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3 Steps to Follow for a Successful Process Planning in '24

3 Steps to Follow for a Successful Process Planning in '243 Steps to Follow for a Successful Process Planning in '24

Process planning is the practice of preparing user instructions to select and transform an input into an output within an organization. With process planning, organizations can generate necessary processes that would help them allocate their resources efficiently to achieve their end-goal.

However, process planning can be an intimidating project for large enterprises in which processes are executed with the involvement of different teams and numerous employees. Moreover, analysts often confuse process planning with process design, thus complicating the implementation of the former.

To help businesses have a more successful process planning, in this article we aim to:

  1. Clarify the difference between planning and design,
  2. Compare manual and automated process planning,
  3. And provide 3 steps to follow with quick tips.

Process Planning vs. Process Design

One reason process planning is confusing is due to its similarity with process design.

Process design, along with operations design, is a prior-step to process planning. Process planning specifies the designed process and operations by converting them into steps and instructions that effectively generate a product or service. For example, process planning can create operations for manufacturing a product or designing a marketing campaign.

Manual vs. Automated Process Planning

Process planning can be done manually or automatically, by leveraging process intelligence tools, which is called automated process planning.

Manual Process Planning

In classical process planning, planning team interviews company personnel to collect data, generate a flow chart with attached information on the details and document the process.

However, this way of planning business processes is outdated for three reasons:

  1. Each step prolongs due to manual work,
  2. The final document might include manual errors,
  3. Final model might not reflect the reality because it relies on people’s rough understanding of the process, rather than the actual data.

Automated Process Planning

Automated process planning tools shorten the planning time, and prevent human errors in data extraction, discovery, modeling and mapping phases.

Here are the three major steps towards process planning:

Step 1: Definition

Definition is the stage where the process is described in terms of inputs and outputs. The points that process planning clarifies include:

  • The output of the process,
  • The party that receive output,
  • The necessary inputs,
  • Success measures (e.g., quality scores),
  • The important checkpoints in the process

For example, for a manufacturing process, planning team decides on the ultimate product the firm produces. Then, the teams select the raw materials, machining methods, machines, fixtures and machining sequences required to manufacture the good. They calculate conditions and tool paths to finalize the first stage.

Quick Tip:

Leverage process data to identify all inputs and outputs of a process in a faster and data-driven manner. To deal with complex process data, benefit from process mining. Process mining tools automatically extract the data from IT systems and web applications and apply ML algorithms to discover the data.

Step 2: Visualization

Process planning teams use the information clarified at the first step to map the process they aim to plan. The process maps display optimal work flows with all the interactions required between parties and departments but do not include any loops across departments or activities.

These visuals serve as reference models to use in process application, train employees and compare against the actual processes in practice. Based on the comparison results, planning teams can improve their existing processes by re-defining them or implement modifications.

Quick Tip:

Apply process mining tools to seamlessly model and visualize operation flows based on the actual process data. This way you will allocate less time for each activity and produce more consistent and correct models.

You can still go for interviews to capture the qualitative aspect, If you want! Or, you can create models by using process modeling software to fasten this phase.

Also, you can always compare these qualitative models against the quantitative models obtained from process mining to ensure accurate and feasible procedures and standards.

Step 3: Documentation

At this step, the planning team documents the process flow defined and mapped previously. The documents include all the steps, tasks and actions executed in the process flow in-detail.

Once the document is ready, the team would share it with the other departments that are involved for revision sessions. These sessions ensures accuracy of the information. After the revisions, the team document the plan as the final model. The final model is used to apply the process in the business, educate employees, and ensure the internal compliance.

Quick Tip:

Utilize process mining and document automation for easier, faster and precise process plans.

Further Reading

Explore how to implement process mining and other process intelligence tools to plan and improve your business processes, check out our articles:

If you want to apply process mining for process planning but do not know where to start, you can review our comprehensive and data-driven vendor lists.

Efficiently plan of your processes by managing them with top workflow and process management tools such as:

Assess different vendors with a transparent methodology yourself by downloading our checklist: 

Get Process Mining Vendor Selection Guide

And, if you believe you need more help:

Find the Right Vendors
Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Hazal Şimşek
Hazal is an industry analyst in AIMultiple. She is experienced in market research, quantitative research and data analytics. She received her master’s degree in Social Sciences from the University of Carlos III of Madrid and her bachelor’s degree in International Relations from Bilkent University.

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