supply chain consists of numerous related activities where strategic decision-making is required. Such decisions may also be related with a scientific understanding of the issue. Since an agricultural supply chain has an impact of environmental behaviours, and of farmers’ co-operation, it is actually vulnerable to a number of threats (Lam et al. 2015).
The importance of multi-mode decision making can be traced in challenges that it faces while not being able to support the range of processes with effective decisions. Effective decision-making requires strategic thinkers to construct and show ways to implementing it. However, those strategic thinkers lack an effective co-operation from the stakeholders. The fact does not just create a communication gap between the thinkers and stakeholders but, considerably delays or even postpones the implementation process. It, therefore, can be said that multi-mode decision making is an essential part of the agricultural supply chain, which if not conducted appropriately, will affect the productivity. Strategies have failed in past such as the “Contract Farming” (Minot and Sawyer 2016). Hence, this is important that a few challenges of agricultural supply chain is effectively sorted out by strategic thinkers and the stakeholders. Multi-mode decision making is required in some issues such as those being discussed in this section.
Delivery of commodities is one of the issues. Commodities are delivered to warehouses by various means of transportation such as by truck or train. In most cases, trucks are used to deliver the food materials to concerned warehouses. The process appears simpler; however, it is not so. Trucks have to face a range of issues at sites. They are required to be in queues before they could reach to the lading junction. Such sites will be tested here for their efficiency in clearing trucks in a less span of time. Hence, sites with less efficiency will lose the business to those that bear more efficiency. This will put more pressure on the gainers as the average handling of trucks would then increase. As obtained from Jones et al. (2017), an increased workload will affect the work efficiency until and unless being managed through increased efficiency to perform. An increased efficiency would, therefore, need an increased area of operation supported by an increased number of labours. However, this is not as easier as it appears. Moreover, delivery of commodities are in the hands of an unstable transport system.
Need of temporary workers is another issue. During peak harvest times, some temporary workers are required to support the increased work pressure. Sites where crops are grown generally require additional resource for an extended period of 3-4 months. Some of these workers may be new to this industry. With these new workers, sites are explored to safety and training issues. Both safety and training will put additional cost burden on such sites. Hence, there will be the need to work towards ensuring the required inflow of money. As opined by Schut et al. (2016), huge spending is being made nowadays on the training. In addition, these site owners will face the challenge of creating a safe working environment. According to Schut et al. (2015), farming is not a place to experiment when one does not know the science of farming. One can contribute to it when they only have an appropriate knowledge of the science of farming. Indeed, the risk can be minimized if the needs to have temporary workers could somehow be reduced.
Increased production needs is the other challenge that establishes the needs to have a multi-mode decision-making. With increasingly growing population, the demand for foods has also increased. There is no other way than to produce more. However, this is itself challenging considering the changing climate near and surrounding the agro-ecosystem. The changing climate has a significant impact on farming. The most hardly affected crops include wheat, rice, corn etc. There are certain parameters, which need to be met to take care of the entire farming process. Farming requires adequate supply of water, which may vary depending on the kind of crops and the geographic regions. These are a few parameters of farming; however, even if these parameters are not met, productivity will get hampered. It is, therefore, a challenge for strategic thinkers, and the stakeholders to be able to make useful decisions and also implement those to produce the outcomes. An effective coordination with farmers has always been an issue to the agricultural supply chain. As stated by Notenbaert et al. (2017), there is evident gap between farmers and the stakeholders. Their understanding have rarely collaborated. Actually, stakeholders have never been able to reach to the root of the farming process. Until and unless there is no collaboration between stakeholders and farmers, the increasing demands for foods will largely remain unmet. As obtained from Brandenburg et al. (2014), site operators must also find ways to reduce costs and become automated.
There are several factors that affect the decision factors. If these factors are effectively managed, there won’t be any decision failure. Some of the factors affecting the decision factors are decision timing, decision priority, and decision follow-up.
Decision timing is one of the factors, which affect the decision making. It says that decisions should be made appropriate to the situation. For example, sales have declined and marketing efficiency have been identified as its potential causes. Despite making any improvement to its marketing efficiency, the operations manager focusses on including more products into the product portfolio. It is just waste of time and resources. It can also be a loss of money, which is subjected to sales. Instead, marketing strategies should be focused on. This could have made a sense.
