Tag Archives: Machine Learning

Factors ranging from the weather to celebrities’ social media posts can spur the public’s demand for particular products. Those spikes can cause supply chain constraints company leaders aim to avoid. It is better when corporate teams can predict what people will want and get those products far enough in advance to cater to everyone wishing to buy them. To achieve this, businesses are using AI to strengthen their supply chains. Here’s how…

Managing Demand While Selling Diverse Product Assortments

Demand planning is especially complicated when retailers sell huge varieties of goods within a large category. Such was the case with one of Canada’s largest electronics retailers. People go there to purchase everything from phone chargers to televisions.

However, the demand for those two examples is very different. Many consumers buy several phone chargers per year, such as if they want one for each main room in a home or have forgotten to pack the item before going on a trip. However, most TVs last several years, and people only buy them once the ones they have break or otherwise no longer meet their needs. Plus, many shoppers are more likely to buy those big-ticket items during the holiday season than at other times.

The Canadian retailer uses AI and machine learning technologies to get data-driven demand insights that shape inventory and supply-chain-related decisions. Its leaders have already noticed several benefits. For example, demand planning has become more automated, and those involved can receive detailed reports highlighting potential business risks and impacts.

Additionally, supply chain employees can address slow-moving inventory, plan more enticing promotional offers and reduce stockouts. Another aspect of the AI solution evaluates various supply chain scenarios and gives prescriptive recommendations to prevent unwanted consequences. These examples show how AI can support workers in their roles and increase productivity.

A common misconception about AI is that it will replace human staff. One study found job loss from automation and other advanced technologies was a worry for 42% of respondents. However, besides assisting them with the tasks they already know, artificial intelligence can expand their skills, encouraging them to use new platforms and tools that make demand planning easier.

Streamlining Demand Planning Processes for Better Productivity

Demand planning processes vary depending on what the brand sells, the size of its supplier network, its budget and more. However, no matter how organizations handle them currently, AI can pinpoint opportunities to streamline the work for better overall outcomes.

One example comes from a multinational consumer goods enterprise offering diapers, detergent, personal grooming products and other household staples. Leaders hoped to improve current demand planning by bringing artificial intelligence into the workflow. Initial data inputs for the project included bill-of-materials information for 5,000 products and 22,000 components. Additionally, users imported various types of supporting supply chain details into the system, including specifics about vendors, warehouses and manufacturing plants.

The technology then compiles all that information to give real-time or trend-based insights. Besides providing live inventory data, the AI product can generate supply projection reports that indicate future needs while highlighting possible supply chain disruptions. Knowing about potential issues sooner gives employees the information to act confidently and prevent or mitigate those problems.

The tool was also a significant productivity booster for the consumer goods firm. For example, supply chain queries used to take more than two hours to complete but now occur immediately. Additionally, although it formerly needed more than 10 people to verify the data, the technology can do that without human oversight. Such improvements substantiate studies showing AI can make people 20%-45% more productive depending on various factors.

Running Supply Chain Simulations Before Key Events

Even though some periods of increased demand are impossible to predict, most supply chain managers can anticipate others with near certainty. For example, Black Friday is one of the biggest shopping days of the year in the United States. Additionally, late summer drives sales of bedding sets, reasonably priced furniture and school supplies as students prepare for college.

Demand planning is essential for giving supply chain professionals the necessary information to source and move the products customers will want most during those hectic periods. Since artificial intelligence can process large quantities of information quickly, users could feed details such as social media mentions, customer service email or chat data, and sales figures into tools to determine which factors make some products more or less desirable.

The leaders of one multinational American retailer used AI to determine what customers would want before Black Friday arrived. The goal was to learn those details before shoppers even consciously expressed a desire to buy specific items. While using the artificial intelligence platform, retail staff entered data about shopping and customer trends, seasonal factors and more. The resulting output steered supply chain decisions and helped address issues that might ordinarily cause Black Friday disruptions.

The retailer has also added AI to its daily supply chain workflows, relying on the technology to anticipate demand cycles and unexpected traffic peaks. Some businesses use complementing technologies such as digital twins to get similar results. These tools enable people to predict bottlenecks and investigate potential actions before pursuing them in real life.

Making Demand Planning More Manageable

Demand planning is tricky and requires a thoughtful approach from people who combine their expertise with trustworthy data. However, these examples show how purposeful AI applications can assist with this all-important aspect of supply chain operations, increasing the likelihood of satisfied customers and profit.

