The bottleneck In Production – What Is It and How to Deal With It?

The bottleneck in production is some scarce resource that reduces the production capacity of the whole chain. The bottleneck needs to be efficiently managed to ensure profit maximization and enhanced business efficiency.

It’s called a bottleneck because the neck of the limits the volume of water to be stored in the bottle and the production bottleneck similarly limits the quantity of the units to be produced in the production process.

How does it work?

Businesses usually face some challenges that bring limitations in their capacity to operate and grow. These limitations can be short or long terms in nature. The short-term limitations usually do not present a greater challenge. However, the cumulative effect of the long-term bottleneck is adversely higher on the business growth and efficiency.

An example of a short-term limitation is the skilled employee going on leaves. This might create problems in the production if an adequate replacement is not available within the organization. As it’s going to stall the production since the time he/she resumes the office.

An example of long terms limitation may be the shortage of chemical mixing machines for a company in a dying industry. While there is sufficient labor to work, sufficient production space, sufficient availability of the raw material, and all other facilities.

However, due to higher time consumption in the mixing machine labour have to sit idle and the capacity of other resources cannot be utilized in full. Hence, chemical mixing machine works as a bottleneck in the process of production.

How to deal with production bottleneck?

An ideal approach to deal with production bottleneck is the identification of the bottleneck, evaluating consequences of the bottleneck on production, managing the bottleneck with all the available resources, and increasing production efficiency. Let’s discuss these conceptual aspects to deal with the production bottleneck.

1) Identification of bottleneck

The bottleneck can be identified by looking at the accumulation in the production process. It’s the point where units take greater time to be processed and there are several units in the queue of production.

However. Identification by accumulation does not always work. So, a more reliable approach is to use throughput in the production process. The throughput helps to determine the number of units processed by the machine at a specific time.

If there are different machines in the process of production, a comparison of throughput between each machine can help to identify the machine that produces the lowest units in a specific time.

Hence, the machine with the lowest production is a specific time is a bottleneck that limits the production of the entire production process.

2) Evaluating the consequences

In the second step, the managers need to understand the consequences of an identified bottleneck. The effects of the bottleneck may be severe depending on the nature of the process.

The business needs to identify the consequences of the bottleneck whether it’s stalling the production process and business is delaying the supplies to the customers or any other adverse impact on the operational efficiency of the business.

The bottleneck can actually stall the production as other resources may remain idle in a wait to get input from another machine which is a bottleneck.

The delayed production due to bottleneck may cause inventory to pile up in the warehouses. The company may have to incur additional costs for warehousing due to slow production.

Further, a low rate of production may be a cause of motivation for the employees as their remuneration may be linked with the number of units produced.

Hence, bottleneck resource needs to be managed appropriately to ensure enhanced business efficiency.

3) Managing the bottleneck

The severity of the consequence helps to decide what action should be taken to manage the bottleneck. If the consequences are severely adverse, immediate action can be required.

For instance, if the consequence of bottleneck is an inability to meet the customer orders and impairment in the good will of the company.

The business may need to manage the bottleneck resource immediately even they have to incur the cost. The bottleneck can be managed by addition to the scarce resource, usage optimization, adequate maintenance of the resource, and any efforts that increase output from the resource.

On the contrary, if the impact of bottleneck resource is not much higher, it may be ignored else the cost of resource management may be higher than the benefit obtained.

What causes a production bottleneck?

The production bottleneck can be caused by any process in the chain of processes that takes more time than other processes. It may be actually some resource that is scarce and cannot be purchased easily.

The problem with bottleneck resources is that production cannot be completed without a product passes through/consumers the bottleneck resource.

For instance, three machines A, B, and, C is required to produce a product-A. These machines take 10 minutes, 20 minutes, and 10 minutes to process the product. In working shift of 6 hours 36 units can be processed in the machine A and C.

However, machine B can only process 18 units. Hence, the whole production can only finish 18 units in one shift and machine A and C remain idle for half of the time as machine B takes double time to process the product.

Hence, machine B is a bottleneck that limits the production of the entire chain. In this case, more production time of machine B causes a bottleneck. The bottlenecks can be different in different situations. Hence, business needs to get one more machine B if they want to avoid an idle time of machine A and C.

However, if the business produces different products and it cannot get a new machine it can prepare the production plan that maximizes the profit.

Profit maximization using bottleneck

To maximize the profit with bottleneck the business needs to consider contribution generation concerning usage of the scarce resource. If there are multiple products of the business it needs to identify which product has the highest contribution margin by using per unit of the scarce resource.

