Real-time analytics. Streaming analytics. Predictive analytics. These buzzwords are thrown around in the business world without a clear-cut explanation of their full significance. Each approach to analytics presents its own distinct value (and challenges), but it’s tough for stakeholders to make the right call when the buzz borders on white noise.
Which data analytics solution fits your current needs? In this post, we aim to help businesses cut through the static and clarify modern analytics solutions by defining real-time analytics, sharing use cases, and providing an overview of the players in the space.
TL;DR
Real-time or streaming analytics allows businesses to analyze complex data as it’s ingested and gain insights while it’s still fresh and relevant.
Real-time analytics has a wide variety of uses, from preventative maintenance and real-time insurance underwriting to improving preventive medicine and detecting sepsis faster.
To get the full benefits of real-time analytics, you need the right tools and a solid data strategy foundation.
What is Real-Time Analytics?
In a nutshell, real-time or streaming analysis allows businesses to access data within seconds or minutes of ingestion to encourage faster and better decision-making. Unlike batch analysis, data points are fresh and findings remain topical. Your users can respond to the latest insight without delay.
Yet speed isn’t the sole advantage of real-time analytics. The right solution is equipped to handle high volumes of complex data and still yield insight at blistering speeds. In short, you can conduct big data analysis at faster rates, mobilizing terabytes of information to allow you to strike while the iron is hot and extract the best insight from your reports. Best of all, you can combine real-time needs with scheduled batch loads to deliver a top-tier hybrid solution.
How does the hype translate into real-world results? Depending on your industry, there is a wide variety of examples you can pursue. Here are just a few that we’ve seen in action:
Next-Level Preventative Maintenance
Factories hinge on a complex web of equipment and machinery working for hours on end to meet the demand for their products. Through defects or standard wear and tear, a breakdown can occur and bring production to a screeching halt. Connected devices and IoT sensors now provide technicians and plant managers with warnings – but only if they have the real-time analytics tools to sound the alarm.
Azure Stream Analytics is one such example. You can use Microsoft’s analytics engine to monitor multiple IoT devices and gather near-real-time analytical intelligence. When a part needs a replacement or it’s time for routine preventative maintenance, your organization can schedule upkeep with minimal disruption. Historical results can be saved and integrated with other line-of-business data to cast a wider net on the value of this telemetry data.
Real-Time Insurance Underwriting
Insurance underwriting is undergoing major changes thanks to the gig economy. Rideshare drivers need flexibility from their auto insurance provider in the form of modified commercial coverage for short-term driving periods. Insurance agencies prepared to offer flexible micro policies that reflect real-time customer usage have the opportunity to increase revenue and customer satisfaction.
In fact, one of our clients saw the value of harnessing real-time big data analysis but lacked the ability to consolidate and evaluate their high-volume data. By partnering with our team, they were able to create real-time reports that pulled from a variety of sources ranging from driving conditions to driver ride-sharing scores. With that knowledge, they’ve been able to tailor their micro policies and enhance their predictive analytics.
Healthcare Analytics
How about this? Real-time analytics saves lives. Death by sepsis, an excessive immune response to infection that threatens the lives of 1.7 million Americans each year, is preventable when diagnosed in time. The majority of sepsis cases are not detected until manual chart reviews conducted during shift changes – at which point, the infection has often already compromised the bloodstream and/or vital tissues. However, if healthcare providers identified warning signs and alerted clinicians in real time, they could save multitudes of people before infections spread beyond treatment.
HCA Healthcare, a Nashville-based healthcare provider, undertook a real-time healthcare analytics project with that exact goal in mind. They created a platform that collects and analyzes clinical data from a unified data infrastructure to enable up-to-the-minute sepsis diagnoses. Gathering and analyzing petabytes of unstructured data in a flash, they are now able to get a 20-hour early warning sign that a patient is at risk of sepsis. Faster diagnoses results in faster and more effective treatment.
