Making the connections in supply chains more effective

Supply Chain

Article | May 26, 2023

The complexity of today’s supply chain has resulted in a highly fragmented supply chain ecosystem. Whether it is a global pandemic or an ongoing war, streamlining the moving parts in the supply chain management system is an immense feat in the current climate. The resulting shortage of materials, disruption of transportation, and delays have complicated the supply chain even further. Add to it the expectation to minimize expenses, optimize inventory, and enable quality and customer expectations, and stakeholders at every level of the supply chain are inundated with challenges. This is where using advanced analytics in supply chain can be a game changer for many manufacturers. It can help them gain a deeper insight into their operations and how the supply chain is moving. How Supply Chain Analytics Enriches Supply Chain Management A 360-degree view of the supply chain is crucial for supply chain leaders. A lack of insight can cause costly delays and avoidable disruptions. Obtaining complete visibility in supply chain management, on the other hand, can be difficult. The immense volume of data that many organizations have to deal with makes it difficult to gain proper insight. Supply chain analytics tools help in gaining a better sense of the aggregated data from different parts of the supply chain, such as procurement, ERP in supply chain management, warehousing management, shipping and logistics management, and many more. The more accurate the data is, the simpler it is to use advanced supply chain management analytics to forecast, predict, and plan better in order to maximize the supply chain’s capabilities. Optimizing the Supply Chain: 3 Ways to Do so with Analytics With the range of analytics applications in supply chain in use today, manufacturers have the opportunity to completely transform how to view, manage and strategize. Here are five data analytics supply chain tools to consider in the pursuit of optimization. Demand Forecasting Inventory management is at the heart of supply chain optimization. Not calibrating the inventory stock based on demand and supply for bestselling products and those that don’t move fast can cause either an overstocking or understocking problem. Either way, forecasting the right balance is difficult to achieve, but data analytics can make it simpler. The lack of the right products in stock can heavily impact the bottom line. According to a survey by Logility, 36% of supply chain experts consider inventory optimization the primary reason for adopting analytics. An integrated mapping of retail sales, inventory levels, and the flow of goods will lead to accurate demand forecasting. This data can empower organizations to: Strategize sales promotions Define product pricing Maximize budgets Predict accurate inventory levels Inventory management has a cascading effect on the supply chain, and advanced demand forecasting has helped many organizations achieve an optimum level of inventory for the right products. Predictive Warehousing Maintenance Machine Learning (ML), artificial intelligence (AI), and the Internet of Things (IoT) have been emerging technologies on the supply chain management landscape. By using a mix of these technologies, it is possible for manufacturers to deploy predictive maintenance in warehousing. Predictive maintenance is the process of assessing data generated from the past and in real-time to determine patterns and identify equipment failure and maintenance schedule before a breakdown occurs. This helps manufacturers to: Avoid heavy repair costs Plan spare part supply Slash downtime from equipment failure Eliminate production delays In addition to reducing production bottlenecks, predictive maintenance gives businesses the ability to manage their equipment and optimize their shelf-life. Warehousing Efficiency At the warehouse phase of the supply chain, analytics can help manage the supply chain in more than just one way. The warehouse workflow is just as crucial, as it facilitates a clear view of the condition of goods as well as the optimization of the warehouse space. In addition to warehouse space maximization, quality control is another challenge that warehousing management addresses. Supply chain analytics can enhance warehousing efficiency by: Ensuring the correct storage of goods based on their weight, fragility, and perishability Aligning with resource management like equipment, vehicles Identifying disruptions to storage before they occur One of the advantages of supply chain analytics is getting a deep insight into the warehouse’s operation so manufacturers can identify gaps and take steps to make it more efficient. Transportation Tracking Real-time analytics of transportation and logistics is a crucial piece in the puzzle of supply chain management. For example, the data generated in relation to fuel consumption, weather conditions, and even traffic patterns can help organizations boost their logistics and carrier management. It can help them to: Schedule deliveries Determine better routes Evaluate current routes Strategize their shipping schedule To Wrap it Up To put it simply, a deep insight into the supply chain, inventory management, and warehouse operations is a great way to ensure the supply chain is on track. Effective supply chain management software is an asset for an organization when it is able to leverage the insights and make well-informed strategies to further optimize the supply chain.

