The most popular process improvement methodologies in the digital era

As companies innovate and adapt to new digital technologies, their processes need to be adapted too. But before reviewing your processes, it’s necessary to understand what the different process improvement methodologies are and which ones are most suited to your organization. This blog outlines some of the more well-known methodologies and some newer ones designed for the digital era.

[joli-toc]The main goals of continuous process improvement are minimizing errors and waste, improving productivity, performance and quality, and streamlining the efficiency of a company’s internal and external processes. Knowing which lean process improvement methodology to use will help your organization meet customer needs in today’s fast-paced world. The right tools and methodology will also help you understand how your organization functions and compares against your competitors.

 

Popular process improvement methodologies

Here are seven common process improvement methodologies, each one accommodating a different need.

 

1. Lean

The central idea of Lean manufacturing is to minimize waste and overproduction and to maximize customer value. As the most widely recognized and commonly used methodology, the constant quest is for perfection through root cause analysis, optimizing the flow of all parts of machine production and human work, and finetuning every logistics point in the value stream.

 

2. Six Sigma

Like other process improvement methodologies, Six Sigma helps reduce costs by saving time and effort and achieving total customer satisfaction, but aims to prove improvement through statistical measures and evidence. The bottom-line return on investment (ROI) is seen as a litmus test for decision-making. The lean Six Sigma methodology for process improvement also incorporates three powerful problem-solving frameworks:

• DMAIC, which drives process improvements
• DMADV, a unifying methodology for creating new processes and products
• Cause and Effect Analysis (also known as the Ishikawa diagram or fishbone diagram) is a visual mapping technology used to analyze existing processes

 

3. DMADV

In use for over three decades in various industries, including manufacturing, services and transportation, DMADV (Define, Measure, Analyze, Design and Verify) is experiencing somewhat of a revival. It’s an important Design for Six Sigma (DFSS) framework that supports the development of new products, services or processes. The framework aims to ensure the optimum balance between three perspectives – customer needs, the process or procedure to fulfil these needs, and the company’s objectives. It’s a handy tool when implementing new strategies because of its early identification of success, basis in data and thorough analysis.

While some of the steps are similar to the better-known DMAIC process, there are distinctions:

Define:		In this step, you’ll state the problem, specify the customer deliverables, identify the project goals, and outline the target process.
Measure:		This step is about establishing the factors that will be important to the customer during the design of a new product or process. These factors are subsequently linked to quality and the development of critical to quality (CTQ) criteria.
Analyze:		During this step, you will develop design alternatives and determine the optimum combination of requirements. The step aligns closely with the ‘‘Measure’’step.
Design:		Here, you document the detailed process that meets customer requirements.
Verify:		In this step, you validate that the newly designed product or process meets the customer’s needs.

4. SIPOC analysis

SIPOC stands for Suppliers, Inputs, Process, Outputs and Customers. It happens during the “Measure” stage of DMADV or DMAIC and helps companies define and establish a process improvement project (PIP). It also helps identify requirements before starting the improvement project. When you create a SIPOC, begin with the name of the process you wish to analyze and write down its essential steps. Make sure you know where the process starts and where it ends. You’ll also need to identify your customers and specify the outputs of the process. You can use a SIPOC diagram to understand precisely who the suppliers of each given process are and how your inputs should function.

 

5. Supply chain optimization

Supply chain optimization (SCO) focuses on raw materials, input process goods and finished inventories that comprise the physical “supplies” in the chain. A feature of supply chain optimization is mathematical modeling software tools to identify inefficiencies such as bottlenecks. It also highlights opportunities for customer segmentation, while scheduling, inventory and information optimization aim to eliminate excess costs from the supply chain.

 

6. Hoshin Kanri

Also called Policy Deployment, Hoshin Kanri is an essential Lean management method to ensure a company’s strategy gets executed across the hierarchy. The Japanese words “hoshin” and “kanri” mean “direction” and “management”, respectively. The focus of the improvement method is to achieve a unified direction to align manufacturing operations with the organization’s strategic objectives. The method has built-in continuous improvement mechanisms, a key element of its success. These are known as Catchball and PDCA (Plan-Do-Check-Act) tools.

 

7. Total quality management

As the name indicates, Total Quality Management (TQM) emphasizes quality: The methodology is a management framework driven by a pursuit of ongoing quality improvement and customer satisfaction. This customer-focused approach demands accountability throughout all levels of operations. Customers are sometimes defined internally as both a further quality control and a customer-centricity simulation. Recognized as a generic management tool, TQM is just as applicable in service and public sector organizations.

