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Technology is crucial in most industries to advance safety and efficiency. The automotive sector is an excellent example of how advanced technology transforms products and, thus, the world. Big data and analytics have become an integral part of it all. So, how exactly has the automotive industry taken advantage of analytics, especially with maintenance and predictive diagnostics? How can using it benefit manufacturers? Let’s find out…
How Did Big Data Analytics Emerge in the Automotive Industry?
Big data is a relatively new concept, but its modern adaptations originated in the 1960s. For example, in 1964, IBM introduced the System/360, offering processors 100 times more potent than their predecessors. This technology is primitive in retrospect, but it was an essential first step for data processing. In the 1970s and 1980s, tech companies improved this technology to include the automotive industry.
By the 1990s, automotive technology producers began using big data analytics for vehicles. For example, global positioning systems (GPS) became more prominent this decade. These devices allowed consumers to use navigation technology well-known in the U.S. military. Many luxury cars came with this feature installed to entice consumers.
While GPS devices are still prominent, big data has improved cars enough to where they can be self-reliant. Soon, automakers will remove GPS devices once autonomous vehicles become widespread. These vehicles know where they’re going and do not need a GPS for navigation.
What Role Does Big Data Analytics Play in Automotive Maintenance and Predictive Diagnostics?
The last two decades have seen incredible growth for big data and its role in the automotive industry. Automotive professionals have used advanced technology for maintenance and predictive diagnostics. Using data helps technicians know precisely what the problem is and the necessary methods for mitigation.
Automakers primarily take advantage of big data analytics through embedded sensors in their vehicles. These devices allow the manufacturers to track cars anywhere in the world and detect where the problems lie. With this information, the automaker can notify the consumer of issues, find trends and develop solutions for widespread problems. Then, they know what issues to correct for future models of the same vehicle.
What software and technologies do automotive professionals use? These examples demonstrate how industry experts use big data for predictive maintenance and predictive diagnostics.
Machine learning (ML) has become a critical part of the automotive industry because it solves complex problems and creates algorithms. For auto manufacturers, ML has helped technicians predict equipment failures.
For example, automakers use ML to analyze historical data from their vehicles. Their computers use sensor data to detect trends and see what abnormalities led to the issues. Therefore, manufacturers can catch what problems may arise when they see a particular pattern occurring in a vehicle.
Another use for ML is creating maintenance schedules for vehicles. Historical data indicate when owners of a particular model should bring their cars for routine maintenance. The algorithm is intelligent enough to combine the data with driver performance to alert when service is necessary for the vehicle.
Telematics is one of the earliest examples of big data in the automotive industry, and it’s become vital for car owners and fleet managers worldwide. Research shows about 80% of Class 8 trucks in North America use telematics for safety and efficiency.
Telematics is essential for maintenance because it monitors vehicle health. These devices often detect problems earlier than the operator can, allowing companies to act swiftly on their machines. Early detection and mitigation save money and improve safety by not putting drivers in harmful situations.
What Are the Benefits of Big Data in Automobiles?
Big data analytics is a win-win for manufacturers and consumers. All parties can have peace of mind knowing their machines are safe and efficient. The following three benefits demonstrate how automotive professionals benefit from big data.
Cars are essential for daily travel, but they can be dangerous. The National Highway Traffic and Safety Administration (NHTSA) says nearly 43,000 people died in motor vehicle traffic crashes in 2022. Reasons for accidents vary, but they can originate from fixable mechanical failures.
Big data analytics decreases the likelihood of these failures by scheduling preventive maintenance and alerting when serious problems arise. Users can know if their brakes, steering wheel or battery needs attention before a catastrophe happens.
Big data analytics has become invaluable for fleet managers worldwide. The average fleet manager may have 10, 100 or 1,000 vehicles under their wing, making it difficult to track all of them simultaneously. However, advanced data allows them to monitor all the cars and detect trends.
Warning systems send information to the fleet manager, allowing them to act immediately. The modern supply chain demands maximum productivity from fleets, so taking advantage of big data analytics is essential.
Sustainability has become a significant focus for auto manufacturers. The push for more renewable energy and less waste has led to innovation across the industry. How are they achieving sustainability standards? Big data analytics is helping automakers care for the environment.