That’s only the tip of the iceberg. For organizations in the healthcare payer space, real-time analytics has the potential to improve member preventive healthcare. Once again, real-time data from smart wearables, combined with patient medical history, can provide healthcare payers with information about their members’ health metrics. Some industry leaders even propose that payers incentivize members to make measurable healthy lifestyle choices, lowering costs for both parties at the same time.
Getting Started with Real-Time Analysis
There’s clear value produced by real-time analytics but only with the proper tools and strategy in place. Otherwise, powerful insight is left to rot on the vine and your overall performance is hampered in the process. If you’re interested in exploring real-time analytics for your organization, contact us for an analytics strategy session. In this session lasting 2-4 hours, we’ll review your current state and goals before outlining the tools and strategy needed to help you achieve those goals.
Professionals in the supply chain industry need uncanny reflexes. The moment they get a handle on raw materials, labor expenses, international legislation, and shipping conditions, the ground shifts beneath them and all the effort they put into pushing their boulder up the hill comes undone. With the global nature of today’s supply chain environment, the factors governing your bottom line are exceptionally unpredictable. Fortunately, there’s a solution for this problem: predictive analytics for supply chain management.
This particular branch of analytics offers an opportunity for organizations to anticipate challenges before they happen. Sounds like an indisputable advantage, yet only 30% of supply chain professionals are using their data to forecast their future.
Though most of the stragglers plan to implement predictive analytics in the next 10 years, they are missing incredible opportunities in the meantime. Here are some of the competitive advantages companies are missing when they choose to ignore predictive operational analytics.
Enhanced Demand Forecasting
How do you routinely hit a moving goalpost? As part of an increasingly complex global system, supply chain leaders are faced with an increasing array of expected and unexpected sales drivers from which they are pressured to determine accurate predictions about future demand. Though traditional demand forecasting yields some insight from a single variable or small dataset, real-world supply chain forecasting requires tools that are capable of anticipating demand based on a messy, multifaceted assembly of key motivators. Otherwise, they risk regular profit losses as a result of the bullwhip effect, buying far more products or raw materials than are necessary.
For instance, one of our clients, an international manufacturer, struggled to make accurate predictions about future demand using traditional forecasting models. Their dependence on the historical sales data of individual SKUs, longer order lead times, and lack of seasonal trends hindered their ability to derive useful insight and resulted in lost profits. By implementing machine learning models and statistical packages within their organization, we were able to help them evaluate the impact of various influencers on the demand of each product. As a result, our client was able to achieve an 8% increase in weekly demand forecast accuracy and 12% increase in monthly demand forecast accuracy.
This practice can be carried across the supply chain in any organization, whether your demand is relatively predictable with minor spikes or inordinately complex. The right predictive analytics platform can clarify the patterns and motivations behind complex systems to help you to create a steady supply of products without expensive surpluses.
Smarter Risk Management
The modern supply chain is a precise yet delicate machine. The procurement of raw materials and components from a decentralized and global network has the potential to cut costs and increase efficiencies – as long as the entire process is operating perfectly. Any type of disruption or bottleneck in the supply chain can create a massive liability, threatening both customer satisfaction and the bottom line. When organizations leave their fate up to reactive risk management practices, these disruptions are especially steep.
Predictive risk management allows organizations to audit each component or process within their supply chain for its potential to destabilize operations. For example, if your organization currently imports raw materials such as copper from Chile, predictive risk management would account for the threat of common Chilean natural disasters such as flooding or earthquakes. That same logic applies to any country or point of origin for your raw materials.
You can evaluate the cost and processes of normal operations and how new potentialities would impact your business. Though you can’t prepare for every possible one of these black swan events, you can have contingencies in place to mitigate losses and maintain your supply chain flow.
Formalized Process Improvement
As with any industry facing internal and external pressures to pioneer new efficiencies, the supply chain industry cannot rely on happenstance to evolve. There needs to be a twofold solution in place. One, there needs to be a culture of continuous organizational improvement across the business. Two, there need to be apparatuses and tools in place to identify opportunities and take meaningful action.
