Any company that wishes to keep a competitive advantage in its field needs a well-functioning supply chain. But did you realize that better demand forecasting can also be aided by supply chains? We will examine how supply chains may be utilized to enhance demand forecasting in this blog post and offer some advice on how to get started.
Understanding the available information is the first step in using supply chains to enhance demand forecasting. Sales data, customer data, supplier data, and transportation data are a few of the data sources employed. You may start creating a strategy for using the various data forms to improve your forecasting by knowing them.
You need to consider how to use this information to enhance your demand forecasting once you clearly understand the many readily available data forms. Examining past trends is one method to do this. You can use this to spot trends in consumer behavior and purchasing patterns. After that, you can utilize this data to forecast future demand.
Examining current patterns is another method of using supply chain data to enhance demand forecasts. This might assist you in determining markets with rising or falling demand. The estimates can then be modified accordingly.
Finally, you must ensure that your supply chain data is constantly monitored. This will enable you to spot adjustments in consumer behavior or buying habits. By monitoring the data, you can ensure that your prediction is as accurate as possible.
By starting with these pointers, you can use supply chain data to enhance your demand forecasting. You can ensure that your company has a competitive edge by being aware of the different forms of data that are accessible and how to use them most effectively. So why are you still waiting? Today, begin utilizing supply chain data to enhance your demand forecasts!
Demand forecasting is one of the most challenging aspects of supply chain management, especially when all the variables are considered. An organization’s supply chain may be impacted by natural disasters, product shortages or delays (such as those brought on by a cargo ship getting stuck in the Suez Canal), and, of course, a global pandemic. Regardless, technology and techniques can aid organizations in improving their capacity to foresee demand.
To choose the most proper supply chain management (SCM) software for your company, use our supply chain management product selection tool. In as little as five minutes, you’ll receive a brief, personalized list of product recommendations.
Use AI to improve predictions.
Leading manufacturers will increasingly rely on AI to manage their supply chains during the next five years. AI can sort through masses of data using predictive analytics to make wise predictions about future sales trends. This enables companies to manage their inventory more proactively and effectively.
Businesses can reduce the manual work necessary to simultaneously examine many market variables by using AI for forecasting. Furthermore, AI provides more precise short-term forecasts, allowing firms to be more adaptable in their planning. However, compared to passive demand forecasting, using AI for predicting does require more data up front (which only uses historical data to make predictions). In addition, this technology’s maintenance requirements can be more challenging than traditional prediction methods.
Analyze order history to identify trends
Prior purchases might help you determine which products are the most sought-after and who they appeal to. But you don’t have to do it by hand. You should be able to import sales data into your BI tools using a connection between your supply chain management software and your BI platform, where the AI will then automatically highlight pertinent insights.
To better understand the purchases your customers will make, you might ask them about their sales forecasts if you run a manufacturing business. However, you might need to give out lesser incentives or awards to get this information.
Keep an eye on competitors.
You might not be able to respond quickly enough to a competitor’s promotion or special offer on a product you sell to retain the consumer. The sales projection can be modified to account for the lost sales. Additionally, maintain tabs on closures resulting from local weather emergencies or data breaches. When this happens, customers frequently go to other businesses; thus, being ready will raise demand for your goods and services.
Consider inventory elasticity
Your inventory is deemed elastic if a bit of change in pricing causes a significant shift in the demand for your goods. A demand-focused supply chain management tool can alert organizations of this mismatch between actual and predicted sales velocity; for instance, if you forecast strong sales of a product, but then the market suddenly changes, and you don’t sell as many units as you previously planned. Therefore, rather than incurring losses by getting rid of all the unsold goods, it recommended that the company cut its prices so that it could sell any excess stock while still generating a profit.
As we observed early in the pandemic, companies maintained high sales levels without wasting any raw materials when some manufacturers redirected their production lines to create new products that met the sudden increase in demand for goods like hand sanitizer and alcohol-based cleaning supplies. Producers should stock up on a range of raw materials so that nothing is wasted to be ready for upcoming events.