Using big data analytics extends the life of cars and reduces the need for customers to consume resources by purchasing cars. Instead, they can keep the same vehicle longer and spend less time at the mechanic.
When cars reach their end of life, many head to the scrapyard. While recycling has improved, parts and pieces still go to waste. For example, the European Union scrapped 5.4 million passenger cars in 2020. Installing telematics devices and using big data would extend their time on the road and reduce waste.
Big Data Analytics in the Automotive Industry
Automotive technology has come a long way and only improves yearly. Modern software allows auto manufacturers to utilize big data analytics with maintenance and predictive diagnostics. With this technology, manufacturers lower the cost for themselves and consumers and make their processes more efficient.
*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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With all the recent media attention about ChatGPT and other AI tools taking over our jobs (and our world), it’s easy to forget that technology — even AI — is a tool we can use to make our lives easier and our businesses more efficient.
At USCCG, we’ve been using software to help our clients harness and wrangle their data and transform it into usable insights in real time, and support their operational goals and aspirations.
The various software modules that make up our proprietary LINCS system, along with Microsoft’s Power BI, were designed to do just that. Here’s a look at the software we use to help you harness your data.
A cornerstone of the work we do at USC Consulting Group, the Lean Information Control System (LINCS) gives supervisors, managers and execs the operating information they need, when and where they need it. LINCS offers state-of-the-art tools and software applications that facilitate fact-based decision making in real time from the shop floor to the boardroom.
LINCS includes modules for advanced planning, manufacturing and logistics, value-stream mapping, time-phased production scheduling, inventory analysis and more. These modules can be used in combination or alone, based on the needs of your business. That’s because of another of our cornerstones: no cookie-cutter solutions. You can pick and choose which modules and features are most useful for your particular needs.
LINCS is designed to be a sophisticated decision support tool kit that is adaptable, affordable, easy to use and easy to configure.
We’re fond of saying companies need to “manage by the numbers.” This is the way to do it.
Here’s a closer look at the modules.
Performance Analysis and Reporting
This module collects and analyzes operating and production information in real time. Seeing how the work is stacking up as it takes place gives operators and executives powerful information by which to make decisions, prioritize activities and pivot when things aren’t going according to plan. It allows management to view operations at a glance or drill down to examine specific areas of opportunity via an executive dashboard. And it’s easy to configure and use.
- Acts as middleware, interfacing with enterprise-level and SCADA-level systems, as well as human input
- Identifies and quantifies improvement opportunities in your operation
- Provides accurate tracking and analysis of important key performance indicators
- Includes analytical and statistical tools to highlight performance problems
- Produces easy-to-read graphics and reports that allow you to understand operational performance at any level in your organization
- Provides actionable information from which to launch improvement initiatives
- Prioritizes needed action, then tracks and quantifies improvement effort
- Runs on Microsoft Windows® technology for ease of use and compatibility with existing systems
- Produces reports online and in hard copy at each level of the organization
- Interfaces easily with existing systems; does not require expensive software or hardware upgrades
- Does not require extensive in-house IT maintenance
- Is highly scalable; can grow with your business
No modesty here — this is a really cool software program. It’s a 3D simulation of new processes or practices before you roll them out onto the shop floor. It’s an object-oriented simulation software for processes like manufacturing, material handling and workflow. It helps engineers, managers and decision makers visualize and test proposed operations, processes or systems in “cyberspace” before trying it out in real life. The goal, like everything we do here at USCCG, is to boost productivity, eliminate bottlenecks and ultimately enhance your profits.
- Fully object-oriented with C++ integration
- Models created graphically using drag-and-drop
- Amazing 3D virtual reality using drag-and-drop
- Unsurpassed flexibility and power
- Cool factor: High!
Resource Capacity Planning
This invaluable tool gives you a realistic, real-time view of available versus required resources, including people, materials, tools and equipment. It helps you make sure you’re on track to achieve your production plan, each and every day and shift. It’s user friendly and can be customized for each client’s needs.
