Project Selection in Agribusiness after covid-19 Pandemic

Project Selection in Agribusiness after covid-19 Pandemic
Project Selection in Agribusiness after covid-19 Pandemic

Project Selection in Agribusiness after covid-19 Pandemic

The method of selecting a project or series of projects for the company to execute is known as project selection. Project Selection in Agribusiness after covid-19 Pandemic has significant importance.  Since projects, in general, necessitate a significant financial and resource commitment, both of which are limited, it is critical that the projects chosen by a company have a good return on the resources and capital invested. This demand must be balanced against the need for an organization to progress and develop.
Because of the high degree of complexity in today’s business climate, project management has become critical to an organization’s long-term survival, with the difference between selecting successful projects and bad projects practically indicating the difference between organizational life and death.

More complicated decisions usually necessitate more sophisticated models because a good model must capture every crucial aspect of the decision. “Every complex problem has a straightforward solution; sadly, it is incorrect.” Designers of tools face a significant challenge as a result of this fact. Project decisions are always high-stakes, dynamic decisions involving complex technical issues—exactly the types of decisions that are hardest to model:

  • Because of their strategic consequences, project selection decisions are high-stakes. The projects that a company chooses will influence the goods it sells, the work it does, and the business path it takes. Customers, contractors, partners, regulators, and shareholders, for example, will all be affected by project decisions. To capture strategic consequences, a sophisticated model may be required.
  • Project decisions are fluid because they can span many budgeting periods, with several opportunities to slow, intensify, re-scale, or terminate the project.

A successful project can also result in the creation of new assets or goods that generate time-varying financial returns and other effects over a long period of time. To account for dynamic effects, a more sophisticated model is needed.

  • Project decisions usually have a wide range of effects on the company. A project could, for example, increase revenue or lower potential costs. It may have an effect on how consumers or investors view the business. It could give you new skills or knowledge that will help you succeed in the future. Making good decisions necessitates not only calculating the financial return on investment but also comprehending all of the aspects in which projects add value. To account for all of the various types of possible impacts that project selection decisions can have, a more sophisticated model is needed.

Project Decisions:

Project decisions are often fraught with risk and ambiguity. The significance of project risk is determined by the essence of the risk as well as the organization’s other risks. To properly deal with risk and uncertainty, a more sophisticated model is needed. Project selection is the process of reviewing individual projects or groups of projects and then deciding which ones to execute in order to meet the parent organization’s goals. This same systemic method can be extended to every aspect of a company’s operations where decisions must be taken between competing options.

  • A manufacturing company, for example, may use evaluation/selection techniques to determine which machine to use in a part-fabrication process.
  • In its 7:30 p.m. weekday time slot, a television station can choose from a number of syndicated variety shows to rerun.
  • A hospital will find the right combination of psychological, orthopedic, obstetric, and other beds for a new wing by selecting the best subset of a wide group of possible projects on which to bid.

Each project’s costs, benefits, and risks can vary. These are only identified with certainty in a few cases. It’s difficult to choose one project out of a series because there are so many variations. Choosing a portfolio of programs, on the other hand, is much more difficult. In the parts that follow, we’ll go through a few strategies that can be used to assist senior managers in project selection. Plan selection is only one of the choices that must be made while managing a project.

We use decision-aiding models to solve both of these issues. We need such models because they abstract the important aspects of a problem from the multitude of details in which it is engrossed. To deal with reality in its entirety is just too difficult. A dilemma requires a “idealist” to remove almost all of the truth from it, leaving only the aspects of the “actual” situation in which he or she wishes to deal. Modeling the problem is the method of removing unnecessary truth from the bones of a problem. A model is the idealized version of the problem that emerges. The problem’s structure, or shape, is represented by the model. Every problem has a structure, even though we don’t always understand it well enough to explain it. In this book, we’ll use a variety of models, including graphs, analogies, and diagrams, as well as flow graph and network models to aid in scheduling, and symbolic (mathematical) models for a variety of purposes. Models can range in complexity from simple to extremely complex. In general, incorporating more reality into a model makes it more difficult to exploit. When the input data for a model is unknown, we often use probabilistic information; the model is then defined as stochastic rather than deterministic. Again, stochastic models are more difficult to control in general. We are living in the midst of a phenomenon known as the “intelligence explosion.” “90 percent of all we know about science has been discovered since Albert Einstein published his initial work on special relativity,” and “80 percent of what we know about the human body has been discovered in the last 50 years,” are common statements. Furthermore, data is cited to demonstrate that understanding is expanding exponentially.