Management

Article | June 21, 2023

Efficient demand forecasting techniques transform supply chain management, help optimize inventory levels, streamline operations, improve customer satisfaction, and achieve a competitive edge. Contents 1. How Accurate Demand Forecasting Impacts Business Operations and Profitability? 2. Navigating the Pitfalls of Traditional Demand Forecasting 2.1 Limitations of Manual Forecasting Methods 2.2 Inaccuracy and Inconsistency in Demand Prediction 2.3 Multiple Products and Markets Challenges 2.4 Influence of External Factors on Demand Forecasting 3. Advanced Demand Forecasting Techniques for Supply Chain Management 3.1 Statistical Forecasting 3.2 Collaborative Demand Planning 3.3 Demand Sensing and Real-time Data Analytics 3.4 Agile Supply Chain Management Practices 4 Summing up 1. How Accurate Demand Forecasting Impacts Business Operations and Profitability? Accurate demand forecasting plays a vital role in determining the operations and profitability of a business. By anticipating future demand, companies can more effectively plan their production, inventory management, and supply chain activities to meet customer needs while minimizing costs. Additionally, accurate demand forecasting can aid businesses in enhancing customer satisfaction by proactively meeting customer needs and expectations, improving customer experiences and increasing customer loyalty. To generate actionable insights that drive informed decision-making, businesses must leverage advanced analytics and predictive modeling techniques that combine data from various sources with industry-specific knowledge and expertise. “Businesses that leverage advanced analytics and predictive modeling techniques for demand forecasting report an average of 5% improvement in their supply chain efficiency.” (Source: A survey by Deloitte) Supply chain businesses frequently rely on sales data from the past, which may not be sufficient in the complex and rapidly changing business environment. Businesses might not observe an improvement in operations and profitability if they rely solely on conventional methods. 2. Navigating the Pitfalls of Traditional Demand Forecasting As businesses strive to optimize their supply chain operations and meet customer demand, traditional demand forecasting methods can often hinder their efforts. In this context, it is essential to navigate the pitfalls of such techniques to achieve success in supply chain management. 2.1 Limitations of Manual Forecasting Methods Manual forecasting methods have limitations that can affect demand forecasting accuracy in supply chain management. Frequently based on historical data, these methods can overlook emerging trends in supply chain management and alterations in customer behavior. In addition, manual processes are time-consuming, prone to error, and incapable of incorporating real-time supply chain data. As a result, businesses struggle to optimize supply chain operations and meet customer demand. In addition, traditional forecasting methods can influence the ability to accurately predict demand, resulting in overstocked inventory, delivery delays, and, ultimately, poor customer satisfaction. Inaccurate demand forecasts can also result in poor purchasing decisions and increased carrying costs, negatively impacting profitability. 2.2 Inaccuracy and Inconsistency in Demand Prediction Inaccuracy and inconsistency in demand forecasting pose significant obstacles in managing the supply chain. This is the case in the dynamic business environment, where market conditions can change rapidly, making it challenging for companies to keep up with shifting demand patterns. As traditional demand forecasting methods depend heavily on historical data, they produce inaccurate forecasts that do not reflect real-time market changes. In addition, inconsistency in demand forecasting can also result in a mismatch between supply and demand, leading to missed opportunities or excess inventory. As a result, creating an effect on company’s bottom line in addition to customer satisfaction. 2.3 Multiple Products and Markets Challenges Accurate demand forecasting is crucial to the success of supply chain management. When there are multiple products and markets to manage, it becomes a challenge for traditional demand forecasting. Different products and markets may have varying demand patterns and drivers, making it difficult for businesses to accurately forecast demand. Manual processes and siloed data can hinder visibility and the ability to identify cross-product or cross-market trends, making supply chain optimization operations and meeting customer demand more complex. Managing multiple products and markets is one of the challenges of traditional demand forecasting when businesses operate in various markets with varying customer preferences and demand patterns for products. 2.4 Influence of External Factors on Demand Forecasting External factors can significantly impact the demand forecasting accuracy for supply chain optimization. These factors are often unpredictable, and conventional methods may not account for them. The external factors affecting the supply chain include natural disasters, economic recessions, and sudden changes in consumer behavior. In addition, political and regulatory modifications, such as tariffs or trade agreements, can affect the supply and demand of particular products. Therefore, businesses must incorporate these external factors into their demand forecasting models and advance the process, as traditional demand forecasting methods cannot predict accurate future demand patterns and ensure optimal supply chain operations. 