PDCA Cycle: what it is, steps and how to use it in the company

When you need to make a change or improvement in your company, what do you turn to: your sixth sense (the famous ‘ feeling’ ) or some planning method to guide the process?

If you are part of the “evidence-based decisions” team, in this article, you will learn everything about the PDCA cycle , a systematized method that helps to plan, execute, monitor and evaluate the actions implemented in business.

With a methodology guiding the process, there is the possibility of having to adjust the chosen actions, imagine modifying them without having any prediction of the impacts that this will cause.

So, continue reading the article and learn how to implement successful strategies, overcome challenges along the way and continue the process of continuous improvement of your company with the PDCA tool!

What is PDCA in a company?

The PDCA cycle (Plan, Do, Check, Act) is a methodology that helps solve corporate problems, with the aim of promoting the continuous improvement of business processes, assisting decision-making with accurate and reliable data and contributing to better predictability of results .

The method was created in the 1920s by the American physicist Walter A. Shewhart. In the 1950s, the statistician William E. Deming perfected the tool, popularizing it in the corporate world.

The difference is that the methodology can be applied to each process optimization cycle that the company goes through. Thus, the steps are repeated, but the context analyzed is always different, since it starts from previous improvements.

In this way, the method is a way to continue optimizing strategies to achieve the desired results, without losing what has already worked.

What are the 4 steps of the PDCA cycle?

The PDCA cycle is based on four main steps: Plan, Do, Check and Act. The names indicate what is done in each phase of the method, but we will detail each step below:

1. Plan

The first step of the PDCA cycle is to plan , that is, find opportunities for improvement and then make the plan that will lead to the changes.

And how do you know what needs to be improved? As a manager, you must pay attention to your sector to identify processes that are not working and causing problems.

This investigation results in a list of demands, so it is necessary to define which will be the priority at the moment. One way of thinking is to evaluate which process has the greatest impact on the workflow and which, if changed, would improve other steps.

Each optimization must have its own planning with a defined objective linked to the business goals. In short, at the end of this stage, you must complete the following steps:

  • Define the problem
  • Analyze the root cause
  • Set a goal
  • Decide on corrective actions

2. Do (do)

The second step of the PDCA method is Do. Here, it is time to put the planning into practice and execute corrective actions to test its efficiency.

Optimizations can occur in several areas, such as the operational line, logistics, maintenance management , training strategies, administrative practices, among others.

The action must be customized for each context, considering the need for improvement and other factors that influence the activity.

To take effective action, map out the process step by step, define those responsible for each part of the task, establish indicators to monitor results and assess the need to include new tools.

3. Check

The third stage of the PDCA cycle is verification (Check) . The main activity is to evaluate the results of the first post-change tests through performance indicators, based on the criteria defined in the planning.

The second check takes place after the implementation period has been completed. At this point, it is possible to identify errors in execution, failures and successful actions.

This way, at the end, you will have a list of what worked and what still needs to be optimized for the process to be fully efficient.

This is a crucial moment in the application of the methodology and requires a lot of attention. Some tips that can help are:

  • Set deadlines for initial testing and the overall process
  • Monitor quantitative metrics constantly
  • Define a qualitative improvement parameter, if the process does not have a numerical reference

4. Act

Closing the PDCA cycle, we reach the action stage (Act) , that is, the defined solution based on the results achieved. At this point, the responsible team creates a new pattern and records the step by step.

The new instructions must be passed on to all employees involved in the activity to avoid errors in the new stage. If, in the future, the first optimization is no longer as efficient, a new PDCA can be carried out to adjust the process.

As mentioned above, the method is cyclical, so with each round of improvements/adjustments, the four steps need to be repeated. Often, due to overconfidence, teams make decisions without planning.

This decision harms the progress of continuous improvement because, by discarding the previous process, the errors overcome may be repeated. Therefore, always remember that the continuity of improvements depends on the correct application of the methodology.

Variations of the method

PDCA is a tool recognized for its efficiency in solving business problems. It is so successful that there are variations of the method, but all maintain the four-step scheme. We have separated three alternatives:

PDSA

PDSA only changes the penultimate step of the original cycle. Check is replaced by the Study step. This does not mean that it is completely eliminated, but in this variation of the method, the study is more in-depth, using historical data from the process .

The person responsible for this change was William Deming, who, when evaluating the original cycle, considered that the verification stage was very superficial and that it was necessary to investigate the changes achieved in greater detail.

PDCL

In PDCL, the changed step is the last one. Act gives way to Learn, so instead of standardizing the process, concluding the cycle, the team turns its attention to learning.