Such comments stress the significance of change management. Firms must establish techniques for evaluating and reassessing their resource usage in order to thrive. Any resource allocation is a long-term investment. Many of these investments are in ventures due to the complexity of most strategies. Special visual effects achieved by computer animation are popular in the movies and television shows we watch on a daily basis, to name only one example of many. They were unheard of just a few years ago. When the capability was still in the concept stage, computer companies, as well as film and television production companies, had to decide whether or not to invest in the advancement of these techniques. When an entertainment company compared investing in computer animation to investing in a new star, a new rock group, or a new theme park a decade ago, the option was not as simple. The proper selection of investment projects is critical to a company’s long-term survival. We see the outcomes of both good and poor investment decisions on a daily basis. We read in our daily newspapers about Cisco System’s decision to buy companies that have developed useful communication network tools instead of developing its own.

We learned about Procter & Gamble’s decision to invest heavily in Internet marketing; British Airways’ decision to buy passenger planes from Airbus rather than Boeing; and issues faced by school districts when updating student computer labs, such as whether to invest in Windows-based systems or continue with their conventional option, Apple®. But, can those crucial decisions be taken logically? Do they ever alter after they’ve been created, and if so, how? The need for efficient selection models is reflected in these questions. Such models can be used to maximize revenues, select investments with limited capital resources, or boost an organization’s competitive position within the limits of its capabilities. They can be used for ongoing assessment as well as the initial selection, making them an important tool for allocating and reallocating the organization’s limited resources.

Modeling:

A model is an entity or idea that seeks to represent some aspects of reality. Models may be used for a variety of purposes, including testing ideas, teaching or explaining new concepts to others, or simply as decorations. Since there are so many uses for models, it’s difficult to come up with a term that is both simple and encompasses all of them. The following concept is useful in the sense of project selection:

“A model is an outward expression of our perception of reality. It’s a visual illustration of the important aspects of the decision that we’re considering. It reflects the decision field by structuring and formalizing the knowledge we have about the decision and, as a result, presents reality in a condensed and structured manner. As a result, a paradigm provides us with a simplified version of a more complex reality”. Cooke and Slack (Cooke and Slack, 1991)When viewed in this light, it is apparent that the need for project selection models stems from the fact that it is difficult to understand the entire world in which a project would be implemented. As a result, the task of developing a successful project selection model is evident. It must strike a balance between the need to retain enough knowledge from the real world to make an informed decision and the need to simplify the situation enough to reach a decision in a reasonable amount of time.

Criteria for Choosing a Project Model:

According to Souder (1973), the following criteria are most relevant when a company selects a project selection model:

Realism:

The model should represent the reality of the manager’s decision-making situation, including the firm’s and managers’ various goals. Without a standard unit of measurement, It is difficult to compare various projects directly using this method. Project A, for example, may increase a company’s market share by expanding its facilities. Project B, on the other hand, could be able to increase its competitiveness by bolstering its technological capabilities. Personnel which is better, assuming all other factors are equal? The model should account for the firm’s constraints in terms of facilities, money, and staff, among other things. The model should also account for project risks, such as technical risks related to efficiency, cost, and timeliness, as well as market risks such as customer rejection and other implementation risks.

Capability:

The model should be capable of dealing with different time intervals, simulating various internal and external scenarios (for example, strikes, interest rate changes), and optimizing the decision. An optimizing model will perform the comparisons that management considers important, take into account major risks and constraints on the projects, and then choose the best overall project or collection of projects.

Flexibility:

The model should produce accurate results under a variety of conditions that the company could face. It should be able to be easily changed or self-adjusting in response to changes in the firm’s climate, such as changes in tax laws, new technical advances, and, most importantly, changes in the organization’s goals.

Ease of Use:

The model should be simple to use and understand, and it should not take a long time to implement. It shouldn’t necessitate specialized interpretation, difficult-to-get details, a large number of people, or inaccessible equipment. The model’s variables should also have a one-to-one relationship with the real-world parameters that the project managers believe are essential. Finally, it should be simple to model the anticipated outcomes of various project portfolio investments.

Cost:

Data collection and modeling costs should be minimal in comparison to the project’s overall cost, and they must certainly be less than the project’s potential benefits. All costs, including those for data management and running the model, should be weighed. We’d also add a sixth condition here.