- Identifies equipment capacities required to meet projected demand
- Considers process maturity and operational learning curves when calculating resource requirements
- Performs long-range material requirements planning
- Adjusts for expected yield and scrap rates
- Provides rough cut (family-level) and detailed (product/line item-level) planning
- Summarizes labor hour requirements by skill set and work area
Microsoft Power BI
What good is the amount of data we have at our fingertips today if we can’t analyze it, interpret it and put it to use, right? That’s where Microsoft Power BI comes in. It creates news you can use. The program is designed to collect the various types of data generated within your business and coalesce it into useful, timely information by which you create actionable insights. It consists of a Power BI Desktop, an online software-as-a-service (SaaS), and mobile apps for Windows, Android and iOS devices. Together, those three components work to wrangle your data into insights you can share with your team.
- AI helps you create machine learning modules, accesses image recognition and more
- AI’s lightning-fast speed gives you actionable insights quickly
- Able to interface with the multiple data sources you may be using, like Salesforce
- Customizable, allowing users to create unique sets of information based on their needs
- Connects data on a dashboard that’s easily viewed and understood
- Includes Report Builder and Report Server features
By using the latest-and-greatest software out there, we help our clients enhance their operations, streamline their day-to-day, stay on top of inventory and create actionable decisions. It’s about managing by the numbers.
For more information about how this software technology can help streamline your operations, call us at 800-888-8872 or email us at firstname.lastname@example.org.
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Supply chain analytics refers to the collection of data and information that provide insights into logistics performance, from inventory management to fulfilling and shipping orders.
How Data Analytics is Changing the Supply Chain Landscape
The ever-increasing reliance on big data is altering the landscape of supply chains as we know them. Historically, the majority of supply chain management was dependent on intuition and experience. However, with the introduction of powerful data analytics technologies, supply chains are now guided by data-driven decision making.
The ever-increasing availability of data is driving this transition. Previously, data was dispersed across numerous silos inside a business, making it difficult to provide a comprehensive perspective of the supply chain. Organizations, on the other hand, may collect and store data from all areas of the supply chain in one central location owing to data warehouses and data lakes. This enables supply chain managers to see the entire picture and make data-driven decisions to increase efficiencies and performance.
The rising availability of strong data analytics tools is another factor pushing the change to data-driven decision making. To examine data in the past, supply chain managers had to rely on manual procedures or limited software tools. However, a wide range of powerful data analytics technologies is now available to assist managers in making sense of massive data sets and uncovering hidden patterns and trends. The transition to data-driven decision making is reshaping the supply chain landscape and has far reaching implications for how businesses function.
Organizations may improve the efficiency and performance of their supply chains by leveraging the power of data, providing them with a competitive advantage in the marketplace.
The Advantages of Data Analytics in Supply Chain Management
Data analytics can aid in the smooth and effective operation of supply chains. Supply chains can uncover patterns and trends in past shipments by examining data from previous shipments. This can help them minimize disruptions and stock-outs while also improving inventory management. Furthermore, data analytics can assist supply chains in optimizing their routes and schedules, as well as tracking their success over time.
Here are some of the primary advantages of employing data analytics in supply chain management:
- Reduced Inventory Costs
- Optimized Production Plans
- More Efficient Cargo Shipments
- Reduced Risks
- Cross-Functional Cooperation
Check out the following infographic by 2Flow which takes a deep dive into ‘Analytics In The Supply Chain’.
Supply chain analytics are guiding managers into the future with data-driven decision making. If you need assistance properly analyzing your data and setting up your supply chain management for success, contact USC Consulting Group today.
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Since Covid people who never heard the term “supply chain” have become painfully aware of what it means and how deeply it impacts their lives. It doesn’t take a viral pandemic to create supply chain disruptions. A factory fire, a natural disaster, or resource scarcity — everyday occurrences — can all lead to items disappearing from shelves.
The recent formula shortage was largely due to a single factory being temporarily shut down.
Product shortages can be a significant hardship for families all around the world. In this article, we talk about how data mining can add stability and predictability to supply chain management.
First, What is Data Mining?
Data mining is the practice of looking at large quantities of information already stored in a database to retrieve new insights from it. Basically, it’s the process businesses use to create actionable knowledge. In the context of supply chain management, the data could pertain to anything from consumer habits, transportation routes, product development, or resource excavation.
Every single action that takes a raw resource out of a mine or jungle and turns it into a product on your shelf creates information. More information than any human (or, for that matter, any room of humans) could ever examine in two lifetimes.
With data mining, data processing, and data analysis, that information can be tamed and channeled toward productive means.
