How can you use data to forecast demand?
Forecasting demand is a crucial skill for inventory management, as it helps you plan your purchasing, stocking, and replenishing decisions. But how can you use data to forecast demand more accurately and efficiently? In this article, we will explore some of the methods and tools you can use to analyze your inventory data and generate reliable demand forecasts.
One of the simplest and most common ways to forecast demand is to use historical data from your sales, orders, or shipments. Historical data can show you the patterns, trends, and seasonality of your demand, as well as the impact of external factors such as promotions, holidays, or events. You can use historical data to calculate the average, moving average, or exponential smoothing of your demand over a certain period, and project it into the future. However, historical data may not capture sudden changes or fluctuations in demand, so you need to update and adjust your forecasts regularly.
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Waqas Masood, CSCP
Deputy Procurement Manager at K-Electric | Chancellor's Honor Roll at SZABIST | Certified Supply Chain Professional from ASCM-USA
Historic data only gives you a trend and depicts where planning can be improved. There are other avenues as well that one access to gain information such as market analysis, customer analysis, micro / macro indicators impacting purchasing power or decisions. Historical data can be more helpful in cases where business is in the CASH COW stage (having constant value/volume) where business life cycle is in maturity stage, you have captured the market with your full potential and recurrence is highly predictable. For the rest, relying on historical data can end up in bullwhip.
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Mukesh Nayak
Communications I Marketing I Branding
Historical data at best can serve as the starting point. However, it is important to continuously monitor the trends, other important events and occurrence that could lead to a surge or fall in demand. I would prefer a quick look at the data from the PEST model perspective and consider Political, Economic, Social or Technological factors that could influence the demand.
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Benn Gehman
Son of One, Brother of One and three, and Father of 11
Historical data might lead to movements across time, especially as you are able to see iterations of the same time structure. Trends of differing amounts of time along with the larger movements of the historical data will enable a better view of where your demand is going. Historical might be yearly, trends might be 3 and 1 month moves against the preceding same time structure. Best guess against all inputs will give best possible forecast.
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Mian Athar jameel
Head of Academic & Operations | Hospitality & Tourism Expert
Historical data is the best way to predict demand, however to forecast demand one has to look at the current r trends in the market and keep in the prevailing situation.
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Marco Koehler
📦 eCommerce Inventory solved with Google Sheets
Historical data is what it is - history. It helps us based on data of statistical significance to make better decisions, but it can't consider internal facts. If a companies growth is simply off (e.g. from 6 Mio € Revenue to 80 Mio € Revenue in one year), then any historical data in the world would not be able to predict this - not even AI. Using internal data like financial plans helps us to combine finances and inventory (as it should be) to make better predictions. We also have to consider marketing events, a change in seasonality and other facts that may lead us to wrong assumptions. Therefore, historical data has to be used, but it also has to be understood. We need more than history to look into the future.
Another way to forecast demand is to use customer feedback, such as surveys, reviews, ratings, or requests. Customer feedback can give you insights into your customer preferences, needs, expectations, and satisfaction levels. You can use customer feedback to identify the gaps, opportunities, and risks in your inventory, and to adjust your demand forecasts accordingly. For example, you can use customer feedback to estimate the demand for new products, discontinued products, or variations of existing products. However, customer feedback may not be representative or reliable, so you need to validate and verify your forecasts with other data sources.
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Lucas Adotey
Supply Chain//Warehouse//Procurement/Logistics//Admin//Inventory Control//
Organizations can not ignore customer feedback. Organizations exist for the customers. If you Don't have customers then you are not in operation. Your user department or end users may hold the key to your edge over the competition through their feedback.
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Hamza Junaidy
Stop GENOCIDE in GAZA | B2B | B2C | Retail Operations | Brand Development
How can you demand without having data? About twenty years earlier, we used to forecast by checking our data in the registers and order form books maintained by order bookers. We have come a long way. Now we use different softwares, apps and other mediums collect data. The more detailed data we have, the better we can forecast. If our best seller article in a range of 24 colors is a beige color trouser in Oct'22, then we have to check before forecast if the same color, size, pattern and other details are still in trend or not. Which were the shops where it's been sold?
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Jeannine Griggs, CPIM, CSCP
Focus on those customer relationships! The collaboration that can exist is worth gold … my biggest contributions have come from information that has been shared because of a good relationship. Feedback is critical.
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Marco Koehler
📦 eCommerce Inventory solved with Google Sheets
Customer Feedback is an important piece in planning your inventory, especially when talking about new product launches. How to know how they're going to sell? Some surveys, even not statistically significant, can give us an idea what the situation might look like. And in the end it's also a business risk to make decisions without being able to see the future; it's part of the game.
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Roderick Sorrell
Procurement & Supply Chain Leader
Keep sharing your view with your customers - they will know best what is going on in their business and what is coming. Put the time in and you will be amazed at what you learn and can take back to your own business!
