6.06 assignment manufacturing design and optimization

• Design World

Strategies to Optimize Product Designs for Manufacture

By Ryan Gray, Managing Partner, and Mike Witt, P.E. Engineering Director with SGW Designworks | January 9, 2017

Optimization for manufacturing is a critical, and all-too-often neglected element of the product design cycle. As product development professionals, we often tend to focus on the sexier aspects of a project: defining and validating feature sets, design, prototype development, and functional development. But ignoring manufacturability can lead to sporadic quality issues, delays in initial shipments, COGS that exceed pro-formas, and even complete rebuilds of production tooling.

So what exactly is manufacturing optimization? Manufacturing optimization, when executed properly, results in a final product design that not only provides the features and functions demanded by the market, but is also compatible with the manufacturer’s equipment and processes, at the volumes needed. A critical element to successful manufacturing optimization is engagement of the actual manufacturer that will make the product in the development process.

Focus on optimization for manufacture should start as early as the concept development. In a product that is entering a less-competitive market space, production cost may be less important. In these cases, the feature set should be the primary focus of early stage work, however, it is still important to avoid concept choices that preclude realistic manufacturing plans.

One of the first considerations for a design optimized for production are the materials, components, and technology that are incorporated into the product. For example, plastic injection-molded part design requires attention in several key areas as well as an understanding of what the manufacturer is capable of doing.

Generally, injection molding of plastic works by pressing two sides of a mold together and injecting the plastic under high pressure. However, parts must be designed with appropriate angles on all faces, called a draft, which aids the finished part in releasing from the mold. This draft must be considered across the entire surface of the part with consideration for both sides of the mold, also called tooling.  

Features like texture are common on plastic parts. Texture provides more material to the part, slightly boosting its strength as well as adding a nice aesthetic. Even so, texture requires considerations be made in the design for it to be produced correctly. It also adds to the cost of the tooling.

If a part needs overhangs, texture, or other more complicated features, the mold becomes more complicated as well, increasing the overall tooling cost. Despite the added cost, some of these complicated features are necessary in a product.

Here is an example of a project that included manufacturing optimization at the appropriate levels, where our company developed an enterprise dock for handheld PCs.

The first obstacle was that the selected manufacturer was located overseas. To this end, a lead engineer with years of experience and a very strong knowledge of plastic part design was selected to bridge potential communications gaps. This was a risk identified early on and addressed just as early. In the preliminary design review, the manufacturer, having already been selected, was engaged to address potential problems.

In this particular project, it was clear from the concept stage that conventional manufacturing processes would be suitable for the product. The product did need additional features that were key to the value of the project. These features included a retention system that required the use of slides that move perpendicular to the draw direction of the injection mold tooling to produce the part correctly, as well as modifications of the original design to accommodate the slides in the part geometry. This meant changes to the design of the part and higher tooling charges, but changes and cost that were identified early on.

In the example above, three decisions led to smooth and efficient optimization for manufacturing:

• A manufacturer was selected early in the process — in this case, before development even began.
• The development team was led by an individual who provided the design vision, as well as expert knowledge in manufacturing processes.
• In addition to validating use-case and feature set, the development team engaged the manufacturer early and often to ensure the development output was compatible with the specific capabilities and limitations of the manufacturer.

In a second example, manufacturing optimization occurred in a similar way — this time for an IoT-enabled coffee grinder — but at different times to accommodate a Kickstarter campaign, which funded the development.

Kickstarter and similar platforms provide a unique means to validate a concept and generate funding for production, and generally a working prototype is required for a product-centric campaign. The prototype only needs to demonstrate the core functionality of the product. This means manufacturing optimization does not need to be included at all in early-stage development. And the prospect of reducing early-stage development costs by using the platform is an attractive option for emerging businesses.

Even before the development for the grinder project began, the client company and the development team agreed that manufacturing optimization would intentionally be pushed to late-stage development.