*This article is written by Jack Shaw. Jack is a seasoned automotive industry writer with over six years of experience. As the senior writer for Modded, he combines his passion for vehicles, manufacturing and technology with his expertise to deliver engaging content that resonates with enthusiasts worldwide.

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Remember when artificial intelligence (AI) was a glimmer on the horizon? And then ChatGPT stormed onto the scene and people were convinced every job out there was soon going to be replaced by a bot? Now it turns out, not so much.

As awesome (and we don’t use that word lightly) as AI is, it’s only as good as the data it has to work with. At USC Consulting Group, we’re finding this is especially true when we’re using AI for predictive analytics. AI doesn’t like variation, and there can be a lot of that in manufacturing processes.

Here’s a look into this issue and how to handle it.

A short primer into AI and predictive analytics

AI is a broad term describing computer systems that perform intelligent tasks, like reasoning, learning, problem solving, and more. Not so obvious is predictive analytics, which is the ability to forecast future outcomes using AI based on data. You’re already familiar with it, to a certain degree. If you’ve ever had a recommendation from Netflix based on what you’ve watched in the past, that’s it. In a nutshell.

Netflix’s use of predictive analytics created a seismic shift in consumer expectations. This technology also has the potential to transform operating procedures and processes for many industries.

It’s extremely powerful when dealing with processes in which multiple predictors are influencing outcomes. It has the ability to tell us which path to take in order to achieve a desired outcome, even when process patterns and trends are changing.

It means greater precision and accuracy, speed and increased efficiency, the holy grails for any manufacturer.

But there is a fly in this cyber ointment.

Variation.

AI doesn’t like it and – low and behold – that means humans are necessary in this process in order for predictive analytics to achieve its potential.

What is variation?

When we’re talking about manufacturing processes, what exactly does variation mean?

In manufacturing, variation is the difference between an actual measure of a product characteristic and its target value. Excessive variation often leads to product discard or rework.

When you’re dealing with high process variation and instability, it degrades efficiency, consistency and ultimately, profits. A key manufacturing performance objective is the establishment of stable and predictable processes that limits variation – minimum variation around target values.

A main focus for USC Consulting Group is to identify the root causes of variation and address them. Generally, it boils down to people, components and materials.

Some examples to causes of variation include:

• Poor product design
• Poorly designed processes
• Unfit operations
• Unsuitable machines/equipment
• Untrained operators
• Variability from incoming vendor material
• Lack of adequate supervision skills
• Changing or inadequate environmental conditions
• Inadequate maintenance of equipment
• Inadequate or changing environmental conditions

It can be one of these factors, several, or something else. But whatever it is, it’s impeding our ability – and the bot’s – to predict outcomes.

Minimizing variation with our Customized Quality System (CQS)

Every situation is different. The cause of variation on one manufacturing line isn’t going to be the same on another. USCCG assesses and evaluates client processes, then applies a customized approach using a series of tools, techniques and methods that is most applicable in addressing the causes of variability. This customized approach enables USCCG to address variability in an efficient manner. We call it our Customized Quality System (CQS).

We review processes from “the cradle to the grave” and identify the highest-impact operations, then drill down to the tasks and steps within those operations until we uncover the culprits.

Although every situation is different, the general roadmap includes:

• Carefully defining the problem
• Selecting the right team
• Objectively identifying high-impact operations
• Drilling down into the tasks within those operations
• Brainstorming possible causes on those high-impact tasks
• Recommending and implementing deeply focused corrective actions
• Controls so it doesn’t happen again

Removing variability through our CQS not only has an immediate impact on improved product conformance but also paves the way for AI to do its job in predictive analytics, i.e., we want predictions with minimum variability.

It’s just one way USC Consulting Group is using the human touch to make sure AI is up to the job.

Read more about this in our free eBook, “AI and Machine Learning: Predicting the Future Through Analytics.”

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Global supply chains are intricate networks that span multiple countries and continents, involving a multitude of processes, from procurement to distribution. The complexity is further compounded by varying local standards and regulations, making standardization a critical need.

The United Nations Standard Products and Services Code (UNSPSC) provides a universal classification framework that is essential for streamlining these complex processes and facilitating seamless international operations.

