Warehousing and Distribution
Article | July 17, 2023
Warehouse logistics is the heart of any supply chain operation, assimilating and dispatching goods to ensure availability and timely delivery. With more consumers turning to e-commerce, it’s important for businesses of all sizes to bolster the supply chain to handle the e-commerce business model. According to research conducted by BigCommerce, the top three factors that influence consumers’ online purchasing decisions are convenience, cost and free shipping. In fact, the National Retail Federation (NRF) found that 75% of consumers expect delivery to be free even for orders under $50.
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Warehousing and Distribution
Article | July 11, 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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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.
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Software and Technology, Supply Chain
Article | July 14, 2022
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.
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