This approach to manufacturing optimization would allow for fast development of a prototype incorporating the key functionality for minimal cost. All parties recognized that the net development dollars to a manufacturing-ready product would increase because of this choice. In this case, delaying optimization was a valid business decision, with some risks and some rewards.

Ultimately the Kickstarter campaign was successful and the project was now driven partly by a delivery target date. As with the last example, the manufacturer was engaged early to address potential challenges. And in this round the manufacturer did have specific needs which required significant iteration to the product’s internal geometry and materials.

The product design started to diverge from the prototype that had been developed for the Kickstarter campaign. Many system elements had to be completely redesigned, adding cost and time to the project, as anticipated when the decision was made to forego manufacturing consideration in the initial design. As a result of this planning, none of these extra costs and time came as a surprise to the client, and this is a key aspect of this strategy.  

In this example, some of the things that led to higher cost optimization for manufacturing were:

• To minimize time and cost to get to an initial functional prototype, manufacturing optimization was not included in early development.
• The manufacturer selected had specific requirements, which were more expensive to address in late-stage design than they would have been in the early stages. This included the development of custom PCBAs which had not been integrated in earlier iterations.

Unlike the PC dock, the implementation of manufacturing optimization later in the grinder was suitable because of the way it was developed. These examples show two different routes to take to a successful optimization for manufacturing.

To sum up, careful evaluation of manufacturing risk relative to feature sets and function is a starting point that can determine how much to focus on manufacturing optimization in initial development work. Dedicated focus on optimization for manufacture in mid and late development, including direct collaboration with manufacturers, can reduce risk in multiple areas. This strategy for product development will also help avoid unexpected expenses since the stages and conditions that will require additional spending are identified early on. 

This article originally appeared in the November/December print issue of Product Design & Development.

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Academics   /   Courses   /   Course Descriptions MECH_ENG 441: Engineering Optimization for Product Design and Manufacturing

Prerequisites, description.

The course is designed to provide engineering students a view of optimization as a tool for engineering decision making. Students will be given a fundamental introduction to the optimization techniques and an opportunity to learn how to model product design and manufacturing problems and solve them using computer-based (numerical) optimization techniques. Students will be encouraged to relate the course material to their research.

Required Textbook

Introduction to Optimum Design, Arora, J.S., 2004, Elsevier Academic Press, 2nd edition. ISBN 0-12-064155-0.

Reference Materials

Engineering Optimization: Methods and Applications, Reklaitis, R.R., 2002. Optimization Concepts and Applications in Engineering, Belegundu, A.D., 1999. Quality Engineering using Robust Design, Phadke, M.S., 1989.

• Introduction to engineering optimization
• Formulation of optimization models
• Linear models and solution techniques
• Unconstrained nonlinear models and solution algorithms
• Constrained nonlinear optimization
• Discrete and mixed integer models and techniques
• Shape and topology optimization
• Computer experiments and metamodeling
• Optimization under uncertainty
• Multiobjective & Multidisciplinary optimization

Homework (35%), quiz (30%), term project (25%written + 5% presentation), class participation (5%)

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Build better handbook, case studies, what is manufacturing optimization (and why should you care).

Joel Garcia

Product design and manufacturing optimization is a messy business. Glues, screws, and the ever-shrinking PCB has made it harder and harder to build something remarkable that stays  remarkable for more than one lifecycle.

Not only are products getting more complex, but the process to build them at scale is getting more complicated. COVID-19 has disrupted travel and supply chains the world over – yet the pressure to launch new products has only gotten higher. In fact, the only thing higher would be consumer expectations – which demand pure perfection in exchange for mercy in their reviews.

As electronics brands compete to grow – or even retain – their existing market share, they’ll need to reimagine the way they build if they want to be first to market with a new product.

And being  first in line  requires being  the best on the line  – and it starts with manufacturing optimization.

Manufacturing Optimization  is a holistic discipline that enables manufacturers to get from proto to mass production and beyond as quickly as possible, with as little waste as possible. It’s a data-driven embrace of  a better way –  one that leverages emerging technology built on the powerful shoulders of  math .