Supply Chain Threats
What variables currently threaten supply chain management? Because there are so many steps taken to turn raw material into a physical product, many variables can interrupt the process. Perhaps there is a storm that halts excavation. A viral outbreak that pauses work at a factory.
Disruptions in the transportation sector. Maybe the demand for a product is so much higher than anticipated that it becomes impossible to manufacture it at an appropriate pace.
All of these scenarios can lead to supply chain disruptions. Through data mining, however, many of them can be mitigated or avoided outright.
Let’s say (with unfortunate accuracy) that there is a recession projected to sweep through the country in the not-so-distant future. Naturally, financial downturns can have a significant impact on the way people shop.
But how can stores and supply chain managers use this information to make sure that there is plenty of the things people need and a relatively modest amount of things that will go largely ignored?
Using historic shopping data, supply chain managers can get a vivid forecast of how people are likely to behave during the next recession. This might mean deemphasizing the production and supply creation of luxury items and focusing more on putting staples on the shelves.
The transportation industry is an enormously important component of supply chain management. Using IoT (internet of things) and data, fleet managers now enjoy unprecedented control over their routes. Maps, even GPS-driven maps, tend to be relatively limited in how granular they get. Route recommendations mostly factor in distances. Even programs that account for speed limits, etc. do so for the benefit of personal vehicles.
Trucking is a different animal. Does this route include a short overpass that the truck will need to detour to get around? Maybe the road winds, requiring a large vehicle to slow down to a crawl.
With historical route data, mined through telematics technology (sensors, mostly) fleet managers now get automated reports that recommend the best routes for their trucks. These recommendations not only factor in arrival times, but can also be calibrated to make recommendations most likely to preserve the condition of the vehicle.
Transportation companies run more efficiently. Products arrive at their destinations on time. It’s a win for everyone.
Adjusting the Chain
In a post-Covid world, one needn’t stretch their imagination to imagine a scenario where something could go wrong within a supply chain. Delays and shortages can happen after only a single break in the chain.
With data, supply chain managers can make reasonable forecasts about potential disruptions, and plan accordingly. Already, the supply chain management industry has moved toward keeping a healthy supply of alternative production lines — often closer to home — so that they can pivot immediately into new solutions when problems arise.
With robust access to data, supply chain managers can receive quicker insights as to when they should reach for these solutions.
The result? Fewer disruptions, and significantly more consumer stability. No more months and months of waiting for a new refrigerator or oven.
*This article is written by Andrew Deen. Andrew has been a consultant for startups in almost every industry from retail to medical devices and everything in between. He implements lean methodology and is currently writing a book about scaling up business. You can follow him on Twitter @AndrewDeen14.
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Supply chain management is a crucial part of every business, which has a wide range of effects, from the streamlined transfer of goods and services to improved customer satisfaction. In this digital age, it has become easier to understand the complexities or risks that affect the supply chain. In general, the supply chain exists in both the services and manufacturing organizations. However, the risk of complexity varies in different organizations.
Managing it effectively is not a simple task. It consists of several challenges and demands to constantly develop a new skill and update the existing one. By implementing effective tactics, you can easily enhance high supply chain performance.
Supply-Chain Essentials Every Manager Should Know
Here are a few things managers should know about managing the end-to-end supply chain from raw material to finished products.
1. Business Communication
If you want to be a leader in supply chain management, you have to communicate well. Depending on whether your company is dealing internationally or locally, being an efficient communicator will surely help you to gain some position in the marketplace. As a supply chain leader, you should be aware of the terms like ROIC, EBITDA, and economic profit. These technical terms must be part of your everyday vocabulary as you would be delivering schedules with suppliers.
2. The Know-How To Negotiate
Negotiation is pivotal in supply chain management. If you want to be successful in this industry, you have to be a good negotiator. Whether you are a lead or participant in negotiation, your skill will influence the relationship of the opposite party.
If you have negotiation skills, you will enter into the discussions looking for an outcome that will satisfy the results. Ask as many questions as you can. It will clear the doubt. An excellent negotiator pays close attention to the opposite parties’ behavior.
3. Customer-First Thinking Is The Key
Supply chain organizations should think about the customer first. This means thinking for the customer when making a decision about the supply chain. In order to gain a good relationship with your customers, you need to spend some time with them and understand their needs and considerations. By focusing on these parameters, you can shape a supply chain that satisfies the customer.