In this way, the work continues, directing efforts to deepen knowledge about the process, evaluating the chances of obtaining other improvements with a new planning and execution cycle.

PDCL can be useful in complex operations in highly competitive markets, where efficiency is a key aspect of the operation, in addition to keeping the business at the forefront of its market .

SDSA

The last variation is SDSA , and here two steps of the original cycle are changed: Plan becomes Standardize and Check becomes Study.

The goal of this cycle is standardization. The idea is to conduct continuous rounds of testing until the goal is achieved. Only when the ideal level of performance is achieved will the new process become standard and be implemented.

To obtain positive results with the SDSA cycle, it is recommended to perform the PDSA first (once or twice) to improve the main aspects and then apply the SDSA with more knowledge about the work context.

The goal is for this approach to consolidate improvements and prevent the recurrence of errors and mismatches.

How to do a PDCA cycle?

To carry out a PDCA cycle, it is essential to follow each of the main steps, or variations. As the process is systematized, the steps complement each other and aggregate information, allowing for accurate assessments.

The methodology can be repeated as many times as necessary and there is no limit. However, having a break between one cycle and another helps to observe the situation with more perspective and understand where the root cause of the error/misadjustment lies.

The responsible team sets the deadline for reapplying the method. This way, those involved can monitor the work and, most importantly, avoid skipping steps due to the rush to reactivate the process.

Remember that one step depends on the other? Therefore, an error or poorly performed assessment can directly impact the quality of the final PDCA analysis.

Errors in applying the tool

To obtain a good result, be careful and avoid the following mistakes when applying the cycle:

In-depth reviews

When a problem arises in a company, the most urgent need is to resolve it quickly. However, rushing can lead to a superficial analysis of the situation. Without finding the root cause, the solutions defined on impulse will not actually improve the situation.

To assist in the planning stage, use other management tools with a systemic approach, such as the 5 Whys and 5W2H , which focus on helping to define the root cause of problems.

Insufficient knowledge

The team responsible for conducting the PDCA cycle needs to have knowledge not only of the methodology, but also of the process being analyzed.

So, if the work will be in the operational sector, the team needs to include people with sufficient knowledge of the area and its processes for the proposals to be coherent.

Furthermore, improvement proposals need to consider the need for new training for the team that will carry them out so that employees can learn the new technique.

Confused assessment

Another PDCA error is to make a confusing assessment without clear criteria to support the conclusions. An incorrect assessment can lead to changes that will not be beneficial to the process, causing losses in productivity and deliveries.

Therefore, the indicators that will guide the analysis need to be well defined for the assessments to be accurate and reliable.

PDCA method example

As PDCA is widely applied in process improvement, we will use the increase in industrial productivity as the objective to be achieved to create a practical example.

In the planning stage, the team must evaluate which activities are carried out, determine the average production time, how many people work in the area evaluated, who the manager is, etc.

One of the problems identified is not having enough staff to cover the entire process quickly. So, in the execution stage, the first test is to include more people in the team for two weeks to try to reduce the average production time by 5%.

During verification, the team assesses whether, in the period analyzed, the indicator was reduced and what impact this had on the rest of the operational chain.

If the results are positive, new employees can take on the position permanently. If the desired result is not achieved, it is essential to listen to the team to understand what went wrong and gather new suggestions for improvement.

The example is simple, but it illustrates the collaborative work of applying PDCA well. It is not just the steps that are connected, the team leading the project and the team executing the tests must collaborate, contributing to broadening the vision of the framework studied.

Where can PDCA be used?

PDCA is a versatile method that can be used to guide various types of projects in different areas of a company. In the corporate world, the method is applied to the following activities:

  • Business process optimization : reorganizes and improves workflows by including or deleting steps to optimize the process.
  • Adapting sectors to compliance guidelines : helps to make changes so that a sector, for example, finance, meets compliance requirements, prioritizing transparency.
  • Creation of new products or services : the PDCA method is very efficient in guiding the steps of including new items in the portfolio, prioritizing the high level of quality of the company’s solutions.
  • Efficient standardization : the method guides the entire process of implementing new standards, highlighting errors and successes and allowing them to be improved to meet the defined requirements.

Supplier selection: how to do it + essential criteria

Building good partnerships in the market is essential for any company. After all, every business needs the support of third-party services or raw materials, both for good management and to keep the operational flow running.

Therefore, a well-made selection of suppliers keeps on the list of options only those who are truly qualified, reliable and capable of providing a good service.

With the digitalization of processes, the search for new providers gains an enabling component: the internet. This way, it is easier to find and filter references.