 

 

Process improvement methodologies and tools for modern organizations

In today’s digital era that demands innovation and agility, a newer breed of continuous process improvement methodologies and tools is emerging – enhanced by technologies such as predictive analytics, 3D printing and digital dashboards. Potential production issues can be identified much faster when manufacturing organizations leverage continuous process improvement tools and modern technologies.

Some of these newer methodologies have their roots in the well-known approaches listed above. 

 

1. Digital twin and simulation

These two concepts both use virtual model-based simulations, but they differ slightly. Simulations are used across industries to test products, systems, processes and concepts. The simulation works by introducing and testing different variables into the digital environment. However, while traditional computer-aided design and engineering (CAD-CAE) simulation capabilities are great for product design applications, simulation offers less than a digital twin.

A digital twin is a virtual model created to reflect an existing physical object accurately. Sensors are attached to the object, such as a wind turbine, to produce data about different aspects of the object’s performance. The data, primarily collected through the Internet of Things (IoT), is then relayed to a processing system and applied to the digital model or twin.

The two-way flow of data sets between the asset and the digital twin, backed by real-time data, enables a user to see how the product is operating in real time. The digital twin can then generate potential improvements to the physical asset. A digital twin can also be created for non-physical processes and systems to run simulations based on real-time data.

 

2. Process mining

Still a relatively new discipline, process mining applies data science to discover, validate and improve workflows. Software is used to mine log data from a vast range of workflow systems by applying algorithms to map the sequence of activities and processes involved in a process flow. By combining data mining and process analytics, companies can better understand the performance of their processes – revealing bottlenecks and other areas for improvement. The algorithms can also uncover opportunities to incorporate robotic process automation into processes, accelerating a company’s digital transformation initiatives.

Although much of the work around process mining centers on the sequence of activities, the other perspectives also convey valuable information for management teams. Organizational perspectives can show up the various resources within a process, such as job roles or departments. The time perspective can reveal bottlenecks by measuring the processing time of different procedures within a process. This can reduce costs but can also drive more innovation, quality and customer retention.

 

3. Business Process Modeling Notation

Business Process Modeling Notation (BPMN) can be thought of as a graphic programming language for process mapping. It uses a flowchart to represent all the different steps of a planned business process from end to end, offering an overview of the sequence. Icons have a defined use and meaning with each representing a step or activity in the process.

The process modeling overview covers all the information flows and company activities needed to complete a process, showing the relationship and connection between intention and the implementation of each sequence. A standard BPMN diagram allows companies to communicate the process in a standard manner, thereby fostering collaboration and understanding between executives, analysts and technical implementation personnel.

 

4. Agile methodology

An agile approach follows an iterative way to project management by breaking the project into several phases. It incorporates team feedback that highlights problems and successes to enable continuous improvements at every stage. Furthermore, an agile methodology improves communication and collaboration among the stakeholders and makes it easier to adapt to change.

As far as methodologies for process improvement are concerned, the agile methodology involves optimizing a development process, whereas the Lean approach focuses on optimizing a production process. Both have proven their worth as integrated systems for helping improve performance.

 

5. Theory of Constraints

The Theory of Constraints (TOC) is a methodology for identifying the most important limiting factor that prevents a goal from being achieved, and then systematically improving that constraint until it is no longer the limiting factor. Conceived by Dr Eliyahu Goldratt in 1984, it’s a highly focused technique for creating rapid improvement.

The methodology follows five steps:

• Identify the constraint
• Exploit the constraint
• Subordinate and synchronize the constraint
• Elevate the performance of the constraint
• Repeat the process

The five focusing steps are a continuous improvement cycle – once a constraint is resolved, address the next one immediately. This is a reminder never to become complacent.

 

The five focusing steps to identify and eliminate constraints

 

An integrative approach to process improvement

Blending the best learnings and practices from all established continuous improvement tools and process improvement techniques allows the organization to unlock greater value and optimize profitability.

It is important to upgrade business process improvement methodologies to gain the full potential of digital technologies. The principle of continuous improvement remains imperative though as improvement means doing better. A fresh approach, an integrated continuous improvement model where process improvements include everyone and impact the whole business, will yield even better results.

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