3. Advanced Demand Forecasting Techniques for Supply Chain Management To avoid the above-mentioned pitfalls, companies need to adopt advanced demand forecasting techniques that enable capturing and analyzing huge data from various sources to generate accurate and real-time demand forecasts. 3.1 Statistical Forecasting Statistical forecasting is an advanced method for demand forecasting in supply chain management that utilizes complex algorithms and statistical models to analyze historical data, identify trends, and generate forecasts. This method employs numerous statistical techniques, including regression analysis, time-series analysis, and exponential smoothing, among others. Statistical forecasting can help businesses overcome some of the limitations of traditional manual forecasting methods because it is more objective, data-driven, and capable of identifying trends and patterns which are not apparent with manual forecasting methods. As a result, by utilizing statistical forecasting, businesses can increase demand forecasting accuracy, optimize inventory management, and better align supply and demand, resulting in enhanced customer satisfaction, greater efficiency, and lower costs. 3.2 Collaborative Demand Planning Collaborative Demand Planning combines intensive forecasting algorithms to predict future demand and a set of ML techniques to achieve better demand forecasting. It involves collaboration between suppliers, customers, and other stakeholders. The advanced data and insights sharing technique improve the comprehensive understanding of demand drivers and trends, leading to more accurate demand forecasting. The collaborative approach enables real-time adjustments to demand forecasts, which can help businesses respond promptly to market conditions and customer demand changes. In addition, using advanced analytics and machine learning algorithms can help identify patterns and trends that would otherwise go unnoticed. That enables businesses to optimize inventory levels, reduce under and overstocking, and enhance customer service levels. In the dynamic business environment, it helps transform the supply chain that is better equipped to meet the ever-changing demands of customers. 3.3 Demand Sensing and Real-time Data Analytics Demand Sensing and Real-time Data Analytics are advanced demand forecasting techniques that can assist businesses in overcoming the challenges that multiple products and markets pose to the supply chain. By utilizing real-time data from various sources, such as social media, point-of-sale systems, and weather reports, businesses can better understand customer demand patterns, adjust inventory and production planning, reduce delay, and increase responsiveness. In addition to implementing demand sensing, businesses can begin with sell-in data obtained from supply chain planning or an ERP system in supply chain management and then incorporate all relevant data sources and external factors to broaden the forecasting horizon. 3.4 Agile Supply Chain Management Practices Agile supply chain management practices are a collection of methodologies and strategies emphasizing supply chain operations' adaptability, responsiveness, and flexibility. These practices involve utilizing real-time data analytics, collaborative planning, and other advanced technologies to enable businesses to respond swiftly to changes in customer demand, market conditions, and other external factors. Adopting an agile model allows the organization to act swiftly and decisively and achieve successful business outcomes despite adverse conditions. Agile supply chain management practices can give companies greater visibility and control over their supply chains, enabling them to adapt more effectively and efficiently to fluctuating market conditions in the context of external factors influencing demand forecasting. By cultivating a culture of continuous improvement, innovation, and customer value, agile supply chain management practices have the potential to transform into modern supply chain. 4. Summing up Demand forecasting accuracy is crucial for supply chain management and profitability. Manual forecasting methods hinder operational optimization and customer demand fulfillment. Customer satisfaction, purchasing decisions, and carrying costs suffer from inaccurate forecasting. In order to avoid these pitfalls, businesses can leverage statistical forecasting and collaborative demand. These methods recognize trends and patterns, optimize inventory levels, reduce over- and under-stocking, and improve customer service using advanced analytics and machine learning algorithms. As the supply chain evolves and becomes more complex, businesses must adopt advanced demand forecasting techniques. Implementing these techniques will enable businesses to optimize their supply chain management by better-aligning supply and demand, resulting in increased productivity, decreased costs, and ultimately increased profits.