For the second part, one of the most effective tools is predictive analytics for supply chain management. Machine learning algorithms are exceptional at unearthing inefficiencies or bottlenecks, giving stakeholders the fodder to make informed decisions. Because predictive analytics removes most of the grunt work and exploration associated with process improvement, it’s easier to create a standardized system of seeking out greater efficiencies. Finding new improvements is almost automatic.
Ordering is an area that offers plenty of opportunities for improvement. If there is an established relationship with an individual customer (be it retailer, wholesaler, distributor, or the direct consumer), your organization has stockpiles of information on individual and demographic customer behavior. This data can in turn be leveraged alongside other internal and third-party data sources to anticipate product orders before they’re made. This type of ordering can accelerate revenue generation, increase customer satisfaction, and streamline shipping and marketing costs.
In the first part of this series, A Step by Step Guide to Getting the Most from Your JD Edwards Data, we walked through the process of collecting JDE data and integrating it with other data sources. In this post, we will show you how to add business logic unique to a company and host analyzable JDE data.
Adding Business Logic Unique to a Company
When working with JD Edwards, you’ll likely spend the majority of your development time defining business logic and source-to-target mapping required to create an analyzable business layer. In other words, you’ll transform the confusing and cryptic JDE metadata into something usable. So, rather than working with columns like F03012.[AIAN8] or F0101.[ABALPH], the SQL code will transform the columns into business-friendly descriptions of the data. For example, here is a small subset of the customer pull from the unified JDE schema:
Furthermore, you can add information from other sources. For example, if a business wanted to include new customer information only stored in Salesforce, you can build the information into the new [Customer] table that exists as a subject area rather than a store of data from a specific source. Moreover, the new business layer can act as a “single source of the truth” or “operational data store” for each subject area of the organization’s structured data.
Looking for Pre-built Modules?
2nd Watch has built out data marts for several subject areas. All tables are easily joined on natural keys, provide easy-to-interpret column names, and are “load-ready” to any visualization tool (e.g., Tableau, Power BI, Looker) or data application (e.g., machine learning, data warehouse, reporting services). Modules already developed include the following:
Account Master
Accounts Receivable
Backlog
Balance Sheet
Booking History
Budget
Business Unit
Cost Center
Currency Rates
Customer Date
Employee
General Ledger
Inventory
Organization
Product
Purchase Orders
Sales History
Tax
Territory
Vendor
Hosting Analyzable JDE Data
After creating the data hub, many companies prefer to warehouse their data in order to improve performance by time boxing tables, pre-aggregating important measures, and indexing based on frequently used queries. The data warehouse also provides dedicated resources to the reporting tool and splits the burden of the ETL and visualization workloads (both memory-intensive operations).
By design, because the business layer is load-ready, it’s relatively trivial to extract the dimensions and facts from the data hub and build a star-schema data warehouse. Using the case from above, the framework would simply capture the changed data from the previous run, generate any required keys, and update the corresponding dimension or fact table:
Simple Star Schema
Evolving Approaches to JDE Analytics
This approach to analyzing JD Edwards data allows businesses to vary the BI tools they use to answer their questions (not just tools specialized for JDE) and change their approach as technology advances. 2nd Watch has implemented the JDE Analytics Framework both on premise and in a public cloud (Azure and AWS), as well as connected with a variety of analysis tools, including Cognos, Power BI, Tableau, and ML Studio. We have even created API access to the different subject areas in the data hub for custom applications. In other words, this analytics platform enables your internal developers to build new business applications, reports, and visualizations with your company’s data without having to know RPG, the JDE backend, or even SQL!
Global competition, rapid innovation in process and logistics, market volatility, and shifting regulations require manufacturers to anticipate tomorrow’s challenges, circumstances, and demands well in advance.
The good news? Predictive analytics provides your manufacturing operations with the ability to extract valuable insight from the complex and diverse data you’re already gathering, seeing well beyond the horizon into future opportunities.
The challenge? You might not know where to start. For those unfamiliar with predictive analytics, there’s hope. A smorgasbord of use cases are already in practice from Industry 4.0 manufacturers, finally maximizing the data from your SCADA systems, automation tools, and other sources. They’ve identified straightforward paths to greater performance, leaner operations, and higher profit margins. Here’s how the right data and analytics partner can help you bridge the gap – and a few examples of how using predictive analytics in manufacturing is an ideal application for your business.
