CRST Van Expedited: Back To the Dunking Booth for the EEOC

Supply Chain

Article | August 17, 2023

At Schneider Electric, meeting our customers’ expectations is a key priority. As concern over COVID-19 (Novel Coronavirus) grows, we are monitoring developments to this situation globally, as well as following local health and government regulations, continually assessing and responding to changes. Our Business Continuity Plan (BCP) has been tested and implemented in geographies impacted. This plan includes health and safety, supply chain, lifecycle management services, and IT infrastructure. Schneider Electric operations meet the criteria of an essential critical infrastructure as defined by most governments. While we do not anticipate interruptions to our operations, local governments may require temporary containment measures. In these cases, we comply with local laws, and in most cases seek support from local authorities to maintain critical business operations as an essential business for our communities.

Warehousing and Distribution

Article | June 16, 2023

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.

Warehousing and Distribution

Article | July 11, 2023

Autonomous robots have transitioned from a futuristic system that only a few enterprises could afford to a sustainable, well-established solution in a wide assortment of warehouse automation projects in recent years. With the flourishing transportation and logistics industry and increasing e-commerce penetration worldwide, innovative technologies are revealing promising opportunities throughout the supply chain. Warehouse Automation: Driving Value in the Supply Chain Historically, autonomous robots have been used to perform tedious and repetitive tasks, necessitating sophisticated programming for setup and incorporation while lacking the dexterity to easily adjust operations. As autonomous robots become more intelligent, their setup times decrease, they need less monitoring, and they are able to work alongside their human counterparts. The benefits for the future supply chain are increasing as autonomous robots become more capable of working day and night with more consistent levels of productivity and quality and performing tasks that individuals should not, cannot, or do not want to do. Autonomous robots drive advancements and add value to the supply chain, primarily by increasing revenue potential and lowering direct and indirect operating costs. Autonomous robots, in particular, can assist: Boost efficiency and productivity. Lower risk, error frequency, and rework rates. Enhance employee safety in high-risk workplaces. Handle low-value, routine tasks so people can work together on strategic projects that can't be done by machines. Raise revenue by optimizing order fulfillment rates, and delivery speed, and, ultimately, increasing customer satisfaction. Sneak Peek into the Future of Autonomous Robot Autonomous robots are expected to witness strong growth in the coming years. These robots will become more common in the future supply chain as technology advances, allowing them to operate with more human-like abilities. Improvements in haptic sensors, for example, will enable these robots to grasp objects varying from multi-surfaced metal assembly parts to fragile eggshells without requiring changes to robotic components or programming. This will encourage companies operating in the industry to increase their research and development activities and introduce innovative and advanced supply chain technologies.

Article | October 15, 2020

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.