Production Optimization

Introduction

What is production optimization?

The term production optimization refers to the evaluation and improvement of processes, resources and technologies in manufacturing companies. Generally, the goal is to increase efficiency, productivity or product quality. Other possible objectives include increased safety, innovative capacity or reputation.

The reasons for optimization projects can be diverse: Acute problems or inefficient processes, increasing competitive pressure or the need to integrate modern technologies are just a few examples.

In this article, we start by covering the most important goals, strategies and methods of production optimization. We then go into detail on five examples of possible optimization approaches.

Objectives

Objectives of production optimization

A particular challenge in production optimization lies in the balancing of different objectives and the definition of priorities. This is because different optimization goals can compete with each other, as the following examples illustrate.

Examples of competing objectives:

• Shortening set-up times by stringing together similar orders might only be an option if delivery reliability does not suffer as a result.
• Shortening the throughput time by using more efficient machines may lead to significantly higher costs (machine hourly rates).

Possible objectives in production optimization include the following:

• Increase production output
• Reduce costs (e.g. energy, material or personnel costs)
• Improve product quality
• Reduce production times/throughput times
• Increase the degree of individualization of products (adaptation to the needs of individual customers or target groups)
• Increase flexibility (ability to react better to current developments)
• Increase innovative capacity (e.g. ability to integrate new technologies without too much effort)
• Enhance reputation with customers and partners (e.g. through high product quality and adherence to delivery dates)
• Ensure security (e.g. workplace and IT security)

While targets such as production output and operating costs are primarily subject to economic calculations, others such as safety and adherence to deadlines must be met to a high degree.

Assessment

Identifying possible optimizations in production

Optimization projects begin with an assessment of the status quo and the identification of potential for improvement.

As production environments and processes are extremely complex, a common approach used in practice is the continuous improvement process (CIP). This describes a strategy in which individual optimization potentials are identified and usually processed step by step. This also has the advantage that acceptance among employees is generally higher – especially if they are involved in the optimization process.

The continuous improvement approach is often used synonymously with the originally Kaizen concept, which describes a permanent questioning and optimization of the status quo. Kaizen was defined in Japan in the 1950s and established itself a few decades later as a CIP management approach in the western economy.

Strategies

Optimization potential in production can be identified in a variety of ways. The following list contains just a few suggestions. We will go into some of them in more detail in the practical section of this article.

• Process mapping: The detailed visualization of production processes and value chains helps to understand all steps and interactions. For example, flow charts or value stream mapping can be used to identify activities that do not contribute to value creation.
• Analyzing production data: Analyzing large volumes of production data (e.g. with the help of artificial intelligence) can provide information about bottlenecks, wasted resources or causes of system failures and product defects.
• Recording and evaluation of key metrics: Companies can compare production metrics and KPIs (e.g. throughput, scrap rate, energy consumption, …) with industry benchmarks or other production sites. This helps to uncover weaknesses and find the areas in production where improvement would have the greatest impact on company performance.
• Simulation and scenario analysis: Depending on the project, companies may be able to use special methods or software to simulate the effects of changes.

Feedback from employees also often serves as a source of inspiration for optimization, as does research into new technologies at trade fairs or via other sources.

Manufacturing companies in particular benefit from an open and targeted feedback culture.

Strategies & Methods

Strategies and methods for production optimization

The optimization of production-specific processes and structures, as well as general management processes with an impact on production, has been a major focus of scientific research for decades.

Numerous concepts and methods have been developed to structure optimization projects and embed them in an overarching strategy. Some of the best known and most important of these are presented in this section.

Lean Production

Lean production is a sub-area of the overarching lean management concept and its application to the production area.

The aim of lean production is to maximize efficiency in production. Unnecessary tasks should be avoided, as should the waste of material resources.

The main focus is on reducing complex and confusing processes and hierarchies as well as continuously improving product quality.

The aforementioned concept of the continuous improvement process (CIP) is also highly relevant to the lean production approach: A permanent improvement process is to be achieved by involving employees more closely and promoting teamwork and personal responsibility.

The leaner and clearer the production process, the easier it is to coordinate with warehouse logistics and supply chains, identify and rectify errors and implement further optimization projects. Increasing adaptability is also one of the most important goals of the lean production concept. Products should not only be subject to constant quality improvements, but should also be better adapted to specific customer and market requirements.

