Software and Technology, Supply Chain
Article | August 3, 2022
Data volume in supply chains is usually enormous. Analytics applied to the supply chain help make sense of the pile of information by identifying patterns and drawing conclusions.
Contents
1 Importance of B2B Supply Chain Analytics
2 Obstacles in Supply Chain Analytics Integration
2.1 Barriers in Collecting and Processing Data
2.2 Insufficient Technical Skills
2.3 Issues in Managing and Integrating Data
2.4 Inadequate Analytics and Insights Generation
3 Addressing Supply Chain Analytics Complexities for Better Decision-Making
3.1 Leveraging External Expertise
3.2 Enhancing Collaboration and Communication
3.3 Improving Data Quality and Governance
3.4 Developing Analytics Capabilities
4 Power of Successful Supply Chain Analytics Integration in Transforming Businesses
5 Conclusion
1 Importance of B2B Supply Chain Analytics
Supply chain analytics has become a pillar of contemporary business strategy, enabling organizations to leverage data insights and enhance vital supply chain processes. By utilizing real-time data analytics, businesses can streamline their supply chain operations, boost productivity, and increase customer satisfaction. With unparalleled visibility into key performance indicators, B2B supply chain analytics provide the opportunity to identify inefficiencies, reduce costs, and react swiftly to ever-changing market dynamics, streamlining the process for supply chain business analysts who manage supply data.
Leveraging the power of supply chain analytics tools is important for the success of modern businesses in the age of data-driven decision making. With the ability to optimize inventory levels, manage lead times, and reduce transportation costs, supply chain data analytics provides a competitive advantage that can increase the efficiency, productivity, and profitability of businesses of all sizes and in all industries.
2 Obstacles in Supply Chain Analytics Integration
Supply chain analytics integration has become increasingly critical for companies seeking to optimize their supply chain operations. However, several obstacles hindering successful implementation often complicate the integration process; hence, understanding and knowing them in advance is vital for smooth operations.
2.1 Barriers in Collecting and Processing Data
Effective data collection and processing are critical in generating accurate insights to drive supply chain analytics decision-making. However, it comes with challenges; the most critical obstacle is data silos, where data is stored in isolated systems or departments, leading to difficulties in accessing and integrating it. It gets more complex when different departments or partners use varied data formats or standards. Additionally, cleaning and processing data is also challenging, as it involves identifying and eliminating duplicates, inconsistencies, and errors that can negatively impact analytics accuracy.
2.2 Insufficient Technical Skills
Successful supply chain analytics integration depends heavily on technical skills and knowledge. Insufficient technical talent and expertise are significant barriers to successful integration. The integration process requires specialized technical expertise and the supply of skilled professionals with expertise in ETL, statistical analysis, knowledge of ML, IoT, SQL, and more in managing supply chain analytics Talent lacking technical skills cannot accurately interpret data, leading to ineffective
decision-making. Preliminary data analysis, processing, and visualization due to a lack of technical expertise results in suboptimal decision-making, which can be costly for businesses.
2.3 Issues in Managing and Integrating Data
Data integration combines data from various sources and formats to create a unified view. However, businesses face data governance, quality, and standardization issues, resulting in incomplete or inconsistent data. Lack of accurate information reduces the efficiency of supply chain analytics and impacts decision-making. Additionally, data management is complex, and business face difficulties creating effective data management processes, resulting in problems related to storing, retrieving, and updating data.
2.4 Inadequate Analytics and Insights Generation
Obtaining valuable insights from enormous data collected during the supply chain process requires advanced analytics tools and technologies. Many businesses, however, continue to rely on traditional reporting methods, which limit the range and complexity of insights generated. The lack of expertise in data analysis and visualization can lead to poor interpretation and use of data, resulting in suboptimal decision-making. In addition, businesses experience difficulty identifying relevant data sources or may struggle to establish the necessary data governance frameworks to ensure data quality and accuracy.
3 Addressing Supply Chain Analytics Complexities for Better Decision-Making
Supply chain analytics can provide valuable insights, but the complexities involved in analyzing and interpreting data can be a significant hurdle. Learning the strategies for addressing these complexities to improve decision-making in supply chain management has become essential.