It’s also not new. Major hardware brands have always looked to optimize their build processes, typically in the form of large one-off consulting engagements. A brand might invite an expert team in to analyze their end-to-end product development and manufacturing processes and recommend a list of opportunities to improve, to be executed over months or years following the audit.

Today, with the expansion of IoT connectivity in factories and cloud platforms that provide data access and transformation, teams are increasingly able to take optimization into their own hands, driving  continuous  – rather than instantaneous – optimizations at the level of individual processes, whole factories, or even across a complex supply chain.

To put it less whimsically,  manufacturing optimization means using data to go faster . Faster at finding defects, faster at validating solutions, and faster at maturing products. Faster where it counts.

“Manufacturing optimization is the practice of using data to build better products, faster, with the goal of being more competitive.”

Below we’ve broken down manufacturing optimization into three components: Product design optimization, ramp and MP optimization, and supply chain optimization.

Product design optimization

Product design optimization is a focused subset of optimization which takes into account the shape, size, component assembly, desired functionality, and consumer lifestyle of a market need in order to build the most effective device possible to meet the demand.

As manufacturers look to build new products to introduce to post-COVID market, designs will need to be smarter and processes more efficient to lower costs and reduce rework that may waste limited supply resources. They must also reach for unprecedented functionality to outpace competitors in their market and establish undeniable demand for the product.

This requires that engineers analyze their product design to assess if components are too close together or difficult to place facing the same direction, if placement of functional parts are too close batteries or overhangs, if component fragility is a potential problem, etc.

A product designer must predict the needs of the manufacturer so that both market needs and the manufacturing process are achievable. Their goal is to minimize operational time and cost, eliminate needless material that add weight, reinforce weak areas that may fail or cause issues in the field, and other  design-specific  defects. This can include deciding between glues and screws, whether or not to hand solder, or the size of your coax cables.

Here are a few new approaches to consider:

• Find more issues in EVT builds.  If you focus on getting a full catalog of issues, you’ll realize that certain design features create clusters of similar issues that might be better resolved through a broader design change.
• Operate against the entire pareto.  Most product teams are just scrambling to fix the obvious issues that happen. Those are often not the most critical or nefarious. Change your mindset to find every issue as early as possible. Putting more effort here (or deploying an optimization tool to do so) will save weeks by allocating resources to solve issues more efficiently.
• Focus on traceability.  Make sure you have the right data ahead of time, it will save you time later.

Ramp and production optimization

Product design optimization occurs during the EVT and DVT part of the build process. As units are produced and failures occur in PVT and approaching ramp and MP, the hunt of  process-specific  defects begins – and can sometimes be the most costly piece of the puzzle.

This requires product data – photographic, functional test data, and any other proprietary components be tracked with the ability to segment in order to find correlations across lines, suppliers, field engineers, etc. Most teams currently use spreadsheets or other intelligence tools to aggregate and assess these data types, though the information is often siloed or localized making it hard to work cross-functionally.

Once this product data starts streaming in, it must also be shared virtually so that teams can collaborate more effectively no matter where they are. Visual inspections and defect analytics transcend language barriers and timezones, creating a more cooperative environment from brand to CM.

• Test for every known issue.  Modern tools beat out antiquated AOI systems by making it easy for you to set up dozens of tests, even for very infrequent issues. As an operations lead in charge of ramp, you should expect 360˚ visibility into every issue so that you can  choose  to manage risk
• Nail down quality differences from different suppliers.  Often parts come from different suppliers and may vary in quality or fabrication, and you don’t know if that causes issues until later. By adopting a data-forward approach, you can quickly correlate downstream issues to these types of differences.
• Improve remote collaboration.  Modern tools like Instrumental capture photographic data by sku and can import functional test data and measurements in order to derive correlations with failures and returns. Links for specific units or groupings of units by defect type or failure can be shared remotely to any teammate for immediate action.
• Assess variable operator performance.  How are people being trained? Are shifts too long? Are procedures too difficult to teach?
• Iterate across generations.  Using a new manufacturing optimization platform that can aggregate data streams across builds can eliminate common problems altogether if certain learnings can be applied.
• Stabilize the assembly line environment.  Are you keeping tracking of variable setups that may be impacting performance? Different types of jigs, tools, or lighting used to perform assembly steps can cause minute issues with larger impacts downstream.
• Discover unforeseen issues unrelated to the product.  Some issues happen agnostic to the inner workings of the product. Shipping and packing may come to be an issue is assemblies are too long or if too many failures occur.

