Leveraging Big Data Analytics For Quality Control And Optimization In Industry 4.0 – You can continue, but to best experience this page, Edge, We recommend using Firefox and Chrome.

The trend of Big Data Analytics is to analyze large amounts of data to discover patterns in the past; It refers to highlighting real-time changes in the current situation and creating projections and predictions for the future. This trend includes different processing techniques of structured data, which include numbers and values stored in a visible and predefined format, as well as unstructured data coming in different native formats, such as video and audio files from sensors and social media. Posts

Leveraging Big Data Analytics For Quality Control And Optimization In Industry 4.0

The importance of data has been well understood by logistics industry operators in general for decades. Without data and analysis; It is impossible to optimize or even prepare things in advance. For these and other visibility reasons, logistics leaders use sensors to collect and display information. They used dashboards and other technologies. As the use of data collection tools increases, so does the amount of raw data found on social media and the Internet. The incoming data rate far exceeds the data collection rate at 463 quintillion (1018) bytes (or 463 billion GB). Data generated every day in 2025. to identify large amounts of unstructured and traditional data that can be easily manipulated on a spreadsheet; Experts call the former “big data.”

How Predictive Analytics Transforms Logistics And Supply Chain

Processing and analyzing big data in real-time using Artificial Intelligence (AI) algorithms and other technologies is an entire field of study. But here are 4 types of big data analytics that can be applied to use cases. Provides entire supply chains: Description; diagnosis, Predictive and Prescriptive Analysis.

Descriptive analysis tries to understand the existing situation and answer the question of what happened while diagnostic analysis tries to investigate why something happened. At the same time, Predictive analytics, as the name suggests, generates predictions and forecasts for what will happen in the future, while prescriptive analytics uses historical and contextual data to suggest changes to what should be done.

The trend of big data analytics has a considerable impact on logistics. without immediately changing the look and feel of the supply chain; The improved visibility and optimized decision-making that results from this approach enables strategic optimization across the supply chain segment; significantly improve service levels; from more efficient pallet storage; To better handle customer cases. According to the understanding Big data analytics is closer to the logistics industry than any other industry. Most, if not all, of them. Most logistics leaders have leveraged big data to make strategic decisions in recent years, and the trend will soon become a standard way of doing business and integrated into logistics services.

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‘Analytics’ by business leaders working with big data; ‘Processing’ and ‘implementing findings’ are said to be the three most feared aspects of working with big data.

One of the main opportunities for big data analysis is the systematic, current conditions on the ground in facilities such as warehouses and hubs. Providing logistics players with real-time visibility that is easy to filter and digest.

At the end of the simulation, Big data processing from sensors can reveal the location and current condition of assets such as roller cages – e.g. They can be displayed if they are currently in use or if they are damaged. Analyzing inventory data from sensors can determine if stocks are low. It can help you determine if there is no or empty space on the pallet racks. For diagnostic purposes; The analyzes can reveal how to identify global or regional events that frequently disrupt the conveyor belt of certain products or have a significant impact on inventory levels of specific products.

Use Cases Of Data Analytics In Hospitality Industry

At the same time, When it comes to forecasting, Analyzing sensor data on assets such as machinery and vehicles can support predictive maintenance processes; Flag broken items that should be inspected and repaired before they break. For statistics, Forecasts can be made to predict incoming orders and delivery patterns during future peak and off-peak seasons. Finally, Prescribing analytics can compare current usage and inventory plans to better allocate inventory space for different stock keeping units (SKUs). For example, the Applied Analytics team conducts studies for clients that can recommend changes based on identified patterns in the data; For example, For example, where inventory exceeds original demand forecasts or where safety stock runs below critical levels. For assets, Historical data can be processed to recommend the best place to store tools and other items to limit the distance traveled to retrieve them.

In general, Big data analytics can give logistics organizations the visibility they need to optimize transportation storage and movement through facilities, as well as improve utility and asset life.

The trend of Big Data Analytics offers various solutions to overcome the challenges that logistics organizations often face in the transportation and delivery parts of the logistics chain.

Big Data Analytics Explained

For descriptive analysis, Big data processing is used to monitor service levels on a given route or lane; Disruptions such as truck breakdowns can be detected in real time when they occur. In addition, Data from dozens of sensors gives supply chain organizations visibility into whether products are being shipped in high-quality condition or damaged along the way. With diagnostic analytics, companies can see why certain shipments are late – perhaps because the route schedule coincides with a rush hour traffic schedule. For example, For example, transit through understaffed ports.

For predictive purposes; Various data sources can help quantify the risk of disruption across segments of the supply chain. for example, Everstream Analytics uses global news feeds and other proprietary data to make forecasts for its supply chain clients across more than 30 risk categories, including natural disasters and political violence. In doing so, it calls for $100 million in savings from transportation mode optimization to reduce disruption revenue losses by 30%. For reference information; Logistics managers can view past data, maximize vehicles, and adjust schedules and fleet sizes to ensure products are delivered on time. Such analyzes can show supply chain managers the wisdom to change the trends and footprints where historical lows have occurred in a particular segment of the supply chain.

In general, Big data analytics can improve shipping efficiency and ensure shipments are delivered in good condition and on time at lower costs.

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Past Use Case Relationships for the Future of Logistics Next Use Case Relationships for the Future of Logistics

Auditing current and potential partners; Be it a robot vendor or a packaging vendor; Part of risk and resilience due diligence assessments can be tedious work. By using big data analytics to drive decisions and even automating some evaluation processes, logistics organizations can save time, It can save money and risk.