Benefits of UNSPSC

UNSPSC serves as a global language for businesses, ensuring that products and services are categorized consistently regardless of where they are produced or consumed. This standardization is vital for global trade, as it simplifies communications between suppliers and buyers, enhances spend analysis and reporting capabilities, and improves procurement efficiency.

By adopting UNSPSC, companies can ensure more accurate demand forecasting and inventory management, which are crucial for maintaining the flow of goods and services across global markets.

AICA’s Automated Approach to UNSPSC

Data management and cleansing specialist AICA offers a SaaS platform that leverages advanced AI and ML technologies to automate the UNSPSC classification process. This automation is driven by AI models trained on extensive datasets, significantly increasing accuracy and reducing errors commonly seen in less sophisticated systems.

The process of manually classifying products into UNSPSC codes is a task that traditionally requires substantial time investment. For instance, cataloguing a single product into the UNSPSC framework manually takes approximately 10 minutes. Classifying 10,000 products would, therefore, require about 69 days. Thus, manually classifying products consumes a significant amount of time, representing a substantial opportunity cost.

However, AICA’s platform automates this process and assigns the classified items with an accuracy score. Items that receive a quality score lower than 93% are flagged for review by our subject matter experts.

Here’s a breakdown of the time savings:

• Total manual classification time for 10,000 products: 69 days.
• Time required using AICA’s platform with automated and expert review: Only the items with a quality score below 93% are manually reviewed. This drastically cuts down the overall manual intervention and time needed compared to the traditional method.

Thus, by using AICA’s system, a task that would normally take over 69 days of continuous work can be reduced significantly to only a few.

This methodology not only speeds up the classification process but also ensures a high level of accuracy and reliability, allowing businesses to deploy resources more effectively and enhance overall productivity in the supply chain.

Universal Relevance

The relevance of UNSPSC and AICA’s technological solutions extends across various critical sectors, including Manufacturing, Mining, and Aerospace and Defense. These industries face unique challenges such as managing complex assemblies, complying with strict regulatory standards, and handling high-value inventories.

UNSPSC codes help standardize component classifications, making it easier to track and manage parts across global supply chains. For these sectors, the ability to accurately classify and analyze product data can lead to more strategic sourcing and better risk management.

Conclusion

For global enterprises aiming to improve their supply chain operations, adopting AICA’s UNSPSC-classifying technologies offers a transformative opportunity. By integrating our solutions, companies can benefit from enhanced data accuracy, improved operational efficiency, and a competitive edge in the global market.

*This article is written by USCCG’s strategic partner, AICA Data. AICA is a data cleansing and management specialist that optimizes your product and services data with AI to provide faster, more accurate, and cost-effective solutions. To find out more about AICA’s services – visit their website here.

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Automobiles are becoming smarter thanks to advancing technologies and manufacturing practices. Nowadays, vehicles can communicate with each other, people and networks to increase safety due to artificial intelligence (AI) and machine learning (ML). What if cars on the road could communicate with everything? This technology is in the works through vehicle-to-everything (V2X) communication. Here’s a guide on V2X, its benefits and what it means for auto manufacturing.

The Role of V2X Communication

Automotive communication systems date back nearly half a century as manufacturers started designing systems to let vehicles communicate with each other in the 1970s — a concept known as vehicle-to-vehicle (V2V). V2V is still evolving, with Volvo and FedEx experimenting with automated platooning in Europe and pairing groups of trucks to follow each other on the highway.

Other types of vehicular communication include:

• Vehicle to Device (V2D)
• Vehicle to Network (V2N)
• Vehicle to Grid (V2G)

V2N will be critical as researchers continue improving 5G. With this system, cars will send information across networks through LTE and 5G. Experts say about 90% of American mobile connections by 2030 will be through 5G.

V2X in Auto Manufacturing

V2X is a critical technology because it combines all types of vehicular communication into one system. With this advanced mechanism, cars will be more intelligent than ever and could establish themselves as better drivers than humans. Auto manufacturers are trying to accomplish this feat with self-driving vehicles, but the industry hasn’t reached fully autonomous operations yet.

Improving V2X is essential in the race for self-driving vehicles, as this technology lets cars see and understand the world around them. An autonomous car or truck must be able to react quickly to traffic jams, emergency vehicles passing, animals crossing the road and other sudden events. Vehicles could work together and make the streets safer, thus creating a safer environment for autonomous machines.