Once the contribution margin per unit is calculated for all the products. The individual products are ranked in order with a high to low contribution margin per unit. This approach makes the business get maximum contribution by using bottleneck/scarce resources.

Linear Programming in Management Accounting

Linear programming is a management/mathematical approach to find the best outcome, giving a set of limited resources. Thousands of businesses emerge every year, as more people aim to be business owners. Most of these businesses do not experience growth and eventually fold up due to failure in management accounting.

How should businesses manage production challenges such as constraints, low productivity, and poor profit margins? What tools can companies employ to perform better?

Linear programming proves to be one of the best tools to use in achieving excellent results in components (decision variables), characteristics etc. management accounting.

What is Linear Programming?

Linear programming in management accounting is a method business adopt to reduce costs and increase profits. In management accounting, it is used to minimize costs or maximize profits by working through a set of options to develop the best combination of resources.

You can only use this technique when all the relationships are linear. And even amid constraints, businesses can thrive efficiently using linear programming.

By constraints, we mean the limitations that affect the financial operations of a business. These constraints can be in the form of a policy or principle that controls how a company carries out its expenditure.

Linear programming covers mathematical methods to determine how one can maximize or minimize a linear function in the face of constraints.

The application of linear programming is open  to various business/industries such as;

  • Energy
  • Food/agriculture
  • Engineering
  • Production/manufacturing
  • Transportation

Decision Variables in Linear Programming

Decision variables usually take the form of mathematical symbols ( e.g., n, x, y). These symbols represent the values that each variable(quantity) should have.

Let’s take an example to understand decision variables better. Your business may be such that you produce two different snacks; burgers and doughnuts. It could be that people often order burgers rather than doughnuts.

Looking at your budget, you discover that you can only produce more doughnuts because the materials you need for the burgers are expensive. You have to decide where to invest more and what quantities of each snack you should produce.

You wouldn’t want to lose, would you? Remember that linear programming uses mathematical methods to achieve better output while minimizing losses and saving costs.

As a decision-maker unaware of how much quantity to produce, you can use a decision variable to create an equation. This equation will, in turn, help you determine the amounts to produce.

In this case, the variables are what quantities of burgers and doughnuts to produce and what amount of material to purchase. You can now use symbols( or decision variables) like n, x, or y to indicate the unknown quantities to get a solution faster.

Characteristics of Linear Programming

There are five major characteristics of linear programming. They include;

1. Objective Function

In a linear program, you must define the objective using a precise mathematical model. The objective is mostly to reduce cost and increase profit.

2. Constraints

A constraint restricts the level of output. The general rule is for every constraint to be described using mathematical symbols. Some examples of the constraints that may apply to a linear programming model are:

  • Shortage of funds
  • A limited amount of labor hours
  • Not enough machine time
  • A limitation in the quantity of materials
  • Not enough operating space
  • A constrained amount of time

3. Non-negativity

You must write variables in positive forms only because the linear program does not support negative variables. The least is zero.

4. Linearity

As its name implies, a linear program is linear. Linear here means the proportional connection between variables ( could be two or more). The proportional degree of the variables should not be more than one.


There is no room for infinite inputs/outputs in linear programming. Where various factors are unlimited, it becomes impossible to find a solution.

Advantages of Linear Programming

  • Linear programming helps in the adequate management of resources, thereby maintaining productivity.
  • As a decision-maker, linear programming equips you with tools to make the right decisions for your business.
  • The various tools linear programming provides are easy to apply to problems and bring real solutions.
  • With linear programming, you can always spot likely problems you would encounter in your business and what to do about them (e.g., during production)
  • You can also adjust to changes that would emanate in the future because the program prepares you in advance.

Disadvantages of Linear Programming

  • It is not possible to change variables.
  • You can not use the graphical method to handle problems that contain more than one variable.
  • You cannot handle functions that are not linear.
  • You may not be able to write all constraints in linear inequalities.

What is Limitation Factor Analysis?

Resources required for production can be quite scarce. Besides, one level of input might be needed to produce different products at different levels of outputs.

Limiting factor analysis is the technique used to figure out how to maximize your production output despite the various limitations that confront the production process. It is the goal of every business to maximize profit; therefore, it is essential to analyze the best combination of limiting factors, to yield maximum return.

It might prove somewhat tasking to make the most out of the resources available. As such, you’ll need a proper understanding of how best to analyze the limiting factors within your organization’s operations.

What is the Limiting Factor Analysis in Management Accounting?

In Management Accounting, Limiting Factor Analysis is a technique that seeks to maximize profit by the appropriate handling of limiting factors.