Warehousing and Distribution

Article | July 11, 2023

Improve supply chain operations with innovative inventory management best practices. Uncover the techniques for achieving exceptional supply chain performance in the B2B competitive marketplace. Effective inventory management is critical for businesses seeking to optimize their supply chain operations and improve their warehousing & supply chain distribution efficiency. By employing demand optimization techniques, inventory management aims to strike the right balance between meeting current and anticipated future demand while minimizing unnecessary inventory costs. Organizations that maintain optimal inventory levels can mitigate challenges associated with inventory, such as overstocking and stockouts. In supply chain management, inventory optimization is vital, as it directly impacts organization’s ability to thrive. For any enterprise selling products, the effective management of goods is essential. Without adequate stock levels for sales or fulfilling customer orders, revenue generation and overall income can be severely hindered. Inefficient inventory management, leading to stock shortages, can create stumbling blocks for businesses. Conversely, improper stock tracking resulting in excess inventory can strain financial resources. As these issues compound, it further contributes to inventory imbalances, eventually leading to bottom-line losses from expired or redundant stock. According to a recent Statista survey, 40% of the supply chain industry has already adopted advanced technologies to optimize its inventory and using networking tools. The above data signifies the importance of optimizing and managing inventory for improved supply chain performance. Inventory analytics, typically overseen by an inventory manager, offer valuable insights that aid in understanding and enhancing inventory performance. Inventory management best practices help achieve effective inventory optimization, crucial data points encompassing products, suppliers, procurement, purchases, and sales that are meticulously tracked within the inventory management system. These data, in turn, serve as the foundation for formulating inventory metrics aimed at demand optimization. This article explains the techniques to optimize and manage inventory with the inventory management best practices that helps overcoming challenges, addressing procedural considerations, and highlighting the significance of implementing these methods. Additionally, it explores the benefits of adopting solution for improved supply chain distribution network. 1. Implementing Standard Inventory Review Systems To enhance supply chain operations, adopting standard inventory review system is essential, which can significantly contribute to inventory optimization efforts. Two effective methods to review systems include the continuous review system and periodic review system. In the continuous review system, fixed quantities of items are ordered in each cycle, providing a steady and consistent approach to inventory management. On the other hand, the periodic review system involves collecting products at predetermined intervals, considering the inventory levels at that specific moment. Embracing these standardized review systems empowers businesses to streamline inventory processes, maintain optimal stock levels, and improve overall supply chain efficiency. 2. Streamline Stocktake Supply chain operations can be improved by streamlining the stocktaking process, which involves meticulously counting and managing inventory. A well-structured stocktake procedure ensures accuracy and prevents losses by keeping staff engaged and focused. To achieve accuracy and earn profits, businesses must: Schedule stocktakes strategically to minimize disruption in regular business operations. Prioritize cleaning and organizing the stockroom before the stocktake to facilitate efficient counting. Clearly define the item count and the counting methods to eliminate guesswork. Conduct comprehensive stock counts, leaving no room for assumptions. By implementing these measures, businesses can optimize inventory management, identify discrepancies promptly, and maintain precise stock records. The streamlined stocktake process contributes to smoother supply chain operations, reduces inventory-related errors, and enhances overall productivity and profitability. 3. Utilize Cloud-Based Inventory Management System Transitioning from Excel inventory management to a cloud-based inventory management system is critical to enhancing supply chain operations. It is considered one of the most used inventory control best practices. Unlike locally-installed applications, cloud-based software offers numerous advantages, enabling businesses to pay for essential features and effortlessly upgrade as needs evolve. Companies can efficiently manage costs with a predictable subscription fee tailored to feature requirements and team size. Seamless upgrades become hassle-free as business growth justifies a move to a more robust platform, ensuring scalability. Additionally, cloud technology provides continuous support, ensuring smooth operations and quick issue resolution. With a dedicated support team on standby, businesses can focus on optimizing inventory management, managing warehouse automation, and driving overall productivity. Embracing cloud-based inventory management is a business-changing decision that unlocks increased agility, accessibility, and cost-effectiveness for long-term success. 