Supply Chain Optimization

Supply chain optimization once could be simply defined as operating a supply chain at peak efficiency. Today, supply chain optimization has an asterisk for operating at peak efficiency  and   resiliency  as well.

The global health crisis has really brought to the forefront how brittle the supply chains that we’ve built in the electronics industry have become. This brittleness wasn’t intentional – but it has become the downstream result of reactive processes. Socio-economic pressures like tariffs and changes in globalizations have made these supply chains longer and spread out over more geographic reasons and 2020 showed us how brittle it truly was.

As we rebuild the supply chains to accommodate a post-pandemic future, organizations will need to continue striving for the same goals but within a new context.

Organizations will still need to negotiate for discounts on volume orders, but there also needs to be a reliability and predictability with what is delivered and in what state it is delivered. They will also need to continue finding better and more competitive suppliers to drive down their own costs, but in order to ramp new suppliers they will also need to build reliability measures to coincide with any new partnership.

In order to make the supply chain more resilient, you have to be able to monitor the “health” status of your supply chain in real-time, in order to eliminate downstream consequences. Optimizing geographically (where are different parts and assemblies built relative to where they will be packaged or sold) will also be critical – as different countries will have different responses to any future resurgence of the pandemic.

• Operating against the entire Pareto.  Focus on identifying and characterizing every issue impacting yield. This helps you figure out which issues are worth fixing, and which ones to fix first.
• Factor in customer sat.  Most electronics brands think of returns / sustaining engineering separately from their supply chain costs, and find it difficult to connect the dots between how bad reviews from defects should factor into their per-unit cost of building – but they do! You need to think about the cost of field failure the same way you think of accounting for defective parts from a supplier (except in many cases the impact is orders of magnitude bigger).
• Look upstream . Most attention is focused on the final assembly line, often run by CM’s, but many defects happen before the parts ever arrive on the line. Supply chains, however, are often a complete black box when it comes to data. Technology like Instrumental allows you to collect and analyze data at upstream suppliers just as easily as in the final assembly plant. The benefits range from earlier detection of issues to better relationships with your CM/process team.

It’s a new world for electronics brands. In a world full of chaos, the last thing someone wants is to buy a product that they can’t trust. Apple will survive this economic downturn in large part because of the trust they’ve instilled in their brand. They can introduce new products substantially different annually seemingly without failure. Not only do they meet customer expectations – but they raise the bar too high for most brands to reach.

Instrumental is giving a leg up to brands looking to pull themselves up and over that bar into the future.

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What Is Manufacturing Optimization (and Why Should You Care)

We understand the importance of manufacturing optimization and are committed to helping businesses streamline their operations and increase productivity.

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Manufacturing optimization is a critical concept that can significantly impact a company's success. By improving efficiency, reducing costs, and increasing productivity, optimizing manufacturing processes can lead to higher profits and a competitive edge in the market. In this article, we will delve into the definition, importance, key components, and challenges of manufacturing optimization, providing you with a comprehensive understanding of why it's essential for your business.

Understanding the Concept of Manufacturing Optimization

Definition and basics of manufacturing optimization.

Manufacturing optimization, also known as operational excellence, is the systematic approach of improving production processes to maximize efficiency, minimize waste, and achieve the best possible outcomes. It involves analyzing and enhancing every step of the manufacturing process, from raw material procurement to product delivery.