Before we talk about the analysis itself, let us understand what limiting factors are. Limiting factors are required scarce resources that can restrain an organization from making maximum profits by affecting production outputs.

They are the inputs that determine the limits in the quantity and quality of the products. Such factors may include a shortage of materials, machine capacity, labor, financial capital, etc.

The aim of the analysis is simple – to maximize profit in the end. As such, it entails careful consideration of the limiting factors and their effects on each unit of production.

When you conduct the analysis, you get a clearer picture of each product’s contribution per unit resource used. This will enable you to place greater priority on products with a higher contribution per unit.

You can guess what the result is- more profit and the utilization of scarce resources more effectively.

Since limiting factor analysis is conducted on a short term basis, variable costs, rather than fixed costs, are the only relevant costs to the decision making process.

Can There Be More Than One Limiting Factor at a Time?

Yes, your business can encounter more than one limiting factor for a given production. For instance, there might be a shortage of labor as well as limited machine power.

Also, one limiting factor can eventually lead to another. For example, insufficient production capital may lead to the inability to purchase enough raw materials. This may, in turn, lead to producing a product below the standard demands in quantity.

Note that you can use Limiting Factor Analysis when there is one limiting factor. In the case of multiple factors, linear programming is employed for a solution to the problem.

How Do You Find a Limiting Factor?

When there is more than one product utilizing a resource, you need a good production plan to stay on course for profit.

For such an effective production plan, you can follow these simple steps.

  • Point out the limiting factor
  • Find out the units of the resource required for each product.
  • Find the per-unit contribution of each product (relative to the resource in question). You can calculate this by subtracting the variable costs from sales.
  • Rank the products in decreasing order of contribution per unit of the vital resource.
  • Use the ranking as your guide for the allocation of the said resource.

Advantages of Limiting Factor Analysis

Some advantages of limiting factor analysis are as follows;

  • A proper analysis of limiting factors will give you an insight into the implications of those factors in your business production.
  • It arms you with the right details on how each product utilizes the allocated scarce resources.
  • Without this crucial knowledge, you risk channeling your energy and efforts to less essential products.

Disadvantages of Limiting Factor Analysis

Simple limiting factor analysis is often useful only when there is just one limiting factor affecting the production.

When there are multiple constraints, it will take some much more complex methods to deduce meaningful conclusions to aid decision-making. This is because there are more details to capture in the analysis if it must be useful.

It simply implies that you will have to go through more rigor. In the long run, you might need to employ the services of an advanced professional. Of course, this interprets as an extra cost.

Bottom Line

Given the scarce nature of the most relevant resources, you must make every effort to allocate these resources efficiently. To do this, you need to know which combination of products gives you the most investment return.

With suitable limiting factor analysis, you can be sure you will set your priorities right and maximize business profits.

What is Special Order Pricing? And How Does It Works

One short-term decision that businesses continuously have to make is whether or not to accept special orders. This decision can prove somewhat of a complication to companies because they do not anticipate it when creating their yearly budget.

Although it might be outside an organization’s scope of activity, it is an opportunity to earn more revenue and crush sales goals. Like the name implies, special orders are not the regular type of order.

They typically require a lower price than the regular orders, and your business might incur additional costs during the execution process.

Although special orders demand a lower price, the emphasis is much more on whether the order will result in a profit or not.


A special order is a one-time customer order that often involves needing a large quantity at a low price. For most businesses, it’s a chance to make or lose money.

Tough choice, right?

When a special order is placed, you’ll be required to make decisions based on the analysis. And of course, the goal is to make the best decision that will maximize profit.

To simplify things for you, here is a list of questions you should ask yourself when considering a special order;

What are the relevant costs?

Variable costs are the most relevant cost to the special order. Unlike the fixed expenses, you want to make sure that your variable costs are covered.

For fixed costs, it would help to assume that you pay your fixed expenses from your regular production activities. It would help if you assumed that you received some orders, delivered the request, and billed the clients. 

With this revenue, you can cover your fixed cost, like insurance premiums, building a lease payment, etc.

Does your company have an excess capacity to fulfill the order?

Like we mentioned earlier, a special order is a random order. As such, it should only be considered if you have extra capacity to do work. You must make sure that your company has excess ability to do the job without altering the standard production process.

Will there be profit in the order?

You need to know this: you can accept a lower sales price and still be profitable with a special order. Since fixed costs have already been paid for with your regular production, they have become a sunk cost. 

As such, you shouldn’t bother about covering your fixed cost with your special order revenue.

When should a Special Order Price be accepted?

Like with every other form of business, the goal here is also to make a profit. Accept a special order price if it will give the company an additional profit.