3.1 Leveraging External Expertise
Leveraging external expertise can be a powerful strategy for addressing supply chain analytics complexities related to collecting and processing supply chain data during integration. External experts bring specialized skills, experience, and knowledge that may not be available in-house, enabling organizations to overcome talent shortages and expand their capabilities. Furthermore, consultants, data scientists, or technology providers provide an objective viewpoint on the organization's data and processes, identifying areas for improvement and optimizing performance. By collaborating with external experts, organizations can access the latest tools, technologies, and best practices, ensuring that their supply chain analytics are up-to-date and relevant.
3.2 Enhancing Collaboration and Communication
Enhancing collaboration and communication can effectively address the challenge of insufficient technical skills in thriving supply chain analytics integration. By promoting cooperation and cross-functional communication, organizations can leverage the skills and expertise of team members from various departments to fill gaps in technical knowledge. This approach can also help to break down data silos and improve data sharing and integration. In addition, collaboration and communication can facilitate knowledge transfer, enabling team members to learn from one another and develop a more comprehensive understanding of the supply chain analytics process. Ultimately, this can lead to improved decision-making, as a more skilled and knowledgeable team can generate more accurate and insightful analytics.
3.3 Improving Data Quality and Governance
Businesses ensure accuracy, completion, and up-to-date data by establishing standardized processes and protocols for collecting, storing, and analyzing data. Data quality checks, including data cleansing and normalization, can help eliminate errors, redundancies, and inconsistencies that can negatively impact the accuracy and usefulness of analytics. In addition, effective data governance, including establishing data ownership, security, and privacy policies, helps ensure that data is managed and shared appropriately across the organization. As a result, it reduces the risk of data breaches, compliance violations, and other data-related issues, ensuring that organizations have access to reliable data for better decision-making.
3.4 Developing Analytics Capabilities
Developing capabilities significantly help organizations overcome inadequate analytics and insights generation challenges in analytics integration. Investing in advanced analytics tools and platforms like technical skills, data infrastructure, and advanced supply chain analytics techniques help businesses generate real-time, accurate, and actionable insights from collected data. Developing analytics capabilities requires creating a culture that values data and analytics, establishing robust data governance frameworks, upskilling the workforce and creating cross-functional teams collaborating on data-related projects. In addition, it helps businesses gain a competitive advantage.
4 Power of Successful Supply Chain Analytics Integration in Transforming Businesses
Effective supply chain analytics integration is revolutionizing business operations. Real-time and supply chain predictive analytics have helped businesses gain unmatched transparency in their supply chains, enhance critical processes, improve operational efficiency and customer satisfaction, and experience revenue growth and profitability. The ability to identify inefficiencies and supply chain optimization opportunities enables businesses to effectively allocate resources and reduce expenses. In addition, successful supply chain analytics integration enables businesses to respond quickly to changing market dynamics, optimize inventory management, and strengthen the resilience of their supply chains.
Businesses are leveraging the power of big data analytics to disrupt and transform supply chain at all levels. The concept of data, which was once a fundamental component of digital supply chain transformation, is now revolutionary. Therefore, it is essential to achieve advancements in supply chain analytics integration and management.
5 Conclusion
With technological and data analytics advancements, businesses can utilize real-time data insights to make data-driven decisions, optimize supply chain processes, and improve customer experiences. Integration of supply chain analytics is crucial for supply chain businesses of all sizes. Utilizing supply chain analytics software can further streamline integration as well as enhance data analytics and supply chain management.
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Software and Technology, Supply Chain
Article | July 18, 2022
Labor costs, on an average, constitute more than 65% of the expenses for a warehouse. It is a best practice to review the operation of your warehouse before implementing automation, but it also has a significant impact on production and operational efficiency. The processes in a disarrayed warehouse cannot easily be automated as automation heavily relies on warehouse layout optimization, as well as standardized processes and procedures.