V2X Applications

V2X offers opportunities to integrate all these technologies into one machine. This vehicular communication system exists in limited numbers currently but could soon make its way into more automobiles.

More recently, Toyota successfully tested its V2X technology in collaboration with Orange. The automaker equipped a vehicle with V2X capabilities and credited 5G and edge computing for its test track accomplishments. V2X technology warned drivers of emergency vehicles, helped them avoid collisions and accurately positioned the car.

What Are the Manufacturing Implications of V2X Communication?

V2X presents an incredible opportunity in the automotive industry to make cars smarter. What does this technology mean for manufacturing? Here are four implications to see as this concept evolves into the mainstream.

Advancing Technologies

Incorporating V2X in all auto manufacturing would make car assembly more advanced due to the AI and ML necessary for building. While some vehicles are simplistic with minimal technology features, these machines require onboard units and other devices to meet V2X’s needs. This change will require employees to understand the technology and how to include it inside the vehicles.

Standardization Needs

Automakers use vehicular communication technology like V2V, but these concepts only work with machines from the same manufacturer. For V2X’s success, auto manufacturers must standardize this technology so cars can connect seamlessly despite the logo on the front. Collaboration must also include semi-chip manufacturers, software developers and other professionals involved in advanced automotive technology.

Cybersecurity Risks

Integrating technology comes with cybersecurity risks, so automakers must ensure their V2X technology has robust security features to protect drivers. Otherwise, operators risk crashes, theft and other unwanted outcomes. One way to safeguard V2X-integrated vehicles is implementing security requirements with third parties to minimize the risk of data breaches.

Supply Chain Visibility

V2X technology can help auto manufacturers with their supply chain visibility — a critical component considering the modern economic climate. With advanced communication devices, automakers can help fleet owners with logistics management and increase transparency with suppliers. For instance, V2X’s enhanced route optimization can reduce lead time for parts, making manufacturing more efficient.

What Advantages Does V2X Communication Bring?

V2X communication is beneficial because it lets the auto industry take another step toward autonomous vehicles. What other advantages does this sector reap? Here are a few positive takeaways from V2X technology.

Driver Safety

With V2X communication, car operators can feel safer on the road. Vehicles communicate with each other to know when hazards lie ahead on the road or changing weather conditions. This benefit is even more pronounced with long-haul trucks, considering their role on America’s highways.

V2X technology in semi-trucks would let logistics professionals use autonomous trucks and reduce accidents and losses. Experts say driverless trucks perform up to 30% better than those with operators, so V2X would go a long way in promoting safety.

Environmental Benefits

Advancing vehicular communication technology also benefits the environment by cutting emissions. The transportation sector is responsible for 29% of all emissions, so reducing this output is essential. V2X can help the environment by mitigating traffic congestion, thus reducing idle time and wasted fuel in cars.

Smart City Integration

Rising urban populations mean cities will need to manage their energy grids better. V2X technology lets vehicles communicate charging needs and reduce strain on the grid. For instance, EVs could select optimal charging times — such as off-peak hours — to help the city’s energy grid and optimize efficiency.

Using V2X Communication for an Autonomous Future

Research on autonomous vehicles has surged as automakers race to be the first to debut fully self-driving cars. Reaching this level of driverless operations requires V2X devices that combine the best aspects of vehicular communication technology. These advanced mechanisms have implications for manufacturers and benefits for drivers, so the future has a lot of potential for this corner of the automotive industry.

This article is written by Jack Shaw. Jack is a seasoned automotive industry writer with over six years of experience. As the senior writer for Modded, he combines his passion for vehicles, manufacturing and technology with his expertise to deliver engaging content that resonates with enthusiasts worldwide.

Auto Parts Supplier Revs Up Its Production Process {case study}

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Since the industrial revolution, every technological advancement has been viewed through the lens of its effect on jobs. Will I be obsolete? Can a machine do my job better than I can? Are the bots coming for me? If my skills are rendered obsolete, what will I do?

The plain truth is, sometimes machines can do the job better, faster or more efficiently than a human can. Think of the advent of the sewing machine. Even your grandmother’s old Singer model is a whole lot faster, more precise and efficient than she is working with a needle and thread. The art and craft of sewing isn’t lost or obsolete, but for sheer volume and exact replication, you can’t beat the machines.

