Supply Chain
Article | August 17, 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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Management
Article | June 21, 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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Supply Chain
Article | May 26, 2023
Warehouse Management System
In Supply Chain, warehouse management acts as the bridge between the supplier and customer. The warehouse facility utilized to consolidate or accumulate products and reduce the transportation cost to achieve economy. Warehouse Management System (WMS) refers to the movement and storage of materials within a warehouse. WMS is part of the Supply Chain Management and concerned with the receipt, shipping and picking of materials.
To effectively monitor the flow of products, WMS utilizes technology devices such as Barcode scanners, Bio-Metrics, and RFID to name a few. A seamless link created between the warehouse facility, order processing and logistics management till shipment. Warehouse management just not limited to the warehouse; it can also a component of Supply Chain Management (SCM) and, when done well, provides a competitive advantage to the business or organization.Supply Chain Management is the management of flow of goods and services including raw materials, work-in-process inventory and finished goods. The markets these days are transcending borders and managing the demand-supply quotient is increasingly getting complex. Production centers are setup at locations where the raw materials and labour are cheaper. Raw materials sourcing and finish goods distribution are done globally.
Supply Chain Management
Thus Supply Chain Management refers to all business processes and activities involved from the procurement of raw materials to the manufacturing and distribution of finished products. SCM in short is the art of providing the right product at the right time, place and cost. As inferred, SCM gets much wider in scope than WMS. However, WMS is perhaps the last mile in the Supply Chain Management system and any hitch in the efficacy of WMS system hampers SCM too.
Conclusion
The primary aim of Supply Chain Management is to match supply with demand. For this to work, the supply chain should be free from bottlenecks such as errant supplies, difficulty in sourcing etc. There seemed significant confusion about the phrases SCM and WMS until recently, and both frequently used interchangeably. However, it been generally accepted that warehouse management refers to the logistics of warehouse, storing, stocking, and also movement of goods. The term Supply Chain has a much broader focus involving suppliers, manufacturers and retailers.By providing customer centric operations in warehousing, companies gain competitive advantage. SCM tools help manage the supplies effectively keeping inventory at optimum levels. The efficiency of SCM relies to a large extent upon the efficiency of WMS. The SCM’s primary concern is to find out the best storage levels, which the WMS attempts to address. Therefore, it is seen that the SCM & WMS are only complementary in nature and not competing. Warehouse Management system also complements the Sales Management System by shortening the sales cycle through quick data access and delivery of quality service, every time.
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Supply Chain
Article | November 11, 2022
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.
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