The implementation of lean production is not only an optimization objective, but also creates an environment in which production optimization can be carried out more easily and effectively.

Six Sigma

The Six Sigma method aims to monitor and improve processes using statistical tools. This usually involves going through the five phases Define, Measure, Analyze, Improve and Control.

The method also defines a hierarchy of roles (e.g. “Black Belt” and “Green Belt”), which characterize the level of expertise and responsibility of those involved in the project. Corresponding skills can be acquired as part of certification programs.

Total Productive Maintenance (TPM)

Total Productive Maintenance (TPM) is a comprehensive maintenance concept aimed at maximizing plant productivity and operational efficiency.

The ultimate goal is to achieve fault-free, accident-free production that is as environmentally friendly as possible.

To achieve this, operators are involved in the maintenance and care of machinery. TPM focuses on transferring skills so that employees can take on maintenance tasks independently and identify potential problems and optimization potential at an early stage.

TPM is based on eight pillars:

• Autonomous maintenance
• Planned maintenance
• Quality management
• Training and education
• Integration into early management phases
• Integration into administrative processes
• Safety, health and environmental protection
• Integration into the supply chain

A particular focus of the Total Productive Maintenance concept is the evaluation of key metrics such as Overall Equipment Effectiveness (OEE). These form the basis for determining and evaluating plant productivity, performance, etc.

Theory of Constraints (ToC)

The Theory of Constraints (ToC) is based on the assumption that the overall performance of a production system (e.g. a production line) is limited by a limiting factor or bottleneck.

This could be for example a single line whose speed limits the possible throughput time or the possible production output.

The Theory of Constraints approach aims to identify and eliminate such constraints.

It defines five focus steps that are performed in sequence in order to optimize a weak point:

1. Identification of the bottleneck
2. Optimal (maximum) utilization of the bottleneck
3. Align production with the bottleneck
4. Elimination of the bottleneck
5. Identification of the new bottleneck

Just-in-time production

Just-in-time production is a strategy in which production is matched as precisely as possible to actual demand (or more specifically, existing orders).

Materials used in production are delivered exactly when they are needed, minimizing stock levels and thus capital commitment.

Just-in-time production requires almost perfect coordination of the supply chain and production. It is used frequently in the automotive industry where components are processed in numerous variations and need to be delivered flexibly to the right place at the right time.

Optimizations

Optimizing production – 5 practical approaches

The following part of this article presents five practical optimization approaches.

Each of these addresses different areas, processes or tasks in production:

• Minimize time for troubleshooting
• Automate recurring tasks
• Make communication between teams more efficient
• Utilize machines and devices as data sources
• Optimize interfaces for production control

1. Minimize time for troubleshooting

Production downtimes can result in significant costs – especially in cases where they are not resolved promptly.

While the complete avoidance of downtime is already an important goal in production optimization, strategies for dealing with short-term disruptions must also be in place.

One of the most important steps when dealing with disruptions is to identify the exact cause of the problem. Once the cause is known, personnel can initiate the necessary repairs, configuration adjustments or similar measures.

In order to speed up the diagnostic process, access to all relevant machine and process data should be as quick and uncomplicated as possible. Mobile and cross-team access options that are available to all relevant persons to the extent required can result in significant time savings.

Depending on the type of error source, companies can significantly accelerate rectification or repairs by utilizing remote diagnostics and autonomous interventions by trained machine operators.

In addition to the recording and documentation of machine and process data, clearly defined responsibilities and simple communication channels are among the basic prerequisites for optimized handling of failures.

Possible solutions

Cloud applications with mobile access for multiple teams offer a way to speed up error diagnosis and resolution. By configuring user authorizations, companies are able to define exactly who has access to which data and control options.

However, one prerequisite is that all relevant data is recorded, structured and correctly displayed. Configurable visualizations and key metrics can help to identify sources of error.

A more effective data visualization can lead to significant time savings.

Automated notifications can be used to alert technical personnel without delay and proactively transmit diagnostic data. Companies can set up user-defined alerts that may be triggered either by specific events, value changes or manually at the push of a button.

Almost all of the procedures described above also support the early detection and prevention of problems in production.