Fast computerization:

Using readily accessible, standard computer kits such as Excel, Lotus 1-2-3, Quattro Pro, and similar programs, it should be simple and convenient to collect and store information in a computer database, as well as manipulate data in the model. The information can be transferred to every regular decision support system with the same ease and convenience.
We’ll look at the different types of project selection models and the characteristics that make them more or less suitable in the following sections. The shortcomings, strengths, and weaknesses of project selection models are then discussed, along with some recommendations for factors to consider when deciding which, if any, of the project selection models to use. We then go into how to choose projects where there is a lot of uncertainty about results, prices, timelines, or technology, as well as how to manage the risks that come with such uncertainties.

Finally, we discuss some unique aspects of the database required for project selection. Then, using a methodology called the Project Portfolio Method, we demonstrate how to choose a series of projects to help the company achieve its objectives. The chapter comes to a close with a review of project proposals.

The Nature of Project Selection Models:

Project selection models are divided into two categories: numeric and non-numeric. Both are very common. Many businesses employ both at the same time, or hybrid models that combine the two. As the name means, non-numeric models do not use numbers as inputs. While numerical models do this, the parameters being calculated may be objective or subjective. It’s important to note that numbers can reflect project qualities and that subjective measurements aren’t always less useful or accurate than objective measures.


Before we look at particular types of models within the two basic types, let’s think about what we want the model to do for us, keeping in mind two crucial yet often overlooked facts.

  • People make choices, not models. The decision is the responsibility of the boss, not the model. The manager can “delegate” the decision-making role to a model, but the manager cannot abdicate responsibility. Also the most complex models are just incomplete representations of the truth they are supposed to depict. Reality is much too complex for any model to capture even a small portion of it. As a result, no model can make the best decision unless it is limited to its own domain.

We’re looking for a model to help us decide which projects to pursue. This model should have the following features: It should evaluate potential projects based on the characteristics addressed previously and, above all, it should evaluate potential projects by the extent to which they can achieve the firm’s goals to create a selection/evaluation system As a result, it is important to create a list of the company’s goals. The organization’s top management should create a list of priorities. It’s a straightforward statement. A statement of the organization’s philosophy and policies The list should go beyond the usual clichés like “survival” and “maximizing income,” which are definitely valid targets, but they are far from the only ones. Other goals may include maintaining market share, developing a better reputation with specific clients or rivals, expanding into a new line of business, reducing vulnerability to business cycles, maintaining jobs for certain groups of workers, and keeping machine loading at or above a certain percentage of capacity, to name a few.

Any deliberate decision implies some kind of model. The decision between two or more alternative courses of action necessitates the consideration of some objective(s), and the decision is thus made in accordance with some, probably subjective, “model.” The use of formal, numeric models to assist in decision-making has grown since the invention of computers and the establishment of operations research as an academic subject in the mid-1950s. Many of these models use financial metrics like earnings and/or cash flow to determine whether a management decision was made correctly. Project selection decisions are no exception, with the primary consideration being the degree to which the organization’s financial targets are met.

One more task remains after the goal list has been established. Every project’s likely contribution to each of the goals should be calculated. A project is chosen or rejected based on the likelihood of such results if it is implemented. These results are supposed to aid in the achievement of the objectives. The project is chosen if the expected degree of target achievement is high enough. If it isn’t, it will be denied. It is necessary to comprehend the relationship between the project’s planned outcomes and the organization’s objectives. The types of data needed to assess a project may be classified as development, marketing, financial, staff, administrative, and other categories. Clearly, none of these considerations must be considered in any particular project decision. Not only that, but the list is incomplete, and some of the elements are redundant. Or more importantly, the variables aren’t in play. A similar degree of generality: both profitability and the effect on the organization’s reputation have an impact on the overall organization, but the effect on working conditions is more production-oriented. A framework neither are all of the elements equally essential.
The effect on current suppliers is generally regarded as less significant than the change in production costs. We’ll look at the problem of producing an appropriate list of variables and determining their relative value in a moment. We’ll talk about developing a Decision Support System (DSS) for project assessment and selection at that time. As we discuss project auditing, assessment, and termination in the next lecture(s), the same topic will come up again. The value of assessing a potential project cannot be overstated, despite the fact that it is a time-consuming and challenging task. According to a major consulting firm (Booz, Allen, and Hamilton, 1966), the primary cause of Research and Development (R and D) project failure is a lack of consideration in assessing the plan prior to spending funds. What appears to be true for such projects often appears to be true for other types of projects, and it is apparent that product development projects that include customer expectations and satisfaction in the design process are more competitive (Matzler and Hinterhuber, 1998).In the construction industry, a thorough review of a future project is a must for profitability. There are numerous horror stories (Meredith, 1981) about companies that embarked on computer information system implementation projects without thoroughly analyzing the time, expense, and disruption involved. Later, we’ll look at the issue of doing an assessment when there’s a lot of doubt about the project’s outcomes. However, before tackling this problem, it’s a good idea to look at a few different evaluation/selection models and weigh their advantages and disadvantages. Remember that the issue of selecting a project selection model will be discussed as well.