What’s happening now with artificial intelligence (AI) in manufacturing is a little bit like that. People on all levels of the manufacturing chain want to know if AI is taking over.

The answer is no. Don’t think of it as a takeover. Think of it as more of a transformation. It’s already happening, and it’s not all bad.

AI’s current impact on manufacturing

Artificial intelligence is seeping into the manufacturing workplace in a couple of important ways.

Automation: Much like the sewing machine and indeed all of the industrial revolution, AI has the power to automate repetitive tasks previously done by humans. Operating machinery, tasks on the assembly line, even inspecting products for defects – all of these things are increasingly being automated.

Efficiency: AI can help us optimize processes and procedures, leading to greater efficiency on the line and as a whole.

New job creation. Yes, you read that right. Whereas AI may reduce the amount of jobs focused on repetitive tasks, it is also creating jobs that we haven’t seen before in the manufacturing realm, including specialized programmers, engineers, and technicians. It means companies will need people with different skill sets, and the savvy employers will dig in and train the people they already have to take on these new roles.

Predictive analytics

At USC Consulting Group, we’ve already been using AI with some of our manufacturing clients, specifically in the area of predictive analytics. We spell it all out in our eBook, “AI and Machine Learning: Predicting the Future Through Data Analytics,” but here is the gist of it in a nutshell.

By now, we all know what AI is — computer systems that perform intelligent tasks, like reasoning, learning, problem solving, decision making, and natural language processing, among others.

Machine learning is a subset of AI. It is, technically, a set of algorithms that can learn from data. Instead of having to be programmed, the computer learns on its own based on data.

Predictive analytics is one output of machine learning. It is the ability to forecast future outcomes based on data. It’s like having a crystal ball that’s informed by vast amounts of complex algorithms and data.

You’re already familiar with predictive analytics but may not know it. You know how Amazon suggests an item for you to buy based on past purchases, or Netflix queues up new shows based on what you’ve already watched? That’s predictive analytics in action.

Much like Netflix’s use of predictive analytics created a seismic shift in consumer expectations, this technology also has the potential to transform operating procedures and processes for many industries.

The benefits of using AI in predictive analytics are many, including:

• Greater precision and accuracy. Yes, humans do the programming. But AI can analyze mountains of data and identify complex patterns people might miss.
• AI can analyze vast amounts of data in a snap. This helps companies make decisions faster.
• Increased efficiency. All of this accuracy and speed leads to greater efficiency, output and ultimately, profits.

Bottom line: AI needs us

AI is a powerful tool we’ve used at USCCG to help our clients achieve greater efficiency, productivity, and profits.

But here’s the thing about that. It’s a tool. And it’s only as good as the data we supply. Any variation, and there can be skewed results.

As we all know, life is not a data set. Variation is happening all around us, all the time, even in projects where we need great precision.

That’s why the bots are never going to replace humans. They need us as much as we need them. At USCCG, we have more than 50 years of experience making process improvements, finding hidden opportunities for efficiency, creating leaner systems and helping companies thrive. For the next 50, AI will be one tool we use to help achieve that.

Read more about this innovative technology, including a specific case study about how AI works in practice, in our eBook, “AI and Machine Learning: Predicting the Future Through Data Analytics.”

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Statistical process control (SPC) is a commonly used machine learning software in manufacturing that measures the consistency of a product’s performance based on its design specifications. Minimizing variability is a crucial part of avoiding defects and maintaining resilient manufacturing operations.

This guide outlines the different ways that businesses can effectively utilize SPC and reap all of the benefits this technology has to offer.

How Statistical Process Control Works

SPC is a tried and true technology that businesses have been using for more than 100 years to improve their manufacturing operations. It conducts ongoing statistical analyses, taking into account factors such as the materials, design, employees who handled the product and the machinery used to create the product.

SPC’s constant vigilance enables businesses to make swift and accurate resolutions to quality control problems. However, it’s not fully autonomous like other manufacturing software that can identify statistical correlations without human help. Instead, it relies on large amounts of training datasets that another source must manually input to achieve the desired results.

This form of machine learning is known as supervised learning. Businesses can input human-labeled datasets by themselves, or they can recruit another algorithm to automatically input statistics in a process called “machine annotation.” In either case, SPC needs to absorb as much raw data as possible to maximize its efficiency.