To achieve manufacturing optimization , businesses employ various techniques, methodologies, and technologies. These include lean manufacturing, Six Sigma principles, advanced manufacturing technologies, data analytics, and more. By incorporating these strategies, companies can streamline operations, enhance quality, and increase customer satisfaction.

One technique commonly used in manufacturing optimization is lean manufacturing. This approach focuses on eliminating non-value-added activities, such as excess inventory, overproduction, and unnecessary transportation. By reducing waste and improving flow, companies can achieve significant cost savings and improve overall efficiency.

Another methodology that contributes to manufacturing optimization is Six Sigma. This data-driven approach aims to reduce defects and variations in the manufacturing process, leading to improved quality and customer satisfaction. By identifying and addressing root causes of problems, companies can achieve higher levels of process capability and reliability.

In addition to these methodologies, advanced manufacturing technologies play a crucial role in manufacturing optimization. Automation, robotics, and artificial intelligence enable companies to increase productivity, reduce errors, and improve overall efficiency. These technologies can perform repetitive tasks with precision and speed, freeing up human resources for more complex and value-added activities.

Data analytics is another essential component of manufacturing optimization. By collecting and analyzing data from various sources, companies can gain valuable insights into their operations. This data-driven approach allows businesses to identify bottlenecks, optimize production schedules, and make data-informed decisions to improve overall performance.

The Evolution of Manufacturing Optimization

Manufacturing optimization has come a long way over the years, evolving alongside advancements in technology and changing market demands. In the past, optimization efforts focused primarily on cost reduction and increasing output. However, with the emergence of lean manufacturing principles and advanced technologies, the optimization landscape has expanded.

Today, optimizing manufacturing processes goes beyond reducing costs. It involves holistic approaches that consider various factors such as energy consumption, environmental impact, sustainability, and worker safety. By taking a comprehensive view, businesses can achieve optimization in multiple areas simultaneously, creating a more resilient and efficient manufacturing ecosystem.

Energy consumption is a critical aspect of manufacturing optimization. Companies are increasingly adopting energy-efficient technologies and practices to reduce their carbon footprint and minimize environmental impact. By optimizing energy usage, businesses can lower operational costs and contribute to a more sustainable future.

Environmental impact is another important consideration in manufacturing optimization. Companies are striving to minimize waste generation, implement recycling programs, and adopt eco-friendly materials and processes. By reducing their environmental footprint, businesses can enhance their reputation, attract environmentally conscious customers, and comply with regulatory requirements.

Sustainability is a key focus in modern manufacturing optimization. Companies are integrating sustainable practices into their operations, such as using renewable energy sources, implementing circular economy principles, and promoting social responsibility. By embracing sustainability, businesses can create long-term value while minimizing negative impacts on society and the environment.

Worker safety is also a crucial aspect of manufacturing optimization. Companies are investing in ergonomic designs, safety training programs, and advanced technologies to ensure the well-being of their employees. By prioritizing worker safety, businesses can reduce accidents, improve productivity, and foster a positive work environment.

In conclusion, manufacturing optimization is a multifaceted discipline that involves continuous improvement, the application of various methodologies and technologies, and a holistic approach to achieving efficiency, quality, and sustainability. By embracing these principles, businesses can stay competitive in an ever-evolving manufacturing landscape.

The Importance of Manufacturing Optimization

Manufacturing optimization plays a critical role in the success of businesses across various industries. It offers a range of benefits that enhance productivity, efficiency, and profitability. By implementing optimization strategies, companies can streamline their operations, reduce costs, and eliminate waste, ultimately gaining a competitive edge in the market.

Enhancing Productivity and Efficiency

One of the primary benefits of manufacturing optimization is the significant increase in productivity and efficiency. By eliminating bottlenecks, reducing downtime, and improving workflow, businesses can accomplish more with their existing resources. This not only boosts output but also enables companies to meet customer demands more effectively.

Furthermore, manufacturing optimization helps identify and address inefficiencies within the production process. It allows businesses to optimize the utilization of machinery, reduce cycle times, and enhance overall equipment effectiveness (OEE). By streamlining operations, companies can minimize waste and maximize value, leading to improved profit margins in a highly competitive market.