4. Implement Adequate Quality Control Practices Enhancing supply chain operations requires the implementation of robust quality control practices. Accurate quality control processes play a pivotal role in maintaining inventory quality, directly impacting customer satisfaction and business growth. Effective steps include developing comprehensive checklists, outlining stock-taking procedures, followed by standard operating procedures to qualify or disqualify products with effective warehouse management systems. By adhering to these protocols, businesses can prevent issues of overstocking or understocking, ensuring customers receive only appropriate merchandise. Companies can strengthen their reputation, increase operational efficiency, and cultivate lasting customer loyalty through this inventory optimization best practice. The seamless integration of quality control practices into the supply chain fosters a thriving business environment built on excellence and customer-centricity. 5. Preparing Well Planned Inventory Budget A well-structured inventory budget is one of the industry-used inventory management best practices to enhance logistics distribution and supply chain processes. Managers commonly utilize an annual inventory budget, meticulously prepared before procuring inventory. The budget is designed to encompass the total cost of ownership for the upcoming accounting period, encompassing materials cost, fixed operational expenses, transportation and logistics charges, redistribution costs, and other miscellaneous expenses impacting the inventory's total cost of ownership. By crafting a comprehensive inventory budget, businesses gain financial clarity, optimize resource allocation, and ensure efficient inventory management throughout the year. A well-planned budget empowers informed decision-making, minimizing financial risks and driving overall supply chain success. 6. Carrying Safety Stock Inventory Operations in the supply chain require safety stock inventory – a strategically maintained surplus of inventory to protect against market demand and lead time fluctuations. By implementing safety stock, businesses can avoid revenue loss, customer attrition, and declining market share that may arise in its absence. Safety stock is vital with the advantages it offers: Protection against sudden surges in demand. Prevention of stockouts, ensuring uninterrupted customer service. Compensation for inaccuracies in market forecasts. A buffer for longer-than-expected lead times, averting production delays. Incorporating safety stock as a fundamental inventory management best practice empowers companies to achieve operational supply chain resilience, optimize customer satisfaction, and maintain a competitive edge in the dynamic market landscape. 7. Optimize Inventory Turnover Rates Optimizing inventory turnover rates is a critical metric that frequently measures inventory sold or used within a specific timeframe, typically a year. Calculating turnover rates provides valuable insights into market demand, identifies obsolete stock, and guides inventory management decisions. Inventory turnover can be improved through various strategies, such as experimenting with pricing to attract more customers and boost sales, liquidating obsolete stock to free up capital and storage space, forecasting customer demand accurately to maintain optimal inventory levels, and redistributing inventory among warehouses for better stock availability. By optimizing inventory turnover rates, businesses can reduce carrying costs, minimize stock obsolescence, and enhance overall supply chain efficiency, as well as gaining competitive advantage in the market. “It’s been my observation that the business world has a weak understanding of inventory management and control. They are trained shallowly, and sometimes they apply only shallow experience to their practices. Sometimes, that works out great. In my 30 years of experience, however, I have seen that a lot of money can be saved by training and managing inventory control in-depth.” -Inventory Control Expert Dr. Pyke Final Thoughts Adopting advanced inventory management best practices is crucial for supply chain optimization in the competitive B2B environment. Standardized inventory review systems and streamlined stocktakes optimize control and accuracy, minimizing disruptions. Cloud-based inventory management offers scalability and continuous support, facilitating data-driven decisions. Adequate quality control ensures inventory quality, driving customer loyalty. Well-planned budgets lead to financial clarity and precise resource allocation. Safety stock inventory and optimized turnover rates fortify businesses against uncertainties, boosting efficiency and profitability. By embracing these practices, logistics professionals can enhance supply chain potential, achieve lasting success, and gain a competitive advantage in the market. With a data-focused approach, these strategies pave the way for streamlined operations, stronger customer relationships, and sustained growth.

Article | June 27, 2020

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.