SPC displays its findings in easy-to-read control charts, and it’s the business’s responsibility to set the parameters for each chart by providing the software with enough information. This process includes six basic steps:

1. Define the manufacturing process you want to monitor and control by establishing the input variables, output variables, equipment, materials and any other external factors that might affect the process.
2. Collect the data that the software extrapolated from the variables you provided, then organize it into a digestible format — usually a chart or spreadsheet.
3. Select and construct the control charts based on the type of data you’re using, such as weight, length, temperature and any defects that might have occurred.
4. Look for patterns in the control charts that indicate special cause variations in performance due to underlying defects. You can calculate process variability through a capability index, such as C, Cpk, Pp and PPk.
5. Investigate the root causes of the variations and make the necessary equipment, material or operational adjustments to correct them.
6. Continue to collect and organize data to identify more variations, updating the control specifications as needed.

This process sounds awfully similar to Statistical Quality Control (SQC), but there are some key differences. Statistical Process Control measures independent variables, while SQC strictly focuses on dependent process outputs. SQC also carries out acceptance tests by screening individual product samples, while SPC relies on large datasets and doesn’t have an acceptance testing feature.

Types of SPC Tools

Many types of analysis tools have developed during SPC’s century-long evolution. These tools are split into two main categories — basic tools of quality (7-QC tools) and supplemental tools (7-SUPP tools). Here’s a quick rundown of how businesses can use the 7-QC tools:

• Stratification: separating data into subcategories by unique characteristics to clarify the origins of an existing problem.
• Histogram: A bar graph that displays the frequency of variability and the most common offenders.
• Check sheet: A document with tabular or metric format that tracks the number of special cause variations.
• Cause-and-effect diagram: A chart that shows all of the factors that lead to special cause variations and draws potential correlations between them.
• Scatter diagram: A dotted diagram that displays the overlap between dependent variables on the y-axis and independent variables on the x-axis.
• Control chart: A line-based graph that shows processes’ stability levels and pinpoints the likely variation within produced items.
• Pareto chart: This chart applies the 80/20 principle — 20% of variables cause 80% of problems — to display the most common causes of manufacturing failures.

Stratification also often appears in the 7-SUPP tools category because of its versatility and importance to statistical analysis. Breaking up large datasets into smaller digestible chunks makes SPC software more accurate at identifying problems and reducing variability. Here are the other six 7-SUPP tools:

• Flowchart: A straightforward diagram that outlines the step-by-step process of a manufacturing sequence.
• Defect mapping: A chart that shows the different types of known product flaws within a business’s manufacturing operations.
• Events logs: A variable summary showing the chain of events that resulted from an undesired occurrence.
• Progress centers: Centralized locations dedicated to tracking improvements and supporting informed decision making.
• Randomization: The deployment of random manual and automated input variables to eliminate human bias.
• Sample size determination: Choosing the number of subjects to include in a representative group when tracking manufacturing trends.

Today’s SPC software modules include all of these tools, allowing businesses to access dashboards that display the various charts and diagrams in one place. These insights can lead to identification of quantifiable improvement opportunities that maximize operational efficiency.

Benefits of Using SPC

SPC is one of the most effective machine learning resources for achieving consistent performance in manufacturing operations. Eliminating process errors allows businesses to simultaneously address the three biggest challenges in material handling — workplace hazards, equipment damage and carbon emissions — in many ways:

• Reduces manufacturing costs
• Monitors employee productivity
• Improves resource utilization
• Optimizes manual inspections
• Reduces rework and warranty claims

When these benefits combine, the final result is a more satisfied client base and a more profitable business. While SPC software can’t do all of the inspection work on its own, the tools and insights it provides are invaluable in a manufacturing environment.

Use Statistical Process Control to Its Full Potential

Business leaders who are willing to put in the necessary effort to provide SPC software with large datasets can use this technology to its full potential. They will gain access to numerous eye-opening statistics about operational inefficiencies and have all the knowledge they need to make accurate adjustments.

*This article is written by Jack Shaw. Jack is a seasoned automotive industry writer with over six years of experience. As the senior writer for Modded, he combines his passion for vehicles, manufacturing and technology with his expertise to deliver engaging content that resonates with enthusiasts worldwide.

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The 2010s are now a memory. Every decade has its fair share of ups and downs, but by most measures, this past decade was a good one for much of the country.