For example, by implementing automation technologies and advanced data analytics , manufacturers can gain real-time insights into their operations. This enables them to identify areas for improvement and make data-driven decisions to enhance productivity and efficiency. Additionally, optimization strategies such as just-in-time manufacturing can help companies reduce inventory levels and improve resource allocation, further enhancing overall productivity.

Reducing Costs and Waste

Another crucial advantage of manufacturing optimization lies in cost reduction and waste elimination. By identifying and eliminating non-value-added activities, businesses can improve resource allocation, minimize material waste, and reduce operational expenses.

Manufacturing optimization methodologies, such as lean manufacturing and Six Sigma, emphasize the identification and elimination of waste through continuous improvement and root cause analysis. By implementing these techniques, companies can eliminate unnecessary inventory, reduce defects, and optimize material usage, ultimately saving costs and increasing profitability.

Moreover, manufacturing optimization enables businesses to identify cost-saving opportunities throughout the entire production process. For instance, by implementing energy-efficient technologies and practices, manufacturers can reduce energy consumption and lower utility costs. Additionally, optimization strategies can help companies optimize their supply chain, leading to reduced transportation costs and improved inventory management.

Furthermore, by leveraging data analytics and predictive maintenance techniques, manufacturers can proactively identify and address equipment failures, reducing downtime and maintenance costs. This not only improves operational efficiency but also extends the lifespan of machinery and equipment, resulting in long-term cost savings.

In conclusion, manufacturing optimization is crucial for businesses looking to enhance productivity, efficiency, and profitability. By implementing optimization strategies and embracing continuous improvement, companies can reduce costs, eliminate waste, and gain a competitive advantage in the dynamic and challenging manufacturing landscape.

Key Components of Manufacturing Optimization

Manufacturing optimization is a crucial aspect of any successful business. It involves implementing strategies and utilizing technologies to improve efficiency, reduce waste, and enhance overall performance. Two widely recognized methodologies in manufacturing optimization are lean manufacturing and Six Sigma.

Lean Manufacturing

Lean manufacturing focuses on eliminating waste , reducing variability, and maximizing customer value. It is based on the principles of continuous improvement, just-in-time production, and enhancing overall efficiency. By implementing lean manufacturing practices, businesses can streamline their processes, minimize non-value-added activities, and improve productivity.

One of the key aspects of lean manufacturing is the concept of value stream mapping. This technique allows businesses to identify the flow of materials and information throughout the production process, enabling them to identify bottlenecks, reduce lead times, and optimize resource allocation.

Another important aspect of lean manufacturing is the implementation of visual management systems. These systems use visual cues, such as color-coded labels and signs, to provide clear instructions and guidelines for employees. This helps to eliminate confusion, reduce errors, and improve overall communication within the manufacturing environment.

Six Sigma is another methodology widely used in manufacturing optimization. It aims to reduce defects and variability by applying statistical analysis and data-driven decision-making. The goal of Six Sigma is to achieve near-perfect operational performance by identifying and eliminating the root causes of defects.

One of the key tools used in Six Sigma is the DMAIC (Define, Measure, Analyze, Improve, Control) methodology. This structured approach helps businesses to define the problem, measure the current performance, analyze the data, implement improvements, and establish control measures to sustain the improvements over time.

In addition to DMAIC, Six Sigma also utilizes various statistical tools and techniques, such as hypothesis testing, regression analysis, and design of experiments. These tools enable businesses to analyze data, identify patterns, and make data-driven decisions to improve their manufacturing processes.

Advanced Manufacturing Technologies

Advancements in technology have revolutionized manufacturing optimization. With the advent of Industry 4.0, businesses can leverage advanced manufacturing technologies to streamline processes and improve operational efficiency.

One of the key technologies in advanced manufacturing is automation. Automated systems, such as robotic arms and conveyor belts, can perform repetitive tasks with high precision and speed. This not only reduces the risk of human error but also frees up human resources to focus on more complex and value-added activities.

