Supply Chain
Article | May 22, 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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Supply Chain
Article | August 17, 2023
Introduction
From warehouse robots to delivery drones, the supply chain is experiencing a tremendous upheaval. AI promises a totally autonomous and self-organized future supply chain. A fleet of vehicles utilizing a swarm algorithm can enhance cargo yard throughput; a trusted peer-to-peer ledger on blockchain architecture could change compliance in the sector; and wearables, mobile robotics, and machine learning technologies could speed up order fulfilment. IOT e-brokerage solutions can link shops to couriers and transporters with a click. Tomorrow's supply chain will be leaner, quicker, and self-organized. A few innovative technologies will fuel this unparalleled rate of change over the next 15 years. Here are the major technologies that are shaping the future of the logistics industry.
Logistics Technologies for the Future
Shipment Tracking Systems
Previously, customers ordered shipments, had an anticipated arrival date, and then were kept in the dark until they chose to call. Customers can now access shipping and tracking systems around-the-clock due to developments in the internet and software. This not only enhances the customer experience (cx), but it also saves the business time and money.
Internet of Things (IoT)
The IoT reduces costs and delays by minimizing hazards in the supply chain. Cabs, cargo ships, trains, etc., have sensors that link to an alarm system or dispatcher. These sensors analyze and communicate information to the crew, who learns about hidden threats. IoT isn't a new technology, but it continues to influence logistics by improving in-transit visibility and delivery.
Radio Frequency Identification (RFID)
RFID technology has been used for a few years to monitor inventories labor-efficiently. A product tag or sensor produces radio waves. The company processes the data. RFID tags are similar to barcodes, but their faster information transport and data processing appeal to companies and the direction of technology. Many organizations use RFID tags to track containers in warehouses.
Enhanced GPS Accuracy
Almost everyone utilizes GPS on their vehicles or smartphones. These devices' accuracy has improved over time, assisting lost drivers and enhancing the supply chain. By monitoring truck locations and boosting hauls with current traffic data, GPS increases efficiency and customer satisfaction.
Closing Lines
Unprecedented times have produced unprecedented transformations that will last for generations. Changing demographics, technology improvements, and COVID-19 impacts are altering global supply networks. We must understand the driving factors and act on what we learn to adapt and rise to the situation. For the sake of our current workforce and future generations.
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Warehousing and Distribution
Article | July 11, 2023
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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Software and Technology
Article | April 10, 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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