Building a customer-centric supply chain is not easy. All the departments, from suppliers to manufacturers, are involved in the supply chain. You must find new ways to meet customers’ needs and exceed their expectations. In 2021, Assignment Assistance UK formed a customer-centric marketing campaign, and the results were amazing, as the sale ratio exceeded their expectations.
4. Understanding Cost-To-Serve
Cost-to-serve is basically a cross-supply chain method used to focus on process-based costs. It helps in calculating the cost-effectiveness of product and market routes along with the customer profitability. Furthermore, it provides you with a fact-based focus to make decisions on operational changes and service mix for each particular customer.
If you can understand the cost-to-serve, you will be able to make decisions to improve the customer’s outcome. Some supply chain leaders have gifted skills, while others need to train themselves and require practice.
If you apply the cost-to-serve concept to your company’s supply chain activity, then you will be able to build a profitable relationship with customers and the production team. That’s why ease with the cost-to-serve is a good skill that helps you to stand out as a competent supply chain professional.
5. Data Is Everything
Data is crucial in business to formulate strategies, streamline operations, introduce new services, and ensure customer satisfaction. But data is nothing unless it is analyzed. I have seen that most of the decisions in supply chain activities are instinct-based, neglecting data analysis.
Always keep a keen eye on cost and never assume something is great because everyone loves new deals. Look at the facts and data and do not rely on emotions and instinct when making decisions. While concluding a literature review, Bob Tucker describes supply chain analytics as the ability to use data in order to improve all activities across the supply chain.
Since data analysis has been utilized for years, the introduction of new technologies like machine learning or artificial intelligence has led to contribution in today’s supply chain forecasting.
Benefits Of Following Supply Chain Essentials
The supply chain plays a vital role in boosting several business processes, including your relationships. Supply chain management isn’t a simple experiment, but effective supply chain management offers several benefits that improve the bottom line. Let’s look at some of the benefits of effective supply chain management.
a) Better Collaboration
In order to resolve any problem, the supply chain team should be able to share information with stakeholders and communicate with the right people at the right time. Consistent communication improves the relationship, which results in better collaboration and boosted business.
b) Improved Risk Mitigation
Having knowledge of risk help companies in achieving their goals. For instance, 87% of companies believe they could reduce inventory by 22% if they have a better risk management system. This all can be achieved by following the supply chain essentials.
c) Better Quality Control
The quality control process improves once a manager starts following supply chain essentials. Since, data analysis is used for decision making, it helps in producing quality products.
Quality control in the supply chain helps to maintain the company’s reputation. In this modern age, the main goal is to gain a unique place in customers’ minds. For this, the quality control subcontractor gives suggestions to companies to increase their benefits.
The supply chain manager focuses on a better relationship with all the members of the supply chain, including the customers. Today, the supply chain industry is growing rapidly. Hence, making a data driven-approach to supply chain management is a must.
Data is not only driven by effective supply chain management but there are also factors such as good vendor and supplier relationships, effective cost control, securing the right logistics partners and adoption of effective supply chain technologies. An efficient manager takes into consideration all these factors, which result in an improved supply chain process.
*This article is written by Claudia Jeffrey. Claudia is currently working as an Auditor at crowdwriter. She has previously looked after operations and customer service departments in the same firm. Claudia is keen to manage an effective supply chain process and believes in company growth with the customers. She loves to travel and explore the world.
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The eCommerce industry is one of only a few business models that have thrived amidst the financial uncertainty of COVID-19. eCommerce did more than just weather the pandemic, it took advantage of the opportunity to accelerate industry growth by 4 to 6 years, according to an Adobe analysis.
While many assume eCommerce gains are relatively insulated, in actuality, the industry affects many adjacent ones, including artificial intelligence and the Internet of Things. As the rest of the world struggles to catch up financially, eCommerce and related industries are thriving and rapidly changing. Here, we’ll explore how data analytics and manufacturing trends in eCommerce are changing industry operations.
Increased online traffic and changing user patterns have led the eCommerce industry to employ sophisticated data analytics methodologies to predict consumer behavior. eCommerce stores worldwide make use of data analytics to provide product recommendations, perform market analysis, optimize price, and forecast demand.