Further steps to optimize troubleshooting:

• Use of diagnostic tools, sensors and other tools (often designed for the respective system).
• Monitoring of stock levels to ensure that frequently required spare parts are always in stock. Visualizations and notifications can also be used in warehouse management.
• Evaluation of collected data (e.g. on previous failures) in order to identify the causes of errors and avoid them in the future (root cause analysis). Artificial intelligence can be used to search through large amounts of data and find correlations.
• Conducting or commissioning training programs that impart the necessary troubleshooting skills – for example, when new hardware or technologies are introduced. Concepts such as Total Productive Maintenance recommend involving different teams in the maintenance process. Machine operators should also be made aware of possible error causes.
• Implementation of a knowledge management system that records known problems and solutions in an easily accessible database.

2. Automate recurring tasks

Automating manual tasks and recurring processes is one of the most effective ways to increase productivity and efficiency.

When used correctly, it frees up teams to use the time gained more profitably elsewhere. Errors resulting from manual input can also be eliminated, potentially reducing the scrap rate and error frequency.

In addition to physical processes like the transport of materials, production steps such as cutting, drilling or welding as well as packaging and labeling, production-related digital processes can also be automated. These include the transmission of work orders, the recording and evaluation of production data and even the procurement of materials.

Examples of processes that can be automated:

• Database operations: Centralized collection of sensor data and automatic report generation
• Data transfers between machines, devices, software systems and cloud/IoT platforms
• Information-rich notifications by email or SMS: Real-time notification of machine problems, deviations or critical events as well as alerts on wear and tear data
• Automatic documentation of processes such as maintenance and cleaning
• Real-time data visualization and calculation of key figures
• Automated ordering based on current inventory data and the requirements determined in production planning
• Automated energy management

The adaptability of such automation is both a challenge and one of the greatest advantages. If a company is able to adapt automated processes to current requirements at any time and without excessive time or financial investment, production achieves a high degree of flexibility.

The aspect of IT security is crucial when implementing automation solutions since new interfaces may also create new areas of vulnerability. The use of secure protocols and technologies, regular updates and maintenance as well as employee training are important measures for the secure implementation of digitalization and automation projects.

Possible solutions

Workflow automation software can be used effectively in many areas of production. Common solutions allow businesses to combine individual processes (often in the form of modules) into complex workflows.

Our cloud platform manubes provides a Workflow Designer that is specifically tailored to industrial production. Users are able to combine ready-made blocks into workflow through a simple drag-and-drop editor. Automations can be controlled using triggers and flexibly adapted at any time. Like all other manubes features, the Workflow Designer follows a no-code approach. This allows end users in production to be involved in the development of individual automation solutions.

Workflow automation solutions such as manubes do not require any programming knowledge.

3. Make communication between teams more efficient

Complex production processes, global supply chains and effective warehousing require close coordination between numerous responsible parties.

Modern production environments feature numerous teams and roles, some of which are listed below:

• Production management and shift supervision
• Production planning and control
• Maintenance and operating technology
• Logistics management and warehousing
• Personnel management and shift planning
• Health and safety
• Quality control
• Research and development
• IT and digitalization
• Finance and controlling

Setting up structures that enable all these teams to work together efficiently is a complex management task. At the same time, optimizations in this area can lead to many positive effects.

Clear and structured communication channels save time and increase employee productivity. The transparent exchange of information can help avoid misunderstandings and resulting errors, while also building trust among employees.

Maintenance teams are able to react more quickly to disruptions, warehousing can be optimized through closer coordination with production planning and new machines or software solutions can be put into operation more quickly. The company-wide availability of data and regular exchanges between teams allow new insights to be gained, safety risks, bottlenecks and quality deficits to be avoided and the company’s innovative strength to be increased.

The larger and more complex a production environment, the more challenging it is to optimize and standardize communication channels. In practice, companies may utilize the continuous improvement process (CIP) approach, in which unnecessary and inefficient structures are gradually dissolved or replaced.

Possible solutions

One of the most effective ways to optimize company-wide collaboration is to establish common platforms.

Cloud platforms in particular offer the opportunity to manage data and processes in one place that is accessible to a wide variety of teams – usually mobile, location-independent and with the option of defining authorizations.

