Supply Chain
Article | May 22, 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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Warehousing and Distribution
Article | July 17, 2023
The pandemic has had a significant impact on the logistics and shipping sectors, forcing businesses to rethink their supply chain tactics. Fresh COVID- Supply systems around the world are already under threat from 19 outbreaks. China's measures to stop the spread of Omicron by shutting down shipping terminals caused a lockjam of cargo ships. A similar scenario was seen in US and European ports that were congested as a result of a significant increase in cargo.
Additionally, these difficulties have been made worse by the tremendous staff shortage in the US. Although there are more job openings than before, the total strength of the workforce has taken a hit. A report by WNS and Corinium Intelligence reveals that over 60% of shipping and logistics organizations have amped up the automation aspect of their operations by two years. So, what does that mean for the future? In this article, we will talk about the four most promising trends that will define the shipping and logistics industry in 2025.
Live Monitoring Powered by Big Data and the Digital Twin Technologies
Big data is simplifying the ability to be agile, efficient, and cost-effective, and connectivity with other technologies and elements of the infrastructure is driving a real-time view of the supply chain further. Other innovations such as drones and self-driving trucks will contribute to this real-time update and processing of massive amounts of data. In addition, tracking software for freight will provide even greater visibility to customers and enterprises.
Automated Planning Through Collaboration Between Humans and Machines
Artificial intelligence (AI)AI in shipping and logistics has grown exponentially in areas of planning. AI applications are used to alter transportation and route planning. According to Gartner, by 2030, AI augmentation, will surpass other forms of AI application and account for 44% of all AI-powered value. Predictive alerting is a prominent example of augmented intelligence. Smart alerts based on predictive analytics can be used by logistics professionals to carry out a variety of crucial tasks, such as estimating truck arrival times, anticipating equipment maintenance and product damage, and organizing for a spike in demand.
Hyper-local Supply Chains
Enterprise will downsize and bring their supply chains onshore over the course of the next few years, ushering in the next generation of hyper-local logistics. The two forces driving this transition are the need to stay agile and resilient to counter disruption and meet challenging customer demands for same-day delivery. The global same-day delivery market is estimated to reach a value of USD 20.36 billion by the year 2027. This will cultivate a hyper-local service to meet the growing demand.
Sustainability
An emphasis on sustainability will be the highlight of the shipping and logistics industry. Enhanced connectivity will enable enterprises to optimize their human as well as robotic resources.
Future Forward
The shipping and logistics industry has been forced to accelerate their digital adoption and increase their resilience due to the uncertainty in the past couple of years. Establishing real-time visibility, leveraging human-machine collaboration, adopting hyper-local logistics, and improving sustainability are all ways that businesses are preparing for the future.
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Warehousing and Distribution
Article | June 27, 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.
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Article | April 20, 2021
You might be wondering what the benefits are of benchmarking. Well, imagine you are training for a 100 metre sprint in your district. What would be the key number, or metric that you would need to know?
It would, of course, be what the winning time was when this race was last run in your district. Without that information, you don’t know what you’re trying to target. It would be impossible to know if you’ll have any chance at all of winning the race.
It’s exactly the same in business. If, for example, you are concerned about the pick rates in your warehouse, or your transport costs, or your inventory accuracy, benchmarking can help you because it can show you exactly where your performance is compared to others in your industry.
A few years ago, I was working with an automotive parts business. They had a little issue with their picking productivity in the warehouse. They wondered how good it was, whether they could improve it. They actually thought it was okay.
We looked at the figures and compared them with other businesses. This helped us realise that their picking productivity should be three times better than it was. And believe it or not, over a few months they did begin to improve their productivity.
Why? Because benchmarking opened their eyes to the fact that they were at a level quite far below others in the industry.
That’s the beauty of benchmarking. Until you know what others are doing, you can’t be sure how good your performance is.
If you’ve never tried benchmarking, there are three ways you could do it.
1. Informal Benchmarking
This exercise would involve you measuring particular functions or aspects of your business and comparing that against other parts of your business. Let’s say you have a warehouse operating in one city and another operating in another city. You might start to measure the same metrics and see which one is performing better.
You might know other people in the industry who are also operating warehouses so you might agree to share some data with them.
This is probably the easiest way to start off, but it has some downsides:
You’re only measuring against a very small sample size. If all of you in the pool are not that good, how would you know what good is?
You have to make sure that the businesses are similar and you are measuring things in exactly the same way. It’s very important in benchmarking to have a standard way of applying the metric.
2. Formal Benchmarking
This can work for much larger businesses. Perhaps you have operations in many different countries. You could agree a formal structure for how you are going to measure performance. You could do monthly or quarterly benchmarks with all the parts of your international organisation. You could learn from each other and share best practice.
This method is okay but you’re not getting access to a very large pool of results to measure yourself against. You will find that companies are very reluctant to give out benchmarking data. You might also be operating in an environment where the performance is quite low right across the business.
3. Hire a Professional Benchmarking Firm
This is the ultimate way to do it, although there are not a lot of professional benchmarking firms such as ours around. If you do manage to find one, you will quickly realise that there are significant benefits to be had by bringing in the professionals:
The metrics are put together in exactly the same way: When we do a benchmarking exercise for our consulting clients, we go through a very robust data-gathering process and then make sure all the costs, for example, are in the same buckets as everyone else’s in the database.
You gain access to a big pool of results: Professionals have measured hundreds, if not thousands, of companies. This enables you to say, ‘Our company is this size, it operates in this industry, these are the characteristics of our supply chain, who else in that pool of results is like us? We want to be measured against them.”
It’s no good measuring the performance of a grocery retailer, for example, against an industrial product supplier. They have different supply chains. You need to be measuring like with like.
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