Prioritizing the decision is another factor that affects the decision-making. It says that managers or other concerned person should bear capabilities to prioritize between decisions. It means that two or more decisions at times must be selected on priority basis. For example, a company faces the declining sales. In addition, the rate of employee turnover is also high. The selection of decision-making, therefore, must be understood. Here, the manager will need to address both of issues because they cannot just afford to lose people or they cannot also afford any more declining in sales. The best decision would be to put an enquiry on identifying the reasons for declining sales and also addressing the employee attrition with training and development programs.
Follow-up is the next most important step, which is largely missed during the decision-implementing stage. For example, if a team is being set to identify the root causes of declining sales, their progress should be monitored regularly to identify whether everything is fine or the team needs some kind of an assistance. On a similar note, if there are arrangements being made to train the selected list of people, it is then important to know how all of them have performed in the training. It is required to supply necessary changes to the training if required or to give feedback to those who could not utilize the resources.
(Lian, Yen and Wang 2014)
These are a few factors that also have its existence in the agricultural supply chain. In addition, there are some more factors that affect decision-making in agricultural supply chain.
An inefficient use of fertilizer is one of the factors affecting the decision-making in farming. Chemical fertilizers are extremely important to facilitate a mass production. However, the needs are rarely met. It happens may be because many farmers are not well educated or that they have no access to such fertilizers for any reason. If fertilizers are not being used, this could be dangerous for both the production and the quality of crops. As stated by Smith and Siciliano (2015), chemical fertilizers can essentially speed up the production and can also boost the quantity. No such steps have so far been taken to address this issue on a larger platform. Farmers, in particular, those living in extreme rural locations have to travel quite a mile to reach to retailers and purchase the chemical fertilizer. On the other hand, farming industry misses an effective co-operation between farmers who work at the lowermost levels and the stakeholders of agricultural industry. There is no such system to address this issue. Contract farming is there; however, there is no such contractors to take care of the science of farming as it used to be at construction sites. This is perhaps one of the potential causes of why there are inadequate supplies of food materials on significant occasions.
Influencing factors span from political parties to the farm households. It highlights an issue that influencing factors are just made to apply on farmers’ households’ behaviour. Farmers are instructed on related activities of farming needs; however, their issues are left unaddressed. Political parties in most cases are not aware of the difficulty level, which is associated to farmers. Farmers’ complaints remain mostly unaddressed. According to Mylan et al. (2015), many farmers cannot bear the expenses being required to buy the latest and upgraded machines to irrigate the land and to run water into them. They are left to be dependent on the surrounding climate, which is itself getting increasingly disturbed from the increased air pollution. Hence, there is no rain during the seasonal time. In addition, the amount of rain being calculated in (mm.) has also decreased due to the global warming. It all adds more challenges to the agricultural supply chain. The national governments and political parties have the budgets to show every year but they rarely address their work on such a bigger platform. These environmental issues are addressed and policies are made as well. However, these policies lack a robust implementation and an appropriate follow up by its stakeholders.
Some measures could be adapted to support farmers more effectively. These measures include, but is not limited to producer incentives, and supply chain modernization. Producer incentives as according to Rueda, Garrett and Lambin (2017) can be a good approach to motivate farmers. Farmers especially those who harvest staple crops like wheat, rice, and corn should, in particular, get these incentives as they hardly make any significant profit from harvesting these crops. They even lose their entire spending when they are hit by natural calamities like flood. For example, an excessive water is harmful for rice. However, it happens sometimes that a heavy rainfall or a flood has destroyed the entire rice crops. Now, the remains of the affected rice crops is of no importance to farmers. This one incident take their charms away from them. They have to lose their entire spending as well the crops. There is now no chance to gain back the invested money. They neither have the crops, which they could use it for their household purpose. Issues like these need to be addressed as well. Incentives is a good solution for meeting the set criteria of harvesting but these circumstances should also be met. If these issues are addressed seriously and effectively, farmers will have motivation to work every single year. Notably, motivation is key to an effective workforce (Rueda, Garrett and Lambin 2017). Incentives along with compensations for natural hit cases can help to improve the farmers’ attitude towards working and hence, productivity will also improve provided that environmental issues are controlled.