How to Start Leveraging Predictive Analytics
Meaningful ROI depends on creating the right foundation. For any manufacturing predictive analytics solution to be successful, you’ll need the following foundational elements:
A Single Source of the Truth
The data in your organization is often complex and more than a little chaotic. JD Edwards data alone is often inscrutable to those unfamiliar with F1111 table names, Julian-style dates, and complex column mapping. That’s just one source system. The different data formats pulled from ERPs, MES platforms, QMS software, and other source systems only complicates matters. If you want to extract real value from your comprehensive data, we can help you create a single source of truth. Even if your early use cases lean toward a specific department (operations, quality assurance, supply chain management, etc.), manufacturing is so holistic that it always helps to have the option to tap into your comprehensive data.
Accurate and Consistent Data
The accuracy and consistency of data impact the ability of any organization to make effective predictions. In the manufacturing industry, the range of different data types from a variety of sources makes data quality management a priority and that there are clear relationships across your master data. Otherwise, you’ll be unable to identify discrepancies or duplicates in your data that can capsize your predictions about everything from future demand to workforce needs. We can help you to develop consistent quality across your data ecosystem to ensure your insights are accurate.
A Defined Data Strategy
For predictive analytics or even reporting to offer the greatest value, your organization needs a firm data strategy designed around your highest priorities. Even if your high-level business goals are solidified in your mind, you still need to determine what choices or actions will realize those goals. We can help to bridge the gap between technology and your business goals, achieving them with the shortest route.
Centralized Data
With the magnitude of data at your disposal, you’ll likely need a centralized data lake to different business units to access your panoply of data. As we’ve mentioned, that requires consolidating all of the different source systems (ERPs, MES platforms, etc.) into a single source of the truth, a feat you can’t achieve without data ingestion.
By implementing data ingestion, we can help you to extract data from various sources, transform it into the appropriate format, and load it into a consolidated storage system a predictive analytics solution can use to unveil transformative insight.
There’s no one-size-fits-all when it comes to centralizing your data – even in the manufacturing space. All the different processes and business units within your organization require your data lake or other hub to offer customized accessibility and functionality. By conducting an assessment of your organization, we can determine the right specifications for your predictive analytics tool – and any other data science applications your organization might need.
Accessible Data
When all of your data is centralized and validated, your internal BAs and data scientists actually need to access the data. Through custom development or an out-of-the-box solution, we can help to create dashboards and portals that enable your team to ask questions that empower them to anticipate demand, manage resources, detect potential risks, and maximize your ROI.
If the last big change you made in your organization was to automate processes, then you’re falling behind the curve. Connecting your plants with tech-forward solutions requires you to embrace the interoperability of your enterprise systems and leverage IoT solutions to your fullest. Rather than jumping on the latest trend, we can help your business identify the quickest wins that can transform your profits, performance, and productivity.
Manufacturing Use Cases
With the right partner, it’s clear you can implement effective predictive analytics solutions. But how can you derive the full value of this analytics solution right from the start? These four use cases offer easy wins for any manufacturing organization:
Predictive Maintenance
The machinery used to fabricate new products or maintain operations in your facility endures high-impact, punishing processes. The extreme pressure, temperatures, or range of motion these parts or components undergo make regular replacement a must. An unexpected breakdown can cost as much as $22,000 per minute – depending on the complexity and necessity of the particular machine.
Many manufacturers are seeing the potential threat and implementing a quick win with predictive maintenance. Preventative maintenance routines only gauge conditions in the moment, whereas predictive maintenance uses the aggregate data from real-time sensors on parts, components, or machines to more accurately anticipate:
When they need replacing
When they are performing outside of normal parameters
The probability they will fail within specific high-volume periods
The likely cause of failure
Which equipment presents the highest short-term risk
What type of maintenance activity best solves the given problem or error code
This analytics-powered practice is becoming even more powerful. Through automation and even machine learning capabilities, predictive analytics programs not only receive automated readings but can send out automated maintenance requests. This streamlines the entire process and can reduce maintenance costs by 10% to 40%.