Best supply chain management software for demand forecasting
Companies looking to improve their capacity for demand planning should consider one of the following supply chain management tools. Each choice on this list was chosen for its superior forecasting capabilities.
-Logility
With the help of model switching, organizations can use the leading supply chain management software Logility to automatically estimate demand at every level of the organization, ensuring that each forecast is appropriate for the production stage. Unparalleled insights on the trends of new items and those with shorter life cycles are provided by it. Because businesses are less likely to overstock inventory due to this increased visibility, operating costs are also reduced. Logility helps companies respond rapidly to market changes and become more agile by looking at elements like transportation and vendor-managed inventory (VMI).
-SAP Integrated Business Planning
The Integrated Business Planning software from SAP is made to assist companies in enhancing continuity and responsiveness. Businesses can use simulations to evaluate various demand scenarios and determine how they would respond to market shifts. Performance monitoring compares actual outcomes to expectations so enterprises can promptly spot gaps and make necessary adjustments. Additionally, SAP provides embedded analytics, enabling customers to get forecasting information wherever they are working with data.
-Atlas
Atlas is an artificial intelligence-based supply chain planning program. Atlas combines supply and demand information from several sources to provide more context for the process and to support group planning and what-if analysis. Additionally, automated workflows reduce time spent on errors and human labor while accelerating business choices through machine learning. Machine learning enhances forecasting methods by gathering more insights than conventional forecasting methods.
Better demand forecasting lowers operating costs.
By implementing a better demand forecasting system, your company can cut operating costs by avoiding overstocked commodities and unnecessary inventory space. Additionally, you can purchase more significant quantities of things all at once to benefit from volume savings. Additionally, AI-based machine learning systems require less labor-intensive manual work from staff while producing precise forecasts for future sales demands.
We have the ideal option for your company if you’re seeking supply chain management software to improve your capacity for demand forecasting. Simply respond to a few questions about your company’s requirements to receive specialized software recommendations using our SCM Product Selection Tool.
What is the demand for forecasting?
Demand forecasting, as the name suggests, is the act of identifying future demand peaks and troughs. To do this, businesses must analyze all internal and external elements that impact their supply infrastructure, providing them with a comprehensive picture of any patterns and problems that could affect their demand projections.
Demand forecasting is crucial to effective supply chain management to ensure prompt stock replenishment, improved capacity management, and optimum sales and revenue. Additionally, it enhances management and decision-making while advancing future growth and expansion plans.
An in-depth examination of the variables that may impact a company’s supply infrastructure is necessary for accurate demand forecasting. To ensure business readiness, continuity, and a great end-user experience, anticipating demand necessitates rigorous research of several variables, from historical sales patterns to specific occasions in the retail calendar (such as Christmas).
Businesses that regularly forecast demand typically produce several forecasts to anticipate stock supply and demand over a range of timeframes. It is possible to predict future stock requirements from days to months in advance using different levels of granularity as part of the analysis, allowing for improved planning, control, and business confidence.
How Can Demand Forecasting Help Your Business?
There are many benefits to forecasting for long-term profitability and consistency in retail businesses that depend on precise stock forecasts. Additionally, the advantages go beyond preserving excellent customer service; they may also be used to support a company’s plans for growth, improve corporate confidence, and assist it in achieving its objectives.
Below, we take a closer look at some benefits you could enjoy by embracing demand forecasting.
-Reduced uncertainty
Supply chain management is constantly negatively impacted by uncertainty. Decision-making is hampered, projects are delayed, and stakeholder confidence is diminished. Demand forecasting may lessen the uncertainty, ensuring that activities related to the supply are sufficiently funded and that managers are better informed to make wise decisions that support growth and advancement.
-Enhanced supply infrastructure
The health of your supply system depends on your ability to predict demand. Demand forecasting enables you to improve supply chain management by optimizing capacity, inventory replenishment, and warehouse personnel management.
-Increased revenue
For business owners to predict revenue, they must understand when and how things will sell. Demand forecasting guarantees that your products are accessible when needed, ensuring you never lose a transaction.
