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Impact of Incorrect Demand Estimation- Quantitative Insights

Incorrect demand estimation is the proverbial blow that comes just when a business is least expecting it. No, the attempt is not to be dramatic here, but to illustrate how proper demand estimation is crucial for the long-term growth and sustainability of any business. Supply and demand volatility and external factors already make demand forecasting highly challenging.

Fluctuations in demand from the last year are not enough while consumer behavioral shifts keep happening, making it harder to assume anything. So what can be a feasible solution? Companies can rely on quantitative insights and other forecasting tools to improve their demand estimation rates. Let us look closely at these aspects in this article.

The Cost of Incorrect Demand Estimation

The cost of incorrect demand estimation will naturally be immensely high for any business. While putting a number to it is difficult, picture a scenario where there are either stockouts or overstocking.

This may lead to excess costs of carrying, rush orders, higher shipping costs, and operational expenditure. Automatically, they all combine to lower profit margins for businesses, thereby directly hitting at financial performance figures. A few examples of incorrect demand forecasts will help you understand the cost at stake.

1. A former Microsoft CEO once stated in 2007 that the iPhone would be unable to gain major market share, being the most expensive phone in the world and unappealing to business customers because it doesn’t have a keyboard. However, ever since, Apple has been one of the world’s biggest smartphone players, shipping 100 million+ iPhones annually from 2012 onwards. 
2. An automobile executive from Japan famously remarked in the 1980s that he was pessimistic about the future of the Japanese automobile industry, reiterating that he would not join my company today or any auto company. Four decades later, Japanese cars are ruling the roost globally, with leading brand names such as Honda, Toyota, Nissan, and Subaru doing well for themselves. 
3. Most interestingly, a professor at Yale University expressed doubts over market demand for overnight deliveries. The statement was that the idea must be feasible to earn better than a c. The reaction came to a paper by a student that proposed a reliable service in this category. This student eventually built a delivery business worth more than $60 billion, establishing FedEx, the leading delivery and freight company. 

 Would you call these costly forecasting mistakes? You bet! And when it comes to running a company, the costs include the following:

• Higher risks of stockouts, making it tougher to freeze suitable stock levels. 
• Obsolete and excessive stocks, which becomes risky for companies. 
• Poor reputation and customer dissatisfaction resulting from not meeting demand and poor perceptions in general. 
• Inability to manage supplier lead times and poor supplier relationships. 
• Miscalculation of peak sales periods and understaffing across customer-facing and warehouse positions. 
• Revenue losses due to all of these issues.

As can be seen, incorrect demand estimation can be disastrous for companies in diverse sectors. Let us now look at how it impacts customer satisfaction levels as well.

The Impact of Incorrect Demand Estimation on Customer Satisfaction

Incorrect demand estimation can have a negative impact on customer satisfaction levels. Here are some pointers that are worth noting in this context.

• Inability to meet demand consistently leads to loyal customers shifting to competitors. 
• Due to their dissatisfaction with the company/brand, they are unlikely to return. 
• Sharing experiences on various channels may lead to further brand damage and loss of reputation. 
• Incorrect demand estimation and forecasts will poorly impact customer loyalty and satisfaction levels. This results from frequent order cancellations, stockouts, or delivery delays. 
• Failure to gauge customer buying preferences, habits, and peak periods may also lead to dissatisfaction regarding the brand being unable to meet customer needs. 

In today’s era of hyper-personalisation in customer service, such scenarios can adversely impact any brand. Here is how the accuracy of demand forecasts can be improved.

How to Improve the Accuracy of Demand Estimation

Fixing incorrect demand estimation and scaling up the accuracy levels of forecasts is possible with the help of several techniques. These include the following: 

• Quantitative insights and methods are good ways to start. These forecasting methods deploy statistical techniques and mathematical models for predictions. 
• Predictions are based on numerical data including prices, income, sales, seasonality, and population. 
• The methods are frequently used whenever there is a regular trend or pattern in terms of customer demand. 
• Time series analysis, regression analysis, moving averages, and exponential smoothing are some examples of these methods. 
• Quantitative methods are helpful for zeroing in on customer relationships, trends, and buying patterns. 
• They enable more objective and precise estimates, provided challenges regarding data quality are addressed. 
• Qualitative methods rely more on intuition, experience, and judgment to make predictions based on expectations, opinions, preferences, and feelings. 
• These methods are only used whenever historical data is sparsely available or demand is affected by unpredictable scenarios like social media trends, political developments, new product launches, and so on. 

• A few examples include focus groups, interviews, surveys, and the like. 
• While qualitative methods are essential for capturing feedback and knowing the pulse of the customer, they may not always help in terms of accurate demand forecasting and inventory management. 
• Regular tracking and evaluation of forecasting performance is also necessary for tackling inaccuracies, ultimately leading to better decision-making. 
• Market intelligence should be integrated into the process along with using suitable historical data and qualitative information alike. 
• Seasonality and long-term trends should be taken into account by forecasting tools or techniques. 
• Better collaborations between the sales and purchasing departments will also enable improved tracking of sales patterns and trends. 
• Periods of stockouts may be excluded from forecasts while lost sales may be predicted. These can be added to final predictions for higher accuracy. Otherwise, stockouts may lead to inaccuracies. 

Companies can consider suitable demand forecasting tools driven by artificial intelligence and machine learning. It will help them monitor progress with complete visibility into the process. Advanced data capture and analysis will unearth invaluable insights that lead to better forecasts and decision-making alike. These tools also enable forecasts that are more accurate, since they consider promotions, trends, seasonality, and variance in demand. 

Analysis and insights into multiple demand scenarios based on major variables will help companies respond suitably to evolving trends. AI and big data analytics will swiftly process unstructured and structured data in massive chunks from various sources. This will include social media data, market trends, sales records, customer data, and more. AI-based algorithms will help companies identify complex trends and patterns, while predictive models can be generated after learning from historical data. 

Real-time analysis of incoming data will help enhance awareness of new patterns and enable updated forecasts likewise. Businesses can segment demand and consumer groups for better marketing strategies and inventory management alike. Going by the recent trends, AI, ML, and analytics will play a big role in helping companies combat inaccurate demand estimation while positively impacting bottomlines eventually. 

FAQs

1.The Impact of Incorrect Demand Estimation on Marketing Strategy 

Incorrect demand estimation impacts marketing strategy in several ways. Overestimation may lead to more investments in inventory and production capacity in some categories. This may lead to marketing teams missing out on high-demand or trending products. Poor forecasting hinders the abilities of companies to accurately predict when any particular product will be most popular. This will lead to a loss of marketing opportunities. 

2.How Incorrect Demand Estimation Can Affect Brand Reputation

Brand reputation is adversely affected by incorrect demand estimation. Customers will feel dissatisfied about higher order cancellations, delivery delays, and about the brand not meeting their needs at the right time. They may spread this negative perception through word-of-mouth. At the same time, failure to anticipate customer buying habits and trends will lead to inventory undersupply or oversupply, leading to customers shifting to market competitors. 

3.The Impact of Incorrect Demand Estimation on Corporate Social Responsibility

Incorrect demand estimation can also have an impact on the corporate social responsibility (CSR) initiatives of the company. With incorrect forecasts leading to higher resource allocation on unnecessary inventory and subsequent losses throughout the entire operational and supply chain networks, companies will naturally have to slash corporate social responsibility budgets. At the same time, poor brand image and reputation will also impact these initiatives greatly.