Types of Project Selection Models:

Non-numeric models are the older and simpler of the two basic forms of project selection models (numeric and non-numeric), with just a few subtypes to remember. We start with them.


  • Non-Numeric Models:

Non-Numeric models include:

The Holy Cow:

A senior and influential official in the company suggests the project in this situation. Sometimes, the project begins with a simple statement like, “If you have a chance, why don’t you look into…” and ends with an undeveloped concept for a new product, the creation of a new industry, the design and adoption of a global database and information system, or any other project needing the firm’s resources. The establishment of a “fund” to investigate whatever the boss has suggested is the immediate consequence of this bland comment. The project is “sacred” in the sense that it will be continued until it is completed successfully or until the supervisor considers the idea as a failure and terminates it.

The Operating Necessity:

If a flood threatens the factory, a project to create a defensive dike, which is an example of this scenario, does not entail much formal assessment. This criterion (along with the following criterion) was used by XYZ Steel Corporation in assessing future projects. If the project is needed to maintain the system operational, the main question is whether the system is worth saving at the project’s estimated expense. If yes, project costs will be scrutinized to ensure that they are kept as minimal as possible while also ensuring project success, however, the project will be funded.

The Competitive Necessity:

In the late 1960s, XYZ Steel used this criterion to justify a large plant rebuilding project in its steel bar manufacturing facilities near Chicago. The need for modernization of XYZ’s bar mill had become clear to the company’s management if the company was to retain its competitive position in the Chicago market. The decision to take on the project was based on a willingness to preserve the company’s competitive position in that market, despite the project’s complex planning process. In a similar vein, many business schools are reorganizing their undergraduate and master’s degree programs in order to compete with the more forward-thinking institutions. This action is motivated in large part by a decrease in the number of tuition-paying students and the need to create stronger services to attract them. While an operational requirement project takes precedence over a competitive necessity project, all types of projects necessary avoid the more careful numeric analysis required for projects deemed to be less urgent or less essential to the firm’s survival.

The Product Line Extension:

In this situation, the degree to which a project produces and sells new products matches the firm’s current product line, fills a void, enhances a weak connection, or expands the line in a new, desirable direction will be measured. It is not always necessary to perform meticulous benefit calculations. Decision-makers will act on their assumptions on how the new product will affect overall system output if it is added to the line. Assume that a business has a large number of ventures to consider, maybe several hundred. Senior management needs to choose a subset of projects that would be the most beneficial to the group, but the projects don’t seem to be easily comparable. Some programs, for example, are about possible new goods, others are about improvements in manufacturing processes, still, others are about computerization of certain documents, and still, others are about a range of subjects that are difficult to categorize (for example, a plan to open a day-care center for workers with small children). Despite the lack of a standardized process for selecting projects, members of the selection committee believe that certain projects would benefit the company more than others, even though they don’t know how to identify or quantify “benefit.” If not a formal model, the principle of comparative benefits is commonly used to make project selection decisions. The definition is used by most United Way organizations to decide which of many social services to support. The funding organization’s senior management then reviews all initiatives that have received favorable feedback and tries to build a portfolio that best suits the organization’s goals and budget.

Please read:

What is Agribusiness? – Agribusiness Education and Research International

What is the concept of a feasibility study in Agribusiness? – Agribusiness Education and Research International

Conception and Feasibility of Projects in Agribusiness – Agribusiness Education and Research International

Potential Investment sector in Agribusiness after Covid-19 Pandemic in Bangladesh – Agribusiness Education and Research International

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Md. Masudul Hassan
CEO & Editor in Chief of this Portal. Md. Masudul Hassan is an Assistant Professor and Coordinator of a Reputed University in Bangladesh. Professional member of International Food and Agribusiness Management Association ( IFAMA ). He Performed Numerous Research Regarding Agribusiness.