A third way to forecast demand is to use market research, such as industry reports, competitor analysis, or customer segmentation. Market research can help you understand the external factors that influence your demand, such as the size, growth, and trends of your market, the strengths, weaknesses, and strategies of your competitors, and the characteristics, behaviors, and preferences of your customers. You can use market research to estimate the potential demand for your products, the market share you can capture, and the price elasticity of your demand. However, market research may not be accurate or timely, so you need to update and refine your forecasts frequently.
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Muhammad Imran
Inventory Analyst/SAP MM Power User/Senior Planning Engineer
It is crucial to understand the phase that the business is going through whether growth or maturity. We need to understand our business as well as the market. A business in growth phase may forecast on higher side being optimistic while a mature business will aim for higher accuracy taking a more pragmatic approach. Forecast accuracy improves with more historical data but needs corrections or adjustments when current internal and external influencing factors are under observation
A fourth way to forecast demand is to use machine learning, such as artificial neural networks, support vector machines, or random forests. Machine learning can help you analyze complex and large datasets, and find hidden patterns, relationships, and anomalies in your demand. You can use machine learning to create predictive models that can forecast your demand based on multiple variables and scenarios, and that can learn and improve from new data and feedback. However, machine learning may not be easy or cheap to implement, so you need to have the right skills, tools, and resources to use it effectively.
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Utkan Ekinci
Co-Founder at Arkhon Optima
Artificial Neural Networks do not always come with outstanding results when used alone. Along with the past sales data, user should also have a big data set of factors affecting it. This is not practical in all cases; but it is possible only with a significant workforce and budget dedicated. The best for an average company is the combination of machine learning and classical mathematical models. ETS, ARIMA or dynamic regression models provide excellent results when there is quite little data given, perhaps only sales quantities. If the company keeps records of promotions and campaigns, or has information even non-comprehensive and about the related data, then it will be good to include the neural networks in the game.
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Roderick Sorrell
Procurement & Supply Chain Leader
Machine learning is a great way to challenge your own business's assumptions. If you have history, your business assumption and a forecasting algorithm as a view - you are well set up to have a demand forecast discussion.
A fifth way to forecast demand is to use data visualization, such as charts, graphs, dashboards, or reports. Data visualization can help you communicate and present your data and forecasts in a clear, concise, and compelling way. You can use data visualization to compare and contrast different methods and results of your demand forecasting, and to identify the strengths, weaknesses, and uncertainties of your forecasts. You can also use data visualization to share and collaborate with your stakeholders, such as your suppliers, customers, or managers, and to get their feedback and input on your forecasts. However, data visualization may not be sufficient or convincing, so you need to support and explain your forecasts with data and logic.
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Utkan Ekinci
Co-Founder at Arkhon Optima
The most critical trap that the data examine team falls into is making predictions with a straight linear line in their mind. That approach has nothing to say except increase/decrease. So, we end up with a long social chat without numbers. Instead, output of the demand forecasting models must be added to the data projected on the screen. It is also convenient to show the variance arising from the forecast in the visuals. So, the audience will not be enchanted by such a powerful software, will not be overconfident in their findings, and will become cautious.
A sixth way to forecast demand is to use data integration, such as APIs, cloud services, or data warehouses. Data integration can help you collect and combine data from different sources and systems, such as your inventory management software, your sales and marketing platforms, your accounting and finance tools, or your external data providers. You can use data integration to create a comprehensive and consistent view of your demand, and to access and update your data and forecasts in real time. You can also use data integration to automate and streamline your demand forecasting process, and to reduce errors and inconsistencies in your data and forecasts. However, data integration may not be simple or secure, so you need to ensure the quality, compatibility, and protection of your data and systems.
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Utkan Ekinci
Co-Founder at Arkhon Optima
The future of the demand forecasting models lies in here. The models will search for those factors in the vastness of internet, and match them with some numerical facts. For example, imagine we ask ourselves what affects our sales? Air temperature, ex-change rates, something else? So, it will be time to answer an unanswered question; why? Such as “Yes, we do have an estimation but why so ? Why do the sales increase?”... In the future, forecasting models will be capable of measuring on what factors the sales. This way, our sales data will be able to meet the factors affecting itself. Obviously, that will ensure our predictions to be more consistent and conscious.
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Asad Farooq
Supply Chain Professional | SAP Certified Application Associate | SAP Materials Management
Absolutely, analysing historical data with statistical methods or algorithms helps to forecast demand. Apart from this we also need to consider qualitative forecasting to have a clear view of demand trends, enabling businesses to make well informed decisions.
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Zehra Iqbal
Buyer | MM planner | BE | MBA Logistics and Supply Chain Management
Data is a cornerstone in this era. It helps to build predictive models that anticipate future trends. It promote transparency and accountability which ultimately helps an individual and organisation to overcome crucial decisions.
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Roderick Sorrell
Procurement & Supply Chain Leader
Forecasting can be a daunting job. Try to focus on the 80/20 principle and automate as much of your data as you can. That way, you can focus on exceptions and the lines which really matter and are best suited for a business discussion.
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