Granted, due to the Great Recession, the U.S. economy started out in a bit of a rough patch. However, thanks in part to regulatory changes and good old fashioned entrepreneurialism, the unemployment rate has reached record lows nationwide and extreme poverty globally is now in the single digits (8.6% from 18.2%, according to World Bank data).

There were many notable strides in the 2010s aside from sheer job growth. Such improvements were largely due to the role of operational excellence. Whether in terms of productivity, creativity or ingenuity, operational excellence is the unyielding pursuit of greatness, the constant and consistent refining of current processes in order to achieve a better outcome. According to polling conducted by the Institute for Operational Excellence, more than 70% of businesses professionals say OpEx is instilled in the very fabric of their company’s culture. Whether it’s business transformation, lean six sigma, process improvements or business process management, these methods are all designed to help businesses reach a little farther and dig a little deeper in terms of becoming better than they were yesterday, a month, or a year ago.

There are many ways to examine the OpEx lifecycle from 2010 to today, but perhaps the most salient examples are technological development, process management and ideologies, meaning the beliefs that help inform businesses’ strategy and understanding of what is the most important aspect of their operations. Here are a few examples from each category that show how the role of operational excellence has evolved over time.

Technology: Automated intelligence

Automation has changed the world in an extraordinary number of ways. From ubiquitous handheld technology, fast-food kiosks in restaurants and robotic installations in factory settings, automation today is everywhere. In the early 2000s, the share of new robot installations in hi-tech manufacturing rose 21% to a total of 21,000 worldwide, according to Oxford Economics. But by the mid-2010s, they grew an additional 31% to 91,000 in 2016.

What accounts for the surge? For starters, automation-related processes are not only better by today, but cheaper. As a result, more employees are working alongside robotics in order to manufacture and deliver products quicker and more efficiently. Much of this is attributable to growth and development in technological improvements in things like machine learning.

Karen Hao of MIT Technology Review wrote in 2018 that were it not for machine learning, many of the artificial intelligence advancements — such as viewing recommendations on Netflix or “fill in the blank” search suggestions on Google — would have stalled.

“Machine learning has enabled near-human and even superhuman abilities in transcribing speech from voice, recognizing emotions from audio or video recordings, as well as forging handwriting or video,” Hao explained, as quoted by Popular Mechanics.

While some presidential hopefuls and economists warn of significant job losses posed by automation, only 27% of respondents are worried about such a scenario affecting them, according to polling conducted by CNBC and Survey Monkey. This may be a function of  employers retraining employees and repositioning them in roles where their skills can be better leveraged and in a better position for the company to achieve operational excellence.

Processes: Change management

In order to achieve results and get to a better place, change may be necessary. By its very nature, change is difficult, but in order to move forward, develop and learn from previous mistakes, structural or process-related changes may be required.

The roots of change management trace back to the early-to-mid 20th century from thought pioneers like Arnold van Gennep and  Kurt Lewin. The last 10 years or so has resulted in change management taking on a life of its own, as not only have most businesses heard of the term, they’ve refined the process so whole-scale changes are less drastic.

As noted by Oracle Technical Program Manager Burhan Syed, this has come from a greater focus on implementing evolutionary changes rather than revolutionary, using a more methodical, incremental approach versus those that are all at once. Today, change management is a process-related strategy as well as a profession, as companies hire individuals or operations management consultants to lead these sweeping efforts. Regardless of who pilots them, leadership is key.

“Leaders need to understand that their management styles must be able to adapt to the nuances of championing organizational change,” Syed wrote.

Ideology: Customer experience

While many would argue that the customer experience is every bit as important today as it was in 2010, few can deny the extent to which its become a singular focus. This is largely due to a greater number of companies vying over a smaller pool of consumers, so they must distinguish themselves to earn their loyalty. When it comes to measuring the success of improvements efforts, the third most common response among business owners point to is customer satisfaction, the Institute for Operational Excellence found.

Connie Moore of the Digital Clarity Group points to organizational change management, innovation, “outside the box” thinking and analytics as some of the key drivers to improving and refining the customer experience on an ongoing basis.

What will be the key takeaways in the 2020s and beyond? Time will tell, but you can make the decade a successful one by working with USC Consulting Group. From asset utilization to productivity improvements, sales effectiveness to cycle time reduction, we can help you achieve operational excellence so your greatest challenges in 2019 become your biggest strengths in the days ahead. Contact us to learn more.

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