Supply chain modernization is a growing discussion, which can also affect the multi-mode decision-making. According to Dubey, Gunasekaran and Ali (2015), supply chain visibility and efficiency is an essential part of maintaining a profitable agricultural business. The modernization thus required can be brought to the agricultural supply chain with Internet of Things (IoT). IoT can help to optimize the most complex part of the agricultural supply chain. It is to be noted that every single stage of an agricultural supply chain is highly vulnerable to potential securities. Agricultural products like vegetables, fruits, dairy, and meats are significantly impacted by delays. These agricultural products need certain temperatures to remain afresh. The agricultural supply chain comprises of several stages traveling over which products are delivered to its end users. If stakeholders could work together, they will be aware of each other’s’ strengths and weaknesses. Moreover, IoT could then be deployed at each level to make it more automated. According to Dubey, Gunasekaran and Ali (2015), Internet of Things (IoT) can be advantageous for supply chain management. With IoT, there will be enhanced communication between farmers, suppliers, stakeholders, and the end users. Data could be generated and accessed in real-time. An increased access to data, therefore, would help to know the demands and supplies. Moreover, farmers will be aware of the demands that they are supposed to fulfill. Additionally, expected climatic behaviour could be known to the farmers and stakeholders. Hence, stakeholders would be in a position to do their best to protect the crops from natural calamities.
However, implementation of IoT on the agricultural supply chain could be affected from several factors. Many farmers are illiterate. It is, therefore, of no use to educate these farmers on IoT who even do not know the basics of a technology. They cannot be trained as well because they do not possess any resemblance to technology and hence, would feel loosely connected to it. There is very limited scope in this part of an agricultural supply chain.
Agricultural systems science is a field that studies the complex and challenging behaviour of agricultural systems. It does so with the help of a model that quantitatively guides to understanding the complexity of agricultural science. It encourages to understand the land used for farming and its surrounding ecosystem to help farmers with more proven facts. Natural calamities are string barriers to farming. Farmers have no clues on how to deal with the issue. However, scientific modeling could be helpful in a gradual understanding of farming and its influencing factors. As opined by Jones et al. (2017), understanding the scientific behaviour of land and agro-ecosystem can be made possible through advancing in scientific modeling in an agricultural supply chain.
Scientific modeling of an agricultural ecosystem has become a subject of increased interest for variety of groups across private and public institutions. Academic scientists, private and public firms, stakeholders, and national government are all seem like being united to one common platform with their contribution to it. They are united to create a next generation, which will be equipped with a scientific understanding of farming. However, as pointed by Jones et al. (2017), such agricultural systems modeling could be tested for many reasons such as those listed below:
Capitalise on Crises: The historical data shows that scientific advancements occurred only when there were crises such as food security concerns. It disappeared soon after the crises got over. According to other studies, major developments occur during only the major disaster.
Technological Advancements: A technological transition should be conducted by those who are responsible for developing the next generation. For example, mainframe computers, the internet, and the PC were embraced by those who were involved in the advancement process. In a similar way, the transition of agricultural systems models should be taken care by those who are its experts.
Collaboration: Major advances have occurred only when different forces have joined together. In a similar way, there is a need to have a collaboration between different stakeholders of agricultural supply chain. If this could happen, days are not far away when production rate will rapidly increase. Notably, stakeholders in agricultural supply chain had struggled in past to work collaboratively with the farmers.
The one gap that repeatedly gets coming throughout this literature review is the lack of an effective collaboration between stakeholders and farmers. Similar problems have continued for a long time. Number of proposals were also being made. However, implementation had lacked a follow-up process. There were no significant changes being done to this part. The study did not find any evidence regarding a collaborative work between the farmers and stakeholders. So many strategies were being made in different regions across the globe. However, those strategies had lacked an appropriate implementation.