Based on the above explanation, we can draw that the special order price is the price at which the company is willing to offer the special order. Employ diligent care and attention when determining this price.

This will help you can build a good customer relationship and secure a potential next order. The result is that you and your customers are happy. It’s a win on both sides!

To achieve this satisfaction, the company should be willing to accept a lower profit margin with a large quantity. When these two are combined, you can make an excellent profit.

To further determine if you should accept a special order or not, use the contribution margin approach to do your analysis. This analysis will ascertain if the order will lead to a profit or loss. Follow these steps;

1. Determine the contribution margin per unit

The formula for calculating the contribution margin per unit is:

Order Price – Variable Costs per unit.

Exclude irrelevant costs like fixed costs from the calculation.

2. Determine the total Contribution Margin

You can determine this by multiplying the contribution margin per unit by the number of units in the special order.

3. To determine Profit or Loss, less any Incremental Fixed Costs from the Contribution Margin

If there are any incremental fixed costs, you’ll have to subtract them from the contribution margin. But if there are no fixed costs, your contribution margin is your total profit. It’s that simple.

4. Decide whether or not to accept the Job

The general rule is to take the job if it generates a profit and decline if it incurs a loss.

Bottom Line

You will agree that the entire concept of special order pricing is not as difficult as it seems. It starts by first identifying your irrelevant costs and then subtracting them from your calculations.

Use the answer you derive from the contribution margin approach to determine if you should accept or reject the order.

Sensitivity Analysis: Types, Methods, and Use

Have there been times when you step out of your house in the morning with your whole day planned down to every minute? However, along the line, one or five things go wrong, and the entire day goes down the drain.

The field of financial modeling can be a lot like this. There are various possibilities, and a good financial model is the one whose sensitivity you can stress-test against all these. 

Sensitivity analysis can help give you appropriate insight into the problems related to any particular financial model.

What is Sensitivity Analysis?

Sensitivity analysis is the method used to find out how independent variable values will affect a particular dependent variable under a particular set of assumptions. 

It is a technique that determines how the unpredictability in the outcomes of a model or system can be as a result of the different sources of unpredictability in its inputs.

Sensitivity analysis is used within specific boundaries, which is dependent on one or more input variables. Also referred to as the what-if analysis, it can be used for any system or activity. 

From making decisions at corporate levels to planning a vacation with some variables in mind, you can do all these through sensitivity analysis.

Simply put, sensitivity analysis is a way by which you can foresee the outcome of a decision provided in the form of a specific range of variables. By creating a set of variables, the analyst can point out how changes in a variable affect the outcome.

Types of Sensitivity Analysis

Primarily, there are two types of sensitivity analysis, which are 

  • Local Sensitivity Analysis
  • Global Sensitivity Analysis

Local sensitivity analysis is based on derivatives (numerical or analytical). The word “local” signifies that the derivatives are taken at a single point. This type of sensitivity analysis is great for simple cost functions but not practical for complex models.

Local sensitivity analysis is a one-at-a-time (OAT) method that assesses the effect of one parameter on the cost function at a time, holding the other parameters fixed.

On the other hand, global sensitivity analysis uses a global set of samples to analyze the design space. It is usually carried out using Monte Carlo techniques. Some of the more widely applied techniques include:

  • Differential sensitivity analysis
  • Factorial Analysis
  • One at a time sensitivity measures

Methods of Calculating Sensitivity Analysis

Below is a step by step method of calculating sensitivity analysis:

  • Firstly the base case output is established; say the NPV at a certain base case input value (V1) for which the sensitivity is to be calculated. Keep all the other inputs of the model constant.
  • Then calculate the output’s value at another value of the input (V2) while keeping other inputs constant.
  • Determine the change in the percentage in the output and the percentage change in the input.
  • Divide the percentage change in output by the percentage change in input. This is how to calculate the sensitivity. 

Keep repeating the process of testing sensitivity for another input while keeping the rest of the inputs constant until you obtain the sensitivity figure for each of the inputs. 

The determination would be that the greater the sensitivity figure, the more sensitive the output is to any change in that input and vice versa.

Uses of Sensitivity Analysis

Sensitivity analysis is important for various reasons. Some of its uses include: 

  • Improves the understanding of the correlation between output and input variables in a system or model.
  • Evaluates the strength of the output of a model or system in the presence of uncertainty.
  • Look for the errors in the system or model by determining the unexpected relation of the inputs with the outputs.
  • Lowers uncertainty by pointing out model inputs that generate uncertainty in the output. 
  • Boosts communication between decision-makers and modelers. It happens by making suggestions that are more credible, understandable, persuasive, or compelling.
  • Enables model simplification by fixing the model inputs, which do not affect the outputs. It may also occur by ascertaining and removing unnecessary parts of the model structure.
  • Attempts to identify vital connections between different observations, forecasts, or predictions and model inputs, which brings about the development of better models.
  • Alleviates the calibration stage by bringing out the sensitive parameters. Sensitivity parameters should be known as without that, the result can be a total wastage of time being spent on the non-sensitive sections.