Business analytics is nothing new, but its ease of use and popularity has increased in recent years. With many consumers worldwide still confined to their homes or local communities, more and more are turning to online shopping. Not only does this expanded consumer base mean more accurate analytics, but it also means more opportunities for business expansion, both in the eCommerce industry and beyond its confines.
Business analytics has become extremely important in business decisions because of its cost-saving abilities and adaptability. Specializations in the field include marketing specialists and business analysts. The U.S. Bureau of Labor Statistics estimates the business and financial professions will expand faster than average over the next decade, making an investment in data analytics an investment in your future.
As data analytics become more widespread, tools like Google Analytics, Supermetrics, and Glew.io are enhancing their user features and accuracy. Analytic usage across industries is easier when these resources are there to help bridge the gap. Each day, they’re becoming more and more accessible to businesses.
eCommerce in Manufacturing
Manufacturing is another sector that’s been heavily impacted by the COVID-19 pandemic and shifting trends in eCommerce. Some of the top eCommerce manufacturing trends include:
- Constant shifts in the industry, including shifts to online business.
- An increased number of D2C sales.
- Using real-time data integration to resolve and reduce order errors.
- Enhancing the customer experience.
- Streamlining the payment process.
Changing trends in manufacturing extend to all commercial industries. If you have a product to sell, increasing efficiency and providing a better customer experience can make all the difference for your business. With the introduction of driverless cars and automated inventory counts, administrative pressures are relieved and businesses can turn their attention to other matters.
The Changing Landscape of Supply Chain Management
When it comes to supply chain management challenges, businesses must understand the problems at hand to identify the most pertinent solutions. Some of the most useful solutions today involve implementing advanced technology, including robotic warehouses, blockchain, and digital supply chain twinning.
Decentralized distribution is also being piloted by companies like Amazon, which is experimenting with drones and has larger ambitions to produce a floating distribution hub. While not all of these innovations have taken flight just yet, as we look toward the future of manufacturing, the eCommerce industry promises much more in terms of automation and agility. Most consumers expect timely, fast delivery via the postal service, and robotics and automation offer the quickest path to meeting high consumer expectations.
Overall, eCommerce is shifting to a digital economy, making use of blockchain for enhanced security and efficiency, while employing more technological and data analytics tools. The rise of chatbots and automated business processes allow business owners to focus on important matters, rather than dealing with trivial mishaps and other time-consuming administrative tasks.
Keeping Up in a Fast-Paced World
Staying on top of eCommerce trends in today’s fast-paced world is not for the faint of heart. It is perhaps for this reason that over the years, business owners have repeatedly held a stagnant mindset when it comes to innovation and improving processes. There’s always an excuse to put it off for later.
However, the fact of the matter is that now is always the time for process improvements. Businesses that stick to the status quo and maintain existing workflows find themselves falling behind financially sooner or later. The risks of stagnation are much greater than the risks of innovation, especially in today’s competitive global marketplace.
The market is continually changing, your competitors are stepping up their game, and consumer demands are increasing each day. Customers expect a smooth on-the-go shopping experience, fast service, and tech-savvy business models. Even for in-person transactions, consumers prefer contactless payment methods and online inventory availability. Their preferences extend far beyond the eCommerce industry itself, meaning progress in fields like automation and artificial intelligence are essential to satisfy new and emerging consumer habits.
With a customer-driven focus, successful eCommerce businesses aim to increase sales through data analytics and boost efficiency through more streamlined websites and supply chain management practices. Don’t allow your business to get left in the dust — eCommerce or not, digital shopping trends are shaping industry operations across the board.
If your business is in need of help to rocket into the future of manufacturing through digital transformation or supply chain optimization, contact the operations management experts at USC Consulting Group. They have been shaping manufacturing operations for over 50 years.
*This article is written by guest author Ainsley Lawrence. View more of Ainsley’s articles here.
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When businesses begin to scale, many of the challenges they face result from added operational complexity and lack of visibility. As organizations start investing more in their business’s functional components, such as inventory production, warehousing, logistics, and order management processing, communication silos and departmental disconnects begin to appear, blurring certain efficiency lines across the company as a whole.
To combat these operational challenges, many companies often rely on Key Performance Indicators (KPIs) to help them make informed decisions about their business as a whole. However, operational data in itself isn’t always useful in its raw state. Organizations typically need to take additional steps to make their system data informative and actionable. This is where data mining comes in.