At the end of the description, Data from sensors and other sources can be used to assess the timely delivery and quality of offers from suppliers in real time. This, coupled with analysis, Logistics managers can help find patterns and understand what makes some suppliers better than others; It can inform organizations of the variables and attributes to look for when evaluating partners in the future. for example, If the results of the diagnostic study indicate that suppliers from certain regions are delaying their shipments due to customs controls; This signals to inventory planners that there may be a problem.

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Predictive information can help in vendor selection. Processing the various attributes of potential vendors in the supply chain can provide automatic estimates of each vendor’s likelihood to meet the logistics organization’s needs in emergency situations such as natural disasters in a certain region. Finally, ရောင်းသူ၏အတိတ်ကို ခွဲခြမ်းစိတ်ဖြာခြင်းဖြင့်၊ ထောက်ပံ့ပို့ဆောင်ရေးကုမ္ပဏီသည် စာချုပ်သက်တမ်းတိုးခြင်းဆိုင်ရာ အကြံပြုချက်ကို လက်ခံရရှိနိုင်ပါသည်။ ဤအမျိုးအစားခွဲခြမ်းစိတ်ဖြာမှု၏ရလဒ်များသည် အဖွဲ့အစည်းအား လက်ရှိနှင့် အလားအလာရှိသော မိတ်ဖက်များကို အဆင့်သတ်မှတ်ပြီး အမျိုးအစားခွဲခြားရန် ကူညီပေးသည့်အပြင် စာချုပ်တစ်ခု သို့မဟုတ် ဝယ်ယူမှုအစီအစဉ်ကိုလိုက်ခြင်းကဲ့သို့သော မဟာဗျူဟာမြောက်လုပ်ငန်းဆုံးဖြတ်ချက်များကို လွယ်ကူချောမွေ့စေပါသည်။

In general, ကြီးမားသောဒေတာခွဲခြမ်းစိတ်ဖြာမှုသည် ပေးသွင်းသူများနှင့် ရောင်းချသူများနှင့် ရှိပြီးသား သို့မဟုတ် အလားအလာရှိသော ပူးပေါင်းဆောင်ရွက်မှုများကို အကဲဖြတ်သည့်အခါ ထောက်ပံ့ပို့ဆောင်ရေးအဖွဲ့အစည်းများအတွက် အသုံးဝင်သောကိရိယာတစ်ခုဖြစ်သည်။

ထောက်ပံ့ရေးကွင်းဆက်အတွင်း ကြီးမားသောဒေတာခွဲခြမ်းစိတ်ဖြာမှုကို မကြာခဏဆိုသလို အသုံးပြုသော်လည်း သုံးစွဲသူအတွေ့အကြုံကို မြှင့်တင်ရန်အတွက် ထောက်ပံ့ပို့ဆောင်ရေးအဖွဲ့အစည်းတစ်ခု၏ ဖောက်သည်မျက်နှာစာလုပ်ငန်းဆောင်တာများကို မြှင့်တင်ရန်အတွက်လည်း အသုံးပြုနိုင်သည်။

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သရုပ်ဖော်ခွဲခြမ်းစိတ်ဖြာမှုကို အသုံးပြု၍ B2B နှင့် B2C ဖောက်သည်များအား လုပ်ငန်း၊ age ပထဝီဝင်ဒေသ၊ မှာယူမှုအရွယ်အစားနှင့် လိုအပ်ချက်များကဲ့သို့ ၎င်းတို့၏ဆက်စပ်သောအရည်အချင်းများနှင့်အတူ မတူညီသောအမျိုးအစားများဖြင့် အုပ်စုဖွဲ့နိုင်ပါသည်။ ထို့နောက် ထောက်ပံ့ပို့ဆောင်ရေးအဖွဲ့အစည်းများသည် ဖောက်သည်အခြေခံနှင့် ထောက်ပံ့ရေးကွင်းဆက်ပြောင်းလဲမှုများကြောင့် မည်သူ့ကို ထိခိုက်နိုင်သည်ကို ထောက်ပံ့ပို့ဆောင်ရေးအဖွဲ့အစည်းများမှ ပိုမိုကောင်းမွန်စွာ နားလည်နိုင်စေရန် ဤအမျိုးအစားများကို ဒက်ရှ်ဘုတ်တွင်ကဲ့သို့ အမြင်အာရုံဖြင့် ပြသနိုင်မည်ဖြစ်သည်။ Meanwhile, စျေးနှုန်းမွမ်းမံမှုများ၊ အဆင်ပြေသောအချက်များ သို့မဟုတ် အခြားပြောင်းလဲမှုများကြောင့်ဖြစ်စေ ဖောက်သည်များဆုံးရှုံးခြင်း သို့မဟုတ် သီးခြားကမ်းလှမ်းချက်တစ်ခုအတွက် ဦးစားပေးမှုများကို အဖြေရှာသည့်ခွဲခြမ်းစိတ်ဖြာမှုများက ကူညီပေးနိုင်သည်။

ဖောက်သည်ရင်ဆိုင်နေရသော အသုံးပြုမှုကိစ္စများအတွက်လည်း ခန့်မှန်းချက်သည် အဖိုးတန်ပါသည်။ ၀ယ်လိုအား ခန့်မှန်းချက်သည် ထောက်ပံ့ရေးကွင်းဆက်တွင် ပိတ်ဆို့မှုများ သို့မဟုတ် စက်ရုံများနှင့် မော်တော်ယာဉ်များကို အသုံးချမှု နည်းပါးသွားလေ့ရှိသည့် ထောက်ပံ့ပို့ဆောင်ရေးအဖွဲ့အစည်းများကို ကူညီပေးသည်။ for example, Applied Analytics အဖွဲ့