-Reduced need for safety and surplus stock
Your turnaround time for the stock will increase if you don’t have enough inventory in your warehouse, which can hurt your bottom line. Demand forecasting decreases the amount of safety stock needed, saving you money on inventory while allowing you to concentrate on the most lucrative products.
-Improved fulfillment = better long-term customer advocacy
Demand forecasting makes it possible for your products to be offered more regularly, which can be pretty advantageous from the perspective of the customer advocate. You can increase sales and your clientele by using accurate demand forecasting to encourage repeat business from customers and word-of-mouth recommendations.
What is Business Forecasting?
Let’s define business forecasting first. Business forecasting is estimating and foreseeing future sales, revenue, resource demand, and inventory changes using time series data.
In particular, business forecasting is generally divided into two main categories:
-Demand Forecasting:
Forecasting resource demands for things like inventory and manpower is essential to ensure that demand is met. To create a demand forecast, a retail business can ask: “How should I plan the list needed at each location in the upcoming quarter, month, or week? More accurate inventory planning at each site reduces costs while boosting customer satisfaction.
-Growth Forecasting:
A precise growth model must be created for effective corporate planning. You can budget more efficiently, use your resources better, and take the required actions to accomplish your goals by predicting future development. For instance, “What will our quarterly sales be at the end of this period?” is a crucial issue for revenue forecasting. This estimate involves analyzing historical data using a variety of metrics, including geographic inventory levels, consumer purchasing patterns, and more.
Business Forecasting with Machine Learning
One of the numerous benefits of using machine learning for business forecasting is that you can utilize thousands of metrics to increase the accuracy of your predictions compared to those made using manual approaches. With machine learning, you may also obtain a fully autonomous turn-key system that updates projections as patterns constantly change to have the most recent data.
The fundamental difference between a machine learning-based solution and an old-fashioned method of business prediction is that AI may be given as many business variables and KPIs as you do. Machine learning allows data to exhibit patterns and relationships that would otherwise go undetected.
In addition to being precise, AI-based forecasting is also autonomous and capable of updating projects continuously as trends change. You’ll be able to make wiser decisions as a result of this.
How does It work?
In an AI-based forecasting solution, forecasts are optimized using a collection of machine learning algorithms. Based on this feedback, the algorithm selects the ideal model for the particular business metric you are projecting.
Here is a step-by-step overview of the process that an AI-based forecasting system uses:
-Connect the data:
We first must gather information from all relevant internal and external sources to create an accurate forecast.
-Select forecast metrics:
The user selects the metrics to forecast and the timing of the forecast.
-Perform automatic data preparation:
An automated solution can help you eliminate any previous data irrelevant to your forecast to concentrate on what is crucial. Data preparation might feel very overwhelming.
-Train the machine learning model:
After preprocessing the data, we train the model by submitting it to several forecasting algorithms. To determine which algorithm performs best, we compare the accuracy of each result.
-Create a customized model:
To build a new, customized model for this particular task, we only select the models that have already demonstrated their accuracy.
-Review the custom model:
We build a personalized model, regularly assess its accuracy, and retrain it as necessary.
-Make a forecast:
A real-time forecast is then created using the custom model and the provided data and saved for further use.
-Consume the forecast insights:
The forecasts are presented in various formats, such as dashboards, reports, or alerts, so anyone can understand and utilize them regardless of technical proficiency.
Use Cases of AI-Based Business Forecasting
Demand and growth forecasting are the two main subcategories of business forecasting. Here are some ways that various businesses are recognizing the enormous value of AI business forecasts:
-Demand Forecasting in Fintech
To design their business strategy, fintech companies, multiplying and handling transactions for eCommerce, must be able to predict demand for each currency. This is particularly challenging because many of these businesses are global in scope and have to contend with fluctuating exchange rates.
By exploiting the three main advantages of AI—accuracy, scale, and adaptability—many businesses are applying it to enhance their commercial plans. In particular, the corporation has more time to make wiser decisions than its rivals since these precise demand projections can adapt to changing market conditions and consumer behavior.
Better demand forecasting enables companies to foresee client requests in the future and keep enough inventory on hand to meet those demands without having too much cash committed at any given time.