References:
Brandenburg, M., Govindan, K., Sarkis, J. and Seuring, S., 2014. Quantitative models for sustainable supply chain management: Developments and directions. European journal of operational research, 233(2), pp.299-312.
Dubey, R., Gunasekaran, A. and Ali, S.S., 2015. Exploring the relationship between leadership, operational practices, institutional pressures and environmental performance: A framework for green supply chain. International Journal of Production Economics, 160, pp.120-132.
Jones, J.W., Antle, J.M., Basso, B., Boote, K.J., Conant, R.T., Foster, I., Godfray, H.C.J., Herrero, M., Howitt, R.E., Janssen, S. and Keating, B.A., 2017. Brief history of agricultural systems modeling. Agricultural systems, 155, pp.240-254.
Jones, J.W., Antle, J.M., Basso, B., Boote, K.J., Conant, R.T., Foster, I., Godfray, H.C.J., Herrero, M., Howitt, R.E., Janssen, S. and Keating, B.A., 2017. Toward a new generation of agricultural system data, models, and knowledge products: State of agricultural systems science. Agricultural systems, 155, pp.269-288.
Lam, C.P., Yang, A.Y., Driggs-Campbell, K., Bajcsy, R. and Sastry, S.S., 2015, September. Improving human-in-the-loop decision making in multi-mode driver assistance systems using hidden mode stochastic hybrid systems. In Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on (pp. 5776-5783). IEEE.
Lian, J.W., Yen, D.C. and Wang, Y.T., 2014. An exploratory study to understand the critical factors affecting the decision to adopt cloud computing in Taiwan hospital. International Journal of Information Management, 34(1), pp.28-36.
Minot, N. and Sawyer, B., 2016. Contract farming in developing countries: Theory, practice, and policy implications. Innovation for Inclusive Value Chain Development: Successes and Challenges, International Food Policy Research Institute, Washington, DC, pp.127-155.
Mylan, J., Geels, F.W., Gee, S., McMeekin, A. and Foster, C., 2015. Eco-innovation and retailers in milk, beef and bread chains: enriching environmental supply chain management with insights from innovation studies. Journal of Cleaner Production, 107, pp.20-30.
Notenbaert, A., Pfeifer, C., Silvestri, S. and Herrero, M., 2017. Targeting, out-scaling and prioritising climate-smart interventions in agricultural systems: Lessons from applying a generic framework to the livestock sector in sub-Saharan Africa. Agricultural systems, 151, pp.153-162.
Pressler, Y., Foster, E.J., Moore, J.C. and Cotrufo, M.F., 2017. Coupled biochar amendment and limited irrigation strategies do not affect a degraded soil food web in a maize agroecosystem, compared to the native grassland. Gcb Bioenergy, 9(8), pp.1344-1355.
Rueda, X., Garrett, R.D. and Lambin, E.F., 2017. Corporate investments in supply chain sustainability: Selecting instruments in the agri-food industry. Journal of cleaner production, 142, pp.2480-2492.
Schut, M., Rodenburg, J., Klerkx, L., Kayeke, J., van Ast, A. and Bastiaans, L., 2015. RAAIS: Rapid Appraisal of Agricultural Innovation Systems (Part II). Integrated analysis of parasitic weed problems in rice in Tanzania. Agricultural Systems, 132, pp.12-24.
Schut, M., van Asten, P., Okafor, C., Hicintuka, C., Mapatano, S., Nabahungu, N.L., Kagabo, D., Muchunguzi, P., Njukwe, E., Dontsop-Nguezet, P.M. and Sartas, M., 2016. Sustainable intensification of agricultural systems in the Central African Highlands: The need for institutional innovation. Agricultural Systems, 145, pp.165-176.
Smith, L.E.D. and Siciliano, G., 2015. A comprehensive review of constraints to improved management of fertilizers in China and mitigation of diffuse water pollution from agriculture. Agriculture, Ecosystems & Environment, 209, pp.15-25.
Tian, F., 2016, June. An agri-food supply chain traceability system for China based on RFID & blockchain technology. In Service Systems and Service Management (ICSSSM), 2016 13th International Conference on (pp. 1-6). IEEE.
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