The report ‘ The Gartner Future of Sales 2025 ‘ by consultancy Gartner, highlights that, by 2025, 80% of sales communications between suppliers and B2B buyers will take place through digital channels.

And to conduct the process online, your business’s purchasing management team needs to be careful before closing a deal with a partner.

Continue reading the article to find out what key requirements a potential supplier needs to meet before being included in your company’s partner list.

What is the supplier selection procedure?

The supplier selection procedure is the process carried out by the purchasing management area of ​​companies to find, evaluate and hire new businesses as partners.

The objective is to put together a list of reliable brands on the market for the supply of products, raw materials or services to supply, or manage, different sectors.

The quality of a business’s supply chain depends on choosing good suppliers that contribute to the continuity of operations.

In addition to resulting in a list of reliable references, the selection process helps to:

  • Reduce purchasing risks;
  • Strengthen partnerships with trusted companies;
  • Gain bargaining power ;
  • Negotiate advantageous values ​​and terms and much more.

How to select suppliers?

To make a successful supplier selection, the process needs to be well organized internally. The goal is to follow the process step by step and analyze all the criteria before including a new partner as a possible purchase option.

If your company doesn’t have any methods yet, start with market research . Suppose you need a new packaging supplier. The first step is to search the internet for businesses in your area.

Next, search engines highlight businesses at the top of the page, helping to raise initial options for further research on websites and social networks.

The next step is to select those you will contact . The goal at this stage is to understand the supplier’s sales processes, whether they have well-defined, clear steps and whether the service is well-conducted.

Then, it is time to send an order proposal and analyze the price, delivery time, or the way the service is provided, and payment methods from the most qualified suppliers.

The team must evaluate everything from the way the budget is presented to the detail and clarity of all the details to close a purchase.

As with any purchase, negotiation is part of the process. So, evaluate the supplier’s negotiation flexibility, what conditions they offer for the first order, whether they offer any type of benefit for recurring purchases, among others.

Closing the supplier selection process does not mean ending up with just one company to serve the packaging category. Define three options , one main and two as a backup, so as not to depend on just one partner. This rule applies to all categories.

What are the criteria for selecting suppliers?

Until reaching the top 3, the purchasing team needs to be rigorous about supplier selection criteria to avoid problems hiring companies that could let your business down.

We have separated five requirements that should be considered from market research to the final choice. Check them out!

1. Market experience

The first criterion for selecting suppliers is market experience . The time and history of operation, as well as the achievements and infrastructure that the company offers to its customers, are indicators of the solidity of the business.

Who the owners are, their qualifications, as well as those of the team, add up to important points when considering a company as an option.

2. Knowledge about solutions

When you speak to a potential supplier’s customer service team or directly to the business owner, take note of their knowledge of the solutions they sell.

In addition to offering the most up-to-date inputs or services on the market, can they clearly explain the advantages of each option? Are their arguments clear? Do they show their preference in the negotiation and confidently defend what they are selling?

The answer to the questions must be a unanimous ‘yes’.

3. Credibility

A company’s reputation in the market is another fundamental criterion when selecting suppliers. Therefore, do not be superficial in your research. Look for channels other than search engines, such as social networks, supplier websites, online review sites, among others.

If what the company sells matches what people say about it, the chances of it being a good partner increase.

4. Transparency

Transparency may seem like a subjective criterion, but it is more tangible than you might think. The way a supplier conducts negotiations, being consistent in all arguments for closing the proposal, without contradiction, is an example of transparency.

The same should happen with contracts and financial obligations involved in transactions. Any “trick” in the process is a red flag. Be careful!

5. Customer evaluation

In the internet age, it is difficult to make a purchase or choose a business partner without knowing the opinion of someone who has already gone through the process, do you agree?

So, research who the suppliers’ partners are, whether they are reliable companies, what positive points they raise and what types of criticism/improvements are pointed out. This way, you can assess whether or not it is worth doing business with them.

Why should you perform periodic predictive analysis?

While periodic predictive analytics is a discussed topic much today, in itself, it is quite an old concept. Mostly different now is that we can count on exponentially greater computing power and data volumes compared to then.

To understand the subject better, in the article we explain what predictive analysis is, how it works and why it is important. We also show you advantages offered, its relation to other technologies and application examples so that you understand why you should perform this kind of analysis.

Happy reading!

What is predictive analytics?

Predictive analytics — or predictive technical analytics — is a way of performing advanced analysis to check data or content in order to answer the question: what might happen in the future?

To do this, statistical modeling techniques, Big Data and machine learning are used, which allow historical data to be extracted and predictions to be made.