-Growth Forecasting in Telco
Forecasting growth is necessary for businesses to set short- and long-term goals. A typical example is predicting the number of clients who will churn from service each quarter. One organization in the telecoms sector opted for AI over conventional forecasting techniques. This allowed them to consider more indicators of when customers are likely to cancel their service.
The telco company found that it could use competition data, such as events and marketing activities, to increase the precision of its churn projections. Being an early adopter of AI allowed the company to use these results to create its marketing and business plans to retain current clients.
How does supply chain analytics work?
The definition of analytics is the use of data-driven judgments, which are often made after reviewing a summary of correct and relevant data. Tools for visualization, such as graphs, and charts, can help with this. Most of the time, supply chains generate an excessive amount of data. The power to make sense of it all, however, comes with supply chain analytics; patterns will begin to emerge along with trustworthy insights.
What are the types of supply chain analytics?
The many supply chain analytics include the following:
-Descriptive analytics
provides a single source of truth for all internal and external systems and data throughout the supply chain.
-Predictive analytics
Organizations can grasp potential future scenarios and their business repercussions with predictive analytics. Predictive analytics, for instance, can be used to forecast and reduce risks and disruptions.
-Prescriptive analytics
gives businesses the tools they need to collaborate effectively for maximum commercial benefit, including the ability to recognize and address problems. Reduces the time and effort spent responding to disruptions by assisting businesses in connecting with logistic partners.
-Cognitive analytics
Assisting in addressing more complex topics in natural language, as an individual or group of individuals would. Supporting them in thinking through a challenging challenge like “How can we improve or optimize X?” it aids firms in problem-solving.
-Applying cognitive technologies
Cognitive technologies, such as artificial intelligence (AI), can only apply with a solid basis in analytics to the supply chain process. Similar to how people communicate, but much more quickly and efficiently.
This more advanced version of supply chain analytics is ushered in a new era of supply chain optimization. It can instantly scan through massive amounts of data to help a business improve forecasts, find inefficiencies, better meet customer expectations, foster innovation, and pursue ground-breaking ideas.
Why is supply chain analytics important?
An organization may benefit from using supply chain analytics to help make better, quicker, and more effective decisions. Benefits include having the ability to:
-Reduce costs and improve margins
With the help of our simple data, you can develop an ongoing and integrated planning strategy that will give you visibility into every facet of your company that you require for more effective operations and crystal-clear insights.
-Better understanding of risks
Businesses may identify and mitigate known risks by thoroughly examining the supply chain. They can also anticipate potential threats by looking for patterns and trends.
-Increase accuracy in planning
Through the analysis of client data, supply chain analytics can help a company better predict future demand. It helps to predict consumer desires after the initial purchase or which products should be reduced when their margins narrow.
-Achieve the lean supply chain
By keeping an eye on warehouse operations, partner replies, and consumer expectations, supply chain analytics can help businesses better understand their supply networks.
-Prepare for the future
Several providers are now offering advanced analytics. Both organized and unstructured data may be handled by advanced analytics, giving businesses an advantage by ensuring that warnings arrive on time to make the best decisions. Advanced analytics may also create connections and patterns between different sources to deliver alerts that reduce risks quickly, cheaply, and with minimal long-term damage.
As supply chain analytics uses of technology like AI become more commonplace, organizations may anticipate increased advantages. Now it is possible to perform real-time analysis on previously unprocessed data due to natural language data analysis limitations. AI can read, interpret, and link data from numerous sources, silos, and systems quickly and accurately. Based on the information it has evaluated, it may then produce real-time insights. Companies will own a lot more supply chain intelligence than they currently do.
Conclusion
Any company that wishes to keep a competitive advantage in its field needs a well-functioning supply chain. But did you realize that supply chains can also aid better demand forecasting? We will examine how supply chains may be utilized to enhance demand forecasting in this blog post and offer some advice on how to get started. Increasing profitability and streamlining processes might strengthen your company’s demand forecasting capabilities. Please get in touch with us if you’re interested in learning more about how supply chains can be used to enhance demand forecasting. We would be delighted to impart our knowledge and assist you in beginning the journey toward increased profitability.