Artificial intelligence (AI) is another technology that is transforming manufacturing optimization. AI-powered systems can analyze vast amounts of data, identify patterns, and make predictions or recommendations. This enables businesses to optimize their production schedules, predict maintenance needs, and improve overall decision-making.

The Internet of Things (IoT) is also playing a significant role in manufacturing optimization. By connecting machines, sensors, and devices, businesses can gather real-time data on various aspects of their manufacturing processes. This data can then be analyzed to identify inefficiencies, monitor equipment performance, and proactively address issues before they escalate.

Predictive maintenance is another application of advanced manufacturing technologies. By using sensors and data analytics, businesses can monitor the condition of their equipment in real-time and predict when maintenance is required. This helps to prevent unexpected breakdowns, reduce downtime, and optimize maintenance schedules.

In conclusion, manufacturing optimization involves the implementation of strategies and technologies to improve efficiency, reduce waste, and enhance overall performance. Lean manufacturing and Six Sigma are two widely recognized methodologies that focus on eliminating waste and reducing defects. Advanced manufacturing technologies, such as automation, AI, and IoT, are revolutionizing the manufacturing industry by streamlining processes, improving decision-making, and driving innovation.

The Role of Data in Manufacturing Optimization

Importance of real-time data.

Data plays a crucial role in manufacturing optimization, providing valuable insights that enable informed decision-making. Real-time data collection and analysis allow businesses to monitor key performance indicators (KPIs), identify deviations, and take immediate corrective actions.

By implementing data capture systems, businesses can track production metrics, inventory levels, equipment performance, and more. This enables them to optimize scheduling, adjust production capacities, and minimize downtime. The utilization of real-time data empowers companies to make data-driven decisions, maximize efficiency, and adapt quickly to changing market dynamics.

Predictive Analytics and Forecasting

In addition to real-time data, predictive analytics and forecasting provide manufacturers with the ability to anticipate future scenarios and make proactive decisions. By analyzing historical data and market trends, businesses can predict demand, optimize inventory levels, and plan production schedules more effectively.

Predictive analytics enables companies to identify potential issues before they occur, preventing production disruptions and reducing costs. It also helps optimize supply chain management by forecasting demand fluctuations and adjusting procurement strategies accordingly.

Challenges in Implementing Manufacturing Optimization

Resistance to change.

Implementing manufacturing optimization can be challenging due to resistance to change. Employees may be reluctant to adopt new processes or technologies, fearing job insecurity or a decline in productivity. Overcoming this resistance requires effective change management strategies, including clear communication, training, and involving employees in the optimization process.

Need for Skilled Workforce

Manufacturing optimization often requires a skilled and adaptable workforce capable of leveraging new technologies and methodologies. Upskilling and reskilling employees is vital to ensure they can effectively utilize and manage advanced manufacturing technologies. Companies must invest in training programs and talent development initiatives to foster a competent workforce that can drive optimization efforts.

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Production optimization is a systematic approach to producing goods or services in the most efficient manner possible.

Quiz 6: Manufacturing Processes

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The product-process matrix shows the relationship between process structures and product volume and variety characteristics.

The essential issue in satisfying customers in the make-to-stock environment is to balance the level of finished inventory against the level of service to the customer.

project layout is characterized by a relatively low number of units produced in comparison with process and product layout formats.

One difference between an assembly line process flow and a continuous process flow is that on the assembly line the flow is discrete rather than continuous.

The time needed to respond to a customer's order is called the customer response time.

Engineer-to-order firms will work with the customer to design the product,and then make it from purchased materials,parts,and components.

project layout is characterized by a high degree of task ordering.

One trade-off illustrated by the product-process matrix is between flexibility and cost.

The essential issue in satisfying customers in the make-to-stock environment is to balance the cost of the finished item against the willingness of the consumer to pay for it.

An example of an assemble-to-order firm is Dell Computer.