The specific use cases are diverse: Information can be visualized in the form of dashboards, employees can collaborate in shared workspaces or send preconfigured notifications at the touch of a button.

Using forms, QR code scans or similar methods, production-relevant data can be recorded on site and immediately made available in the cloud. And last but not least, big data analyses and artificial intelligence offer the opportunity to speed up processes and gain new insights.

4. Utilize machines and devices as data sources

Enabling the transfer of data between machines, devices and software systems is a core component of the Industry 4.0 concept.
Aside from utilizing data to monitor production and gain various insights into existing processes, complex automations are also only possible if the appropriate interfaces are available and systems can communicate with each other.

Production-related data sources include machine controllers, environmental sensors and robots, as well as many other devices such as 3D printers, energy meters and cameras.

These provide a wide variety of data:

• Operating times
• Cycle times
• Production volume
• Status information
• Error messages
• Energy consumption
• Maintenance requirements
• Temperature
• Air humidity
• Pressure

These and other data can be used to monitor production, indicate maintenance requirements and form the basis for both short-term and long-term optimization and management decisions. They may also be utilized in automations in a variety of ways.

Collecting data is a prerequisite for benefiting from technologies such as big data analytics, artificial intelligence and digital twins.

Possible solutions

In order to capture data from production and process it using suitable software solutions, appropriate interfaces and protocols are required.

In order to avoid high project and maintenance costs as well as documentation requirements for individual solutions, it is advisable to focus on standard interfaces. OPC UA is particularly widespread in the industry. The OPC Foundation’s communication standard enables uniform data exchange between a wide variety of machines and devices on the one hand and systems such as SCADA, MES, ERP and cloud applications on the other.

Another approach is the use of IoT gateways, which act as a bridge between non-connected machines and the company’s IT network. These collect data through different machine protocols and provide them via standardized formats, enabling data transmission to databases, cloud platforms or similar. The lightweight MQTT network protocol is often used for this.

Manufacturing companies should consider the required interfaces when selecting software systems. Many modern software solutions, such as SAP, now support the OPC UA standard.

Our cloud platform manubes also relies on standard industrial interfaces. In addition to common database interfaces (see e.g. Microsoft SQL Server), it supports the IIoT standards OPC UA, MQTT and REST.

5. Optimize interfaces for production control

Production control is one of the most important functions in production. It is responsible for ensuring that the results of production planning are put into practice correctly.

The most important subtasks of production control include the detailed planning of production orders and their short-term adjustment (which machines are assigned to which orders), order authorization and transmission, as well as monitoring production processes and intervening in the event of deviations.

In order for production control teams to work optimally, they need real-time access to all relevant data as well as effective and secure options for intervening in production at short notice.

Ideally, the following structures are in place:

• Overview of all relevant key figures and visualizations in real-time
• Mobile access to monitoring and control options
• Alarm systems that send immediate notifications in the event of deviations
• Secure and user-friendly interfaces to machine controls
• Access to historical data

Possible solutions

Manufacturing Execution Systems (MES) are the classic instrument of production control. They are usually connected directly to automation systems and record extensive operating and machine data. Control options can include assigning and transmitting orders to machines, tracking order progress, managing materials and tools and adjusting machine parameters.

MES can be implemented in a wide variety of ways: In addition to complete solutions from larger providers, there are MES that specialize in specific industries. Alternatively, individual software systems or components can be combined to form an overall system, allowing company-specific requirements to be implemented even more flexibly.

Our manubes platform offers companies the opportunity to build a powerful, flexible and future-proof MES in the cloud. In addition to supporting industrial standard interfaces, manubes includes tools for data modeling and real-time visualization of production data. At the same time, control processes are simplified: Instead of complicated Excel lists, teams can utilize simple input fields and forms linked to automations that enable direct data transfer to control systems or databases. Processes can be modeled as workflows that either run automatically in the background, react to specific triggers or are initiated manually.

With the help of no-code-based design tools, end users in production can rapidly develop their own solutions:

• Configurable dashboards
• Workflows to automate data transfers, notifications and other processes
• Options for mobile data capture
• Combination of order overviews with direct control options

Would you like to test production control with manubes for yourself? Create a free trial account with access to all the features of our cloud platform.