Enhance Manufacturing Execution Systems
The transformation of raw materials into finished goods is more dynamic than most manufacturers acknowledge. Raw materials, machinery components, and supply costs fluctuate due to material availability, shipping location, seasonality, and global demand at the time of purchase. When the materials are in place, specific phases in your manufacturing processes can inhibit the flow of the production line. Plus, open or closed control loops that are improperly tuned, performing poorly with prolonged excursions from their set objective. Your traditional manufacturing execution system (MES) can react to these issues, but a predictive analytics tool can anticipate problems before they happen.
Let’s say you want to reduce material costs. As many as 46.4% of manufacturers struggle with increased raw material costs among their primary challenges. Your MES platform might be able to analyze historical data but lacks the foresight to predict major shifts in raw material costs. This year, there have been plenty. In August, the price of nickel surged to $2,000 a ton in one day. In June, natural rubber prices gradually increased after hitting a 10-year low in November 2018. Plenty of other raw materials or supplies are subject to the same volatility. This increase in raw material expenses strains margins and forces many manufacturers to revise their pricing structure to stay afloat.
Predictive analytics can counteract this encroaching profit erosion. Your organization can save on raw materials by creating a more efficient operation. For perishable products (e.g., food and pharmaceutical products) you can reduce mistakes that result in unavoidable waste. Beyond material costs, you can enhance the capabilities of your MES by identifying other significant cost drivers, pinpointing bottlenecks in your operations, and fine-tuning your control loops to improve operational efficiency and profitability.
Demand Forecasting
With how expensive it is to mass-produce goods in the United States, it’s essential for manufacturers to know future demand if they’re going to properly manage their costs. A great example has to do with the seasonality of consumer goods. Think ice cream in the summertime or cold weather attire during the winter. Using the past history of demand supplemented with a few high impact indicators can explain a lot of variability and help plan large capital expenditures or temporary shutdowns.
The idea of demand forecasting isn’t new to manufacturers worldwide, but predictive analytics brings the use of advanced statistical algorithms to the table. Predictive models can account for a complex web of factors including consumer buying habits, raw material availability, trade war impacts, weather-related shipping conditions, supplier issues, and unseen disruptions.
And it can even establish unknown connections between different variables and drivers influencing demand, helping to evolve your supply management practices.
Improve KPI Analytics for Workforce Management
Manufacturers face an uphill battle when hiring. Shortages of skilled professionals and a competitive labor market make smart workforce management essential for the survival of any manufacturing business.
The issue is that multiple workforce management barriers exist in the manufacturing field. Employee productivity is subject to fluctuating demands from consumers or equipment failure. Looking at the Bureau of Labor Statistics data, annual total separations in the industry have been on the rise year over year. This puts manufacturing organizations in a position where they need to predict staffing, scheduling, training, and productivity challenges with greater flexibility.
By working with a partner to enhance your analytical capabilities, you can evaluate a wealth of data from a variety of sources to obtain deep insight into your workforce:
Consumer demand
Industry hiring trends
Internal employee engagement
Seasonal PTO usage
Safety incidents
Employee productivity
Contract negotiations
KPIs by employee
Using all of this data to create a predictive model can help your organization to create the right workforce balance (be it contingent or full-time) or even anticipate which employees are on the verge of leaving to keep attrition low.
Do you want to improve your plant’s efficiency? We can help identify the right solutions and uses for you. Contact us for a manufacturing whiteboarding session to evaluate your options and start determining how to increase your operational performance and profit margins.
The effects of the pandemic have hit the manufacturing industry in ways no one could have predicted. During the last 18 months, a new term has come up frequently in the news and in conversation: the supply chain crisis. Manufacturers have been disrupted in almost every facet of their business, and they have been put to the test as to whether they can weather these challenges or not.