This predictability is possible due to the capacity of Big Data, which obtains data through several interconnected systems. They can be interpreted to verify how a group or a person will behave in relation to a certain context.

How does it work?

This predictive model is nothing more than a mathematical function, which performs a complex statistical calculation to present possibilities to a manager.

In this sense, a retail company, for example, can use a wide variety of data as a basis to understand that demand for a product may increase at a certain time of the year.

Based on this prediction indicated by the algorithm, decision-makers can understand that it is necessary to reinforce stock to meet demand, avoiding being caught by surprise.

How important is predictive analytics?

In an economy with fierce competition, the use of this tool becomes an important differentiator. After all, who wouldn’t want to be more certain about the likely outcomes of a decision?

In this regard, the value of predictive analysis lies in the very prevention of the events themselves based on the trends, traceable from similar circumstances encountered in the past.

What are the benefits of predictive analytics?

What is the most significant benefit of predictive analysis? It is that it enables companies to learn from their experiences – from their data – and to take effective measures to apply what has been learned toward better futures.

Below, see other important advantages:

  • eliminates the burden of manual data analysis and minimizes errors;
  • generates competitive advantage in the market;
  • improves customer satisfaction;
  • increases the chances of successful product launches.

How does predictive analytics relate to other technologies?

To perform predictive analysis, the company needs to keep in mind that it is a process that requires several other enabling technologies. Below, learn about the main ones.

Predictive Analytics and Artificial Intelligence

Here, we have two terms that are similar and closely related. This is because Artificial Intelligence is the fuel of predictive analysis, since this method considers not only historical data, but also seeks to predict various future possibilities.

To do this, you need applications capable of feeding the algorithms with external data collected in real time, to find patterns, behaviors and design future scenarios.

Predictive Analytics and Big Data

Big Data is the backbone framework under which data-gathering applications are implemented. It will be the raw material from which algorithms and models will be built. Hence, good interfaces to Big Data must be important in effective predictive analytics.

Predictive Analytics and Business Intelligence

Business Intelligence is the process of gathering, storing, and analyzing business data to extract insights that help drive better decision-making.

Therefore, when a company performs a predictive analysis, it is applying a BI action. With this, it obtains proposals for executable actions that enable better solutions.

What are the 3 Vs of predictive analytics?

Predictive analytics relies on Big Data. This technology is based on five Vs, identified as volume, variety, veracity, velocity, and value. Three of them are fundamental to the success of predictive analytics — as we will show below.

Variety

It is very important to have a good diversity of data sources and formats, to obtain a deeper analysis. In addition, this aspect helps to obtain less “biased” results — often caused by a single database.

Truthfulness

Veracity is an essential aspect: there is no point in having a large volume of data if the information is not reliable. Therefore, before carrying out any type of analysis, it is important to question whether the source of such data is reliable.

Speed

Just as important as having reliable and diverse data is having the agility to process it. This is because many of the insights may no longer be useful if the timing is lost.

In this sense, a good platform needs to have the ability to cross-reference information collected in real time, to generate accurate predictions based on the analyses.

What are examples of the application of predictive analytics?

Predictive analysis is part of the routine of large companies in a wide range of segments. Below, see some examples of its application.

Churn prediction

Predictive analysis can predict when certain customers are no longer satisfied with the solutions offered. This way, the company can plan better , based on a review of its weaknesses.

This allows you to develop new retention strategies or, in difficult scenarios, at least better prepare for customer loss.

Upsell and cross-sell

In contrast to churn prediction, in this aspect, the company can perceive the customer’s willingness to be interested in a new product.

This way, it is possible to approach it more precisely to offer a more advantageous upgrade for the customer and more profitable for the company.

Agribusiness

One of the biggest challenges that this technology allows us to overcome is knowledge about the climate and the conditions that impact planting.

Based on historical data and the help of advanced algorithms, we are able to predict events and receive insights on measures to overcome them, making the production chain more flexible to survive climate change.

The information also increases business leaders’ visibility into the level of waste and losses, among other aspects.

The use of field data and the automation of agricultural equipment make it possible to carry out rural activities more effectively and on a broader scale. An example of this is an autonomous tractor, which can receive weather forecast data.

Using this information, the equipment identifies when it can carry out its work in better weather conditions. Therefore, if the weather worsens, it can stop automatically and continue the interrupted task as soon as the situation changes.

In this way, agricultural management can incorporate predictive analysis, with a focus on the evolution of cold chain chains, to avoid losses , increase productivity and get ahead of the competition in terms of traceability, quality and reliability.