What is Data Mining?
According to Consumer Notice, data mining is the process of turning raw data points into useful and actionable information. By collecting, sorting, and analyzing large amounts of data from various sources all at once, data mining helps companies discover valuable patterns and trends in their business operations.
Discovering and refining these data points manually across multiple systems would be time-consuming and inefficient for most businesses. Data mining simplifies the process exponentially and provides organizations with the unified data transparency they need to reduce their costs, improve client relationships, reduce operational risks, and increase revenues.
How Does Data Mining Work?
Much like the refining processes of metals and materials, data mining involves several stages before a final product can be achieved. When applying data mining in business analytics, the method uses the following six stages of progression:
- Business Understanding — Identification of business goals and recognize the overall outcome of the data mining process the organization wants to achieve.
- Data Discovery — The implementation of tools and solutions designed to understand all raw data formats and sources for the business and their relevance in the data mining process.
- Preparation — A key part of the “refining” process, often using AI-driven tools to convert unstructured raw data formats into forms that people can understand and quantify.
- Modeling — Several techniques are used to quickly and efficiently sort through several large databases simultaneously, identifying relevant trends and correlation analysis patterns.
- Evaluation — A series of human and machine-driven quality control processes to ensure data is properly mapped and accurately compiled.
- Deployment — Raw data has been fully refined and is now formatted to be used in near-limitless applications, whether represented as metrics, reports, or other digestible and actionable forms.
Data mining plays a fundamental role in business intelligence platforms and will continue to drive the future of data analytics as a whole. As businesses rely more and more on fast, actionable data to inform decisions around their growth and sustainability, data mining solutions will continue to be more readily adopted by all industries. In fact, data mining technology itself has already created roles within organizations such as data analytics specialists and data scientists who dedicate their professions to extracting and presenting new information to the business.
Data Mining in Supply Chain Operations
Management of a supply chain can be a daunting task for organizations of any size. Whether it’s running production facilities, coordinating shipping and logistics, managing inventory across multiple warehouses, or processing and tracking large volumes of orders, supply chains are made up of many individual components, each of them needing their own calibration efforts.
One area of supply chain management that heavily impacts business operations is product transportation. This is especially the case in the current landscape of remote business operations, now heavily reliant on shipping services and efficient vehicle routing. However, while the need for streamlined and profitable logistics coordination efforts has never been higher than it is now, many companies still use outdated and disconnected processes to keep things running.
Legacy logistics planning and tracking processes are often made up of many manual processes and riddled with routing problems that need solving. Some of these problems include inefficient shipping planning that leads to missed deadlines, a lack of visibility between shipment origin and destination, and unknown volumetric capacity or maintenance statuses of vehicles. With so much of these logistics processes disconnected from other critical components of the business, streamlining this data collection with other key business metrics is essential to ensure long-term business sustainability.
The data mining process does this by helping organizations create a unified view of all areas of the business. This is achieved through actionable reports highlighting key performance indicators, giving them the insight they need to improve how they manage product sourcing, efficient execution of their transportation network, and streamline all supporting workflows in and outside of the office.
The use cases of data mining are nearly limitless and can be applied to all business areas. However, by mining for data in supply chain operations, organizations can achieve the visibility they need to balance business efficiency, compliance, and profitability all in one place. This leads to a much more scalable business growth path and ensures long-term sustainability down the road.
*This article is written by guest author Beau Peters. View more of Beau’s articles here.
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USC Consulting Group is a world-class operations management firm that for the past 50 years has helped mining companies around the globe improve their business performance by increasing throughput, reducing costs, eliminating waste, increasing productivity, improving quality and leveraging existing assets.
Your process improvement experience starts with USC digging in to begin to learn what truly makes your mining operations tick. We conduct detailed diagnostics, at the point of execution, whether underground, in the pit, surface, processing plants and support services to gain an understanding of impediments to increased performance. We’ll handpick a team uniquely qualified to address your specific challenges. We’ll observe how you do things around the clock, shift to shift, engaging directly with the people on the front lines – production, maintenance, engineering, and all support departments. Then we’ll collaborate with you to turn our findings into a detailed, workable plan, complete with tools from our well-rounded toolkit.