Manufacturing businesses that began a digital transformation prior to the current global crisis have been more agile in handling the disruptions. That is because manufacturers using data analytics and cloud technology can be flexible in adopting the capabilities they need for important business goals, be able to identify inefficiencies more quickly and be equipped to adopt a hybrid workforce to make sure production doesn’t stall.
The pandemic has identified and accelerated the need for manufacturers to digitize and harness the power of modern technology. Real-time data and analytics are fundamental to the manufacturing industry because they create the contextual awareness that is crucial for optimizing products and processes. This is especially important during the supply chain crisis, but this goes beyond the scope of the pandemic. Manufacturers will want to, despite the external circumstances, automate for quicker and smarter decisions in order to remain competitive and have a positive impact on the bottom line.
In this article, we’ll identify the use cases and benefits of manufacturing analytics, which can be applied in any situation at any time.
What is Manufacturing Analytics?
Manufacturing analytics is used to capture, process, and analyze machine, operational, and system data in order to manage and optimize production. It is used in critical functions – such as planning, quality, and maintenance – because it has the ability to predict future use, avoid failures, forecast maintenance requirements, and identify other areas for improvement.
To improve efficiency and remain competitive in today’s market, manufacturing companies need to undergo a digital transformation to change the way their data is collected. Traditionally, manufacturers capture data in a fragmented manner: their staff manually check and record factors, fill forms, and note operation and maintenance histories for machines on the floor. These practices are susceptible to human error, and as a result, risk being highly inaccurate. Moreover, these manual processes are extremely time-consuming and open to biases.
Manufacturing analytics solves these common issues. It collects data from connected devices, which reduces the need for manual data collection and, thereby, cuts down the labor associated with traditional documentation tasks. Additionally, its computational power removes the potential errors and biases that traditional methods are prone to.
Because manufacturing equipment collects massive volumes of data via sensors and edge devices, the most efficient and effective way to process this data is to feed the data to a cloud-based manufacturing analytics platform. Without the power of cloud computing, manufacturers are generating huge amounts of data, but losing out on potential intelligence they have gathered.
Cloud-based services provide a significant opportunity for manufacturers to maximize their data collection. The cloud provides manufacturers access to more affordable computational power and more advanced analytics. This enables manufacturing organizations to gather information from multiple sources, utilize machine learning models, and ultimately discover new methods to optimize their processes from beginning to end.
Additionally, manufacturing analytics uses advanced models and algorithms to generate insights that are near-real-time and much more actionable. Manufacturing analytics powered by automated machine data collection unlocks powerful use cases for manufacturers that range from monitoring and diagnosis to predictive maintenance and process automation.
Use Cases for Cloud-Based Manufacturing Analytics
The ultimate goal of cloud-based analytics is to transition from having descriptive to predictive practices. Rather than just simply collecting data, manufacturers want to be able to leverage their data in near-real-time to get ahead of issues with equipment and processes and to reduce costs. Below are some business use cases for automated manufacturing analytics and how they help enterprises achieve predictive power:
Demand Forecasting and Inventory Management
Manufacturers need to have complete control of their supply chain in order to better manage inventory. However, demand planning is complex. Manufacturing analytics makes this process simpler by providing near-real-time floor data to support supply chain control, which leads to improved purchase management, inventory control, and transportation. The data provides insight into the time and costs needed to build parts and run a given job, which gives manufacturers the power to more accurately estimate their needs for material to improve planning.
Managing Supply Chains
For end-to-end visibility in the supply chain, data can be captured from materials in transit and sent straight from external vendor equipment to the manufacturing analytics platform. Manufacturers can then manage their supply chains from a central hub of data collection that organizes and distributes the data to all stakeholders. This enables manufacturing companies to direct and redirect resources to speed up or down.
Price Optimization
In order to optimize pricing strategies and create accurate cost models, manufacturers need exact timelines and costs. Having an advanced manufacturing analytics platform can help manufacturers determine accurate cycle times to ensure prices are appropriately set.