Sensitivity analysis is a useful tool that assists decision-makers with more than just a solution to a problem. It gives a reasonable insight into the problems related to the model under consideration.

It also provides the decision-maker with a decent idea of how sensitive the ideal solution chosen by him is to any changes in the input values of one or more variables.

SIMULATION MODELS: Types, Use, and More

Across various industries and disciplines, simulation modeling proffers valuable solutions by giving clear insights to complex problems.

One significant advantage that sets simulation models apart from other models is analyzing a model without altering the real-life sequence.

Let’s consider a couple of things you need to know about simulation models.


A simulation is an attempt to reproduce a real-life event or process in an isolated environment.

So you can understand better, let’s consider this example. Think about building a model about the cycle of events that play out in a retail store. You begin by first deciding the rules.

How will people interact? When will supplies be delivered? You’ll also consider things like Rush-hour, downtime, and every other thing you’ll need to design an accurate model.

The next step will then be to test the model in simulation software. Simulation software will show you the results of implementing the rules against certain variables. Some of these variables may include a black Friday surge or an unavoidable late night shipment.

Three primary situations would necessitate simulation software. They include;

  • When you have insufficient data, you will typically encounter this when studying historical or ancient events.
  • When you have complicated business processes, such that you cannot analyze them with traditional methods.
  •  When you need to experiment with low risk, low-cost environment.

Depending on the problem and nature of the variables involved, you can employ different simulation models to analyze the situation.

Types of Simulation Model?

There is a wide range of simulation models that you can choose from. Your selection, however, should depend on the nature of the real-life event, your intended outcome, and your requirements.

Let’s consider four types of simulation models below.

1. Monte Carlo/Risk Analysis Simulation

In straightforward terms, a Monte Carlo simulation is a method of analyzing business risk. Most businesses use this model before implementing any major project or initiating a change in a routine.

The Monte Carlo simulation model is mathematically inclined and uses empirical data of real inputs and outputs. It further identifies potential risks and uncertainties through probability distributions.

Businesses use this simulation model because it provides a thorough understanding of the market. This model goes very well with any industry or field.

2. Agent-based Modeling & Simulation

The Agent-based simulation model analyzes the impact of an ‘agent’ on the ‘environment’ or ‘system.’ It analyzes a cause and effect situation. For example, consider the effect of new factory equipment on a manufacturing line.

The agent in this model could be any factor that the business environment responds to. It could be equipment, people, and practically anything else. In designing the simulation model, rules must be prescribed to act within the system. You’ll then observe how the system responds to those rules.

And of course, you shouldn’t come up with these rules in abstraction. You should base regulations should be based on real-life world data.

3. Discrete Event Simulation

A discrete event simulation model allows you to observe specific events that trigger your business processes. Take, for example, the technical support process that involves the user calling your company, your system receives and assigns the call, and your agent picks up the call.

You can use the discrete simulation model to study different systems to give you a wide range of outcomes. Some typical plans include healthcare, manufacturing, technical procedures, and others.

4. Systems Dynamics Simulation Solutions

Unlike its counterparts, this simulation model is very abstract and discrete. The Systems Dynamics Simulation Solutions does not encourage specific details. So in analyzing a manufacturing company, this model does not account for data like labor and machinery.

How then does the Systems Dynamics Simulation Solutions operate?

Well, the model simulates for the long term and strategic level overall view of the system. Its priority is to get a comprehensive insight into the entire system about action in basic terms. Some examples of situations where this model may apply are deciding to end a product line, reducing CAPEX, etc.


The simulation model uses vary, and it is often used when it is impossible to conduct experiments on real-life scenarios. Here are some important uses of simulation models;

1. It provides a risk-free environment.

Simulation models provide the safest way to explore and test different scenarios without having to risk anything. With a simulation model, you get to make the right decision before effecting the actual changes.

2. You save money and time

Experiments are less costly and require less time when using simulation models. You also do not have to worry about alerting competition unnecessarily as your activities are conducted more covertly.

3. A higher level of accuracy

With simulation models, you can capture more details than an analytical model. As a result, this type of model’s outcomes is more accurate, leading to precise forecasting.