This is the point when most consultants leave you with a binder and walk out the door. Instead, we’re developing a project plan, organizing work breakdown structure, developing performance goals, determining measurement metrics and making sure our jointly developed strategies get the desired results. Managing data and information in the mining environment is vital for continuous improvement efforts. As part of our implementation process, we will help you enhance how your organization makes use of key data and information. Knowing where the right data and information lives and putting it to value added purposes is essential to managing a successful business. Leveraging enabling technology such as Microsoft Power BI helps to achieve, and then sustain the desired outcomes. Our LINCS® Lean Information Control System will enhance your existing Management Operating System (MOS) by smoothing the change process, providing timely feedback on KPI’s to process owners and actionable business intelligence to key decision makers. We openly share the results of our collaboration to increase and maintain operating excellence, and provide the extra horsepower needed to put ideas (both yours and ours) into action. We help deliver on your goals by empowering your performance. In fact, we’ll help you audit, verify, and sustain results for years to come.
USCCG’s Mining team uses the best of tried-and-proven, and emerging, methodologies to bring about enterprise-wide Lean Transformation, resulting in significant operating and financial gains, all at a very attractive ROI.
Discover more about our work in the Mining industry and contact us today to start your process improvement experience.
Marketplace forces are transforming the pulp and paper manufacturing space, from mass digitization to the maturation of the paperless movement. Many have responded by reinventing their production processes in an effort to access alternative revenue streams and reduce overhead costs, which is particularly consequential as both lumber and sawlog prices continue to surge, according to research from Benzinga.
But how exactly are pulp and paper manufacturers trimming the costs of production? Here are some proven optimization strategies in use in the industry today:
1. Waste reduction
Pulp and paper manufacturing facilities generate a lot of waste. In fact, one researcher found that the average pulp and paper mill produces between 40 and 50 kilograms of dry sludge waste per ton of finished paper product. This byproduct creates considerable operational complexity. Companies must have hazardous waste workflows in place to properly dispose of industrial sludge, supporting infrastructure that greatly affects the bottom line. For instance, organizations must devote considerable resources to establishing processes that comply with Environmental Protection Agency and Occupational Safety and Health Administration standards. Of course, the very presence of waste, especially in great volume, indicates raw material disuse which carries its own budgetary implications.
In recent years, leading pulp and paper manufacturers such as the International Paper Company have turned their focus to reducing waste and the expenses that come with it. IPP implemented new milling techniques that allow production teams to incorporate once-wasted lose fibers into product mixes. The company also boasts robust recovery and reuse programs, which facilitate further reductions in waste. In 2017, IPP engineered a total wastage decrease of 9 percent. The pulp and paper powerhouse intends to reduce manufacturing waste by 30 percent within two years, with the larger goal of achieving zero wastage sometime in the near future.
2. Production optimization
Modern pulp and paper manufacturing facilities leverage fairly advanced production processes, but inefficiencies are still rampant – most notably, because of ineffective supply chain transparency, according to The Forest Trust. How are businesses in the industry addressing these issues? Production optimization via digitization.
Leaders in the space have put into place large-scale data collection programs wherein operational teams leverage industrial sensors and robust information processing, sharing and storage platforms to gather transformative shop floor insights. This data can anchor production improvements with serious bottom line-building potential. For example, one U.K.-based pulp and paper manufacturer increased annual revenues by 11 percent by rolling out a data-backed overall equipment effectiveness initiative, according to the Confederation of European Paper Industries.
3. Strategic sourcing
With raw material costs increasing, companies in the sector must embrace alternative procurement processes to keep overhead expenses under control. Strategic sourcing is perhaps the most effective method for accomplishing this lofty aim, which entails ensuring vendor contract adherence, conducting regular sourcing reviews, and forming relationships with multiple materials providers to achieve the best prices possible. These strategic sourcing practices allow pulp and paper manufacturing companies to keep the cost of production as low as possible, even as raw material prices increase.
Organizations in this niche industry must work quickly to reduce operating costs and build room into the budget to address rising raw material expenses. These strategies can make a significant difference by enabling pulp and paper producers to lay the groundwork for sustained success. But implementing such changes alone can be difficult. USC Consulting Group can help, leveraging decades of consulting experience to help firms in the space catalyze cost reduction.
Contact USCCG today to learn more about our work in the pulp and paper manufacturing space.