Product Development
To remain competitive, manufacturing organizations must invest in research and development (R&D) to build new product lines, improve existing models, and introduce new services. Manufacturing analytics makes it possible for this process to be simulated, rather than using traditional iterative modeling. This reduces R&D costs greatly because real-life conditions can be replicated virtually to predict performance.
Robotization
Manufacturers are relying more on robotics. As these robots become more intelligent and independent, the data they collect while they execute their duties will increase. This valuable data can be used within a cloud-based manufacturing analytics platform to really control quality at the micro-level.
Computer Vision Applications
Modern automated quality control harnesses advanced optical devices. These devices can collect information via temperature, optics, and other advanced vision applications (like thermal detection) to precisely control stops.
Fault Prediction and Preventative Maintenance
Using near-real-time data, manufacturers can predict the likelihood of a breakdown – and when it may happen – with confidence. This is much more effective than traditional preventive maintenance programs that are use-based or time-based. Manufacturing analytics’s accuracy to predict when and how a machine will break down allows technicians to perform optimal repairs that reduce overall downtime and increase productivity.
Warranty Analysis
It’s important to analyze information from failed products to understand how products are withstanding the test of time. With manufacturing analytics, products can be improved or changed to reduce failure and therefore costs. Collecting warranty data can also shed light on the use (and misuse) of products, increase product safety, improve repair procedures, reduce repair times, and improve warranty service.
Benefits of Manufacturing Analytics
In short, cloud-based manufacturing analytics provides awareness and learnings on a near-real-time basis. For manufacturers to be competitive, contextual awareness is crucial for optimizing product development, quality, and costs. Production equipment generates huge volumes of data, and manufacturing analytics allows manufacturers to leverage this data stream to improve productivity and profitability. Here are the tangible benefits and results of implementing manufacturing analytics:
Full Transparency and Understanding of the Supply Chain
In today’s environment, owning the supply chain has never been more critical. Data analytics can help mitigate the challenges that have cropped up with the current supply chain crisis. For manufacturing businesses, this means having the right number of resources. Data analytics allows manufacturers to remain as lean as possible, which is especially important in today’s global climate. Organizations need to use data analytics to ensure they have the right amount of material and optimize their supply chains during a time when resources are scarce and things are uncertain.
Reduced Costs
Manufacturing analytics reveals insights that can be used to optimize processes, which leads to cost savings. Predictive maintenance programs decrease downtime and manage parts inventories more intelligently, limiting costs and increasing productivity. Robotics and machine learning reduce labor and the associated costs.
Increased Revenue
Manufacturers must be dynamic in responding to demand fluctuations. Near-real-time manufacturing analytics allows companies to be responsive to ever-changing demands. At any given time, manufacturing companies have up-to-date insights into inventory, product, and supply chains, allowing them to adjust to demand accordingly in order to maintain delivery times.
Improved Efficiency Across the Board
The amount of information that product equipment collects enables manufacturers to increase efficiency in a variety of ways. This includes reducing energy consumption, mitigating compliance errors, and controlling the supply chain.
Greater Customer Satisfaction
At the end of the day, it is important to know what customers want. Data analytics is a crucial tool in collecting data from customer feedback, which can be applied to streamlining the process per the customer’s requirements. Manufacturers can analyze the data collected to determine how to personalize services for their consumers, thereby, increasing customer satisfaction.
Conclusion
The effects of COVID-19 have shaken up the manufacturing industry. Because of the pandemic’s disruptions, manufacturers are realizing the importance of robust tools – like cloud computing and data analytics – to remain agile, lean, and flexible regardless of external challenges. The benefits that organizations can reap from these technologies go far beyond the horizon of the current supply chain crisis. Leading manufacturers are using data from systems across the organization to increase efficiency, drive innovation, and improve overall performance in any environment.
2nd Watch’s experience managing and optimizing data means we understand industry-specific data and systems. Our manufacturing data analytics solutions and consultants can assist you in building and implementing a strategy that will help your organization modernize, innovate, and outperform the competition. Learn more about our manufacturing solutions and how we can help you gain deep insight into your manufacturing data!
Real-Time Insurance Underwriting
