Why Demand Forecasting Is Important in Supply Chain

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    The COVID-19 pandemic was unanticipated and severely disrupted logistics worldwide. According to statistics, the global supply chain pressure indexar increased from 0.01 in December 2019 to 3.15 in April 2020. While few people could have predicted the chaos the pandemic would cause, the impact led several to start incorporating demand forecasting in the supply chain.

    Business analytics market size had already grown to $67.92 billion by 2020, so extending data analysis to the supply chain was the obvious option. To say the difference has been phenomenal would be an understatement. Demand forecasting has transformed the supply chain industry, optimizing inventory management, vendor relations, etc.

    This blog will explain demand forecasting before addressing its significance for the supply chain. We have added a few tips to explain how to use demand forecasting in the supply chain.

    What Is Demand Forecasting?

    Demand forecasting refers to studying historical and current data to understand the internal and external factors affecting demand. The trend equation is then used to predict or ‘forecast’ what the market would be like in the short-term or long-term.

    There are several ways to determine the demand forecast for your organization, depending on the factors you prioritize. For example, some organizations base their demand forecast on historical data alone, while others consider the present circumstances to get the results.

    Your analysts will determine which factors to rely on depending on your industry. 

    Significance of Demand Forecasting in Supply Chain

    The opportunities and benefits of demand forecasting in the supply chain are endless. The reason is simple – operation management tasks are so heavily interlinked that improving one creates a cyclic improvement. This factor has further highlighted the role this version of data analytics has played in supply chain optimization.

    pile of boxes interconnected in a warehouse

    Below are reasons why demand forecasting is significant for the supply chain:

    1. Need Assessment and Prediction

    Demand forecasting is an excellent way to enhance the accuracy of your need assessment data. This accuracy will help you highlight the optimal order size for a specific timeline. Following the forecast will help you avoid ordering more or less of the product you need for your business. 

    Ordering more than the requirement increases your inventory costs while ordering less would leave your customers without the product. The situation would cause disgruntlement and negatively affect your brand image.

    2. Optimized Allocation of Resources

    The problem with inaccurate ordering goes beyond the excess items you need to store. The more vehicles are involved in the transportation, the more costs are spent on ELD devices, and the higher the overall cost of the order will be. Additionally, you will need to organize more than necessary space for the product, spend more time managing it, and potentially incur higher labor costs.

    Using demand forecasting will help you optimize resource allocation. Firstly, you won’t incur additional costs, saving you unnecessary spending. Secondly, you can divert the money to another function or project that needs funding to achieve better results. 

    3. Improved Sale and Purchase Terms With Vendors

    Data-centric forecasting is optimal because it is objective and allows you to give your vendors correct order quantities for each quarter instead of estimates. It simplifies their work because they can create a production and costing plan for each timeline. 

    The plans will give them accurate material acquisition, labor, and transportation costs. This information is critical for budget planning, and they will appreciate the initiative. Hence, your overall arrangement will become smoother, and your vendor relations will improve.

    4. Inventory Optimization

    Inventory optimization is one of the top benefits of incorporating demand forecasting in the supply chain. As a business owner or manager, you already know the intricacies of arranging storage for your inventory. Therefore, effective inventory management plays a crucial role in ensuring the right balance, minimizing storage risks, and avoiding issues such as sudden damage to the warehouse.

    The warehouse needs to follow health and safety guidelines and requires security to protect the inventory. There is also a risk of sudden damage to the warehouse, like fire or flooding, which can put the stock at risk. 

    The longer your products stay in storage, the higher the risk of damage. Hence, it is much better to order the correct amount.

    5. Enhanced KPI Tracking

    Demand forecasting is objective and presents a data-based prediction of your sales number for certain months, quarters, or years. This prediction is derived from intelligent data analysis, and you can use it to set performance targets and KPIs for related functions.

    It is a good way to assess team performance and determine if members require more training, motivation, etc., to improve performance. There are always other ways of evaluating employee performance, but this method is optimal for functions like sales, marketing, and operations.

    6. Cost-Reduction

    As mentioned above, leveraging demand forecasting will help you avoid overspending on unnecessary inventory and associated costs. This result will reduce your costs, increase revenue, and allow the redirection of funds to other revenue-generating functions.

    Depending on how you invest the money, you can also post your overall earnings significantly.

    7. Less Uncertainty

    Another benefit of demand forecasting is that it takes as many factors into account as possible. Therefore, the chances of the forecast being inaccurate are slim. Adding potential risks to the calculation helps minimize uncertainty and ensures that your order size can compensate for some discrepancies.

    However, this accuracy depends entirely on the data you use for determining the demand forecast. The more comprehensive the details, the better the results.

    Tips for Optimizing Supply Chain Through Demand Forecasting

    You now know why demand forecasting is beneficial for the supply chain. However, you must understand how to leverage it to maximize its value. Following are some tips to help you use forecasting to optimize your supply chain:

    1. Use a Combination of Qualitative and Quantitative Forecasting

    Quantitative forecasting uses historical data to determine demand forecasts for the next year or more. This method is reliable because historical trends are often good indicators of market movements. However, it isn’t without risk. 

    In contrast, qualitative data uses expert opinions for internal and external factors and determines the forecast accordingly. While both methods have their merits, the best solution is to choose a combination to get the best results. 

    2. Opt for Active Demand Forecasting

    Active demand forecasting relies more on existing circumstances, data, and market trends rather than relying entirely on historical data. We recommend this method because the business environment has changed drastically over the past few decades due to rapid technological developments and climate change.

    Historical data is a good reference, but its prediction abilities will likely be limited.

    3. Leverage Probabilistic Forecasting

    Probabilistic forecasting refers to assigning a probability to uncertain factors and volatility in demand. This probability allows businesses to predict future risks and plan accordingly accurately. 

    This form of forecasting became popular after the pandemic hit because this model would have helped offer more accurate forecasting results.

    4. Incorporate Machine Learning for Accurate Forecasting

    Artificial intelligence and machine learning are game-changers of this century and will be instrumental for business transformation. Machine learning uses historical and present data to offer predictions and trend analyses. But, it doesn’t stop there. 

    Machine learning continues to absorb data and adjusts its predictions accordingly. Its learning capability is modeled after human understanding, allowing it to draw inferences.

    Final Thoughts

    The past few years have changed how we respond to changes and crises. Our technological innovation has allowed us to explore the value of demand forecasting in the supply chain and leverage it to transform our operations. 

    The results and the data-centric approach have helped businesses optimize their operations and continue to increase their productivity. 

    We hope you enjoyed reading this blog and encourage you to find more at ClicData. You can also contact our teams to learn more about our solutions and discuss potential partnerships.