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Management
Article | June 21, 2023
Over the past decade, transportation and logistics have witnessed an immense transformation, from using automotive vehicles to drones and paper tags to advanced geotags. Also, the introduction of novel technologies and incorporation of innovative solutions such as artificial intelligence and blockchain has further reshaped the sector.
How is Rising Complexity Triggering the Adoption of Novel Technologies?
Today's supply chain is, in a word, complex. It has so many complexities, from ever-rising consumer expectations and disruptive events to globalized sourcing and omnichannel demand, that it is now being compared to a modern-day Gordian knot. With complexity comes ambiguity, particularly in the transportation and logistics industry.
For instance, importers are expected to find cost-effective and efficient ways to transport goods from manufacturing facilities, which are frequently located halfway around the world, to distribution centers and then, ultimately, into the hands of consumers. In addition, e-commerce has further added to the complexity by introducing new channels into the jumble along with the introduction of same-day or next-day delivery standards with higher transparency and tracking features by leading players such as eBay, Walmart, and Amazon Here are a few prominent technologies assisting logistics companies to counter these complexities.
Data Analytics
Big data analytics aids in the more accurate and efficient planning of operational processes, services, and strategies, allowing businesses to align with emerging consumer needs.
Artificial Intelligence (AI) and Machine Learning
AI and Machine Learning have become the most sought-after tech in the transportation food chain. It assists businesses in enhancing operational functionality, boosting productivity and speed, designing optimal transit networks and routes, and improving safety through predictive risk calculation and elimination.
The Internet of Things (IoT)
With the rising need for transparency and tracking of shipped goods, IoT is garnering immense popularity across the transportation and logistics sector. It enables businesses to monitor and track their goods in real-time with the help of geo-tagging, RFID tags, and GPS, resulting in fewer delays in deliveries.
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Software and Technology, Supply Chain
Article | August 3, 2022
In the past couple of years, supply chain management has witnessed massive disruption. The implications of the pandemic have put pressure on manufacturers to revisit and reimagine how they manage their supply chains. This is why ERP software is so mission critical in supply chain management. It not only helps supply chain chiefs reduce overhead costs but also enhance efficiencies and timely deliveries.Here are a few ways ERP plays a role in supply chain management to keep it moving.
Complete Visibility
Having a 360-degree view of the supply chain is integral to making the right decisions regarding procurement and purchase of materials and inventory. It also allows suppliers to have complete control and course correct when necessary. The insight into the complete supply cycle allows suppliers to respond to customer queries better.
Inventory Tracking
ERPs are truly a one stop shop for manufacturers. They feature extensive inventory tracking on one system so manufacturers are in the loop about inventory control and can better optimize their inventory and resources. For manufacturers it is crucial to know where their inventory is, what has been shipped, what is on hand at all times. With ERPs, it becomes cheaper to keep a stock of the inventory and place orders when it’s running low.
Vendor Performance
Vendor performance is an essential aspect of supply chain management. Being able to compare vendors, measure certain quality and quantity metrics and identify bottle necks help suppliers choose the right vendor as well as gives purchasing departments the power to negotiate for better pricing by consolidating purchase. ERPs allow suppliers to do all the above and more.
Procurement
Procurement of goods can be a complex process if done without the right tools. It also impacts the whole supply chain so it is doubly imperative to ensure the procuring and supply of goods is on time. This is especially true in an environment of custom manufacturing. Add to that the requirement of procuring products with lengthy lead times, manufacturers need to take into considerations product that need to be ordered long before they are even designed. ERPs allow manufacturers to keep all departments including engineering and warehousing to work in sync and plan to procure goods on time.
Real-time Reports
Reporting is labor intensive and can be inefficient when it comes to gathering and processing data. With ERPs, manufacturers can generate reports in real-time and all manual data collection processes are replaced with automation, saving time and money in viewing the insights into the movement of products in the supply chain. It enables manufacturers to get a better understanding and make timely decisions that improve the overall efficiency of the supply chain.
To Conclude
ERP systems offer endless opportunities for manufacturers to improve their processes, save time and resources and optimize and enhance inventory planning. With the right tools, it is possible to establish supply chain management that outperforms and is resilient even in disruptive times.
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