Work center layouts allocate dissimilar machines into cells to work on products that have dissimilar processing requirements.

The closer the customer is to the customer order decoupling point the more quickly the customer receives the product.

The term "assembly line" refers to progressive assembly linked by some material handling device.

The volume requirements for the product are one determinant of the choice of which process structure to select.

The closer the customer is to the customer order decoupling point the longer it takes the customer to receive the product.

A continuous process indicates production of discrete parts moving from workstation to workstation at a controlled rate.

Process selection refers to the strategic decision of selecting which kind of production processes to use to produce a product or provide a service.

Process selection refers to the strategic decision of choosing the volume of output to produce in a manufacturing facility depending upon the way that facility produces.

make-to-order firm will work with the customer to design the product,and then make it from purchased materials,parts,and components.

The focus in the make-to-stock environment is on providing finished goods where and when the customers want them.

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There are many firms using mixtures of process types. One such common exception is the Mass Customization model of production. In mass customization, a company combines low-cost high volume of output, but each and every customer order is customized to the customers specifications. Usually the use of computer-aided manufacturing systems is what permits this customization. Examples include furniture makers who wait to produce the exact model of sofa based on the customers dimensions and fabric choice, or the vehicle manufacturer that has dozens of customization packages and paint options such that each vehicle is custom for the purchaser. A key requirement for successful mass customization is a modular design to allow fast seamless change from each product to the next.

Optimization of AGV sorting systems in pharmaceutical distribution: a two-stage package assignment and simulation approach

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• Published: 19 August 2024

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• Hicham El Baz 1 ,
• Yong Wang 1 ,
• Sang Won Yoon 1 &

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Automated Guided Vehicles (AGVs) have played an important role in modern factories or warehouses, replacing traditional conveyor-based sorting systems due to their flexibility and scalability. However, existing works on AGV sorting systems primarily focus on improving performance from mechanical perspectives or network optimization for routing design. There is a lack of discussion on how to provide a holistic assignment strategy that considers not only the assignment at the sorting area but also the impact of traffic flow from upstream stations in the system. This study introduces a novel two-stage optimization model for AGV sorting systems in central fill pharmacies, implemented via discrete-event simulation and a simulation-based heuristic algorithm. The methodology is based on a detailed analysis of the sorting system layout, assessing performance through key performance indicators (KPIs) such as throughput, utilization, and cycle time, complemented by a sensitivity analysis regarding the number of AGVs. Operational implications include improved assignment strategies that enhance overall system efficiency, reduced cycle time, and optimized resource utilization. Results demonstrate broad applicability across different automated systems, suggesting significant implications for operational efficiency.

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Simulation Budget Allocation for Improving Scheduling and Routing of Automated Guided Vehicles in Warehouse Management

Comprehensive Review of the Dispatching, Scheduling and Routing of AGVs

Intelligent Scheduling and Routing of a Heterogenous Fleet of Automated Guided Vehicles (AGVs) in a Production Environment with Partial Recharge

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Conceptualization: Hicham El Baz, Yong Wang, Sang Won Yoon, Yu Jin; formal analysis: Hicham El Baz; investigation: Hicham El Baz; project administration: Yong Wang, Sang Won Yoon, Yu Jin; resources: Hicham El Baz, Yu Jin; software: Hicham El Baz; supervision: Sang Won Yoon, Yu Jin; validation: Hicham El Baz, Yong Wang, Sang Won Yoon, Yu Jin; visualization: Hicham El Baz; writing—original draft: Hicham El Baz; writing—review and editing: Hicham El Baz, Yong Wang, Sang Won Yoon, Yu Jin.

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El Baz, H., Wang, Y., Yoon, S.W. et al. Optimization of AGV sorting systems in pharmaceutical distribution: a two-stage package assignment and simulation approach. Int J Adv Manuf Technol (2024). https://doi.org/10.1007/s00170-024-14255-7

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Published : 19 August 2024

DOI : https://doi.org/10.1007/s00170-024-14255-7

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