Demand Forecasting: Key to Efficient Inventory Management

What is demand forecasting?

Demand forecasting is the process of figuring out how much inventory you will need in the future by looking at past data, trends, and events to fulfill the demand of the consumer. Businesses ensure that they have enough products to fill customer orders. It helps them mitigate risks associated with the supply chain and plan the execution in a better way.

In today’s fast-paced business environment, demand forecasting has become increasingly important.

Importance of Demand forecasting:

1. Avoid overstocking.
2. Reducing high holding cost
3. Stock obsolescence
4. Preventing loss of sales due to stock out

Efficient inventory management relies in part on good demand forecasts. Insufficient inventory will not only leave customers dissatisfied and cost you revenue, but if it happens enough, or on an important enough occasion for a customer, it can result in lost future business as well. But overstocking is costly in terms of storage and logistics and could leave some inventory unsold for a long time. So, finding the right inventory balance is an indispensable aspect of a good demand forecast.

Types of forecasting which are generally utilized are as below: –

1. Trend forecasting: Use changes in the market for your product over time to guess possible trends. There are times of the year as per past sales statistics that this doesn’t consider especially the seasonality of the sales.
2. Forecasting with graphs: By plotting historical data on a graph, you can find patterns and add trend lines to find insights that you might not have seen otherwise.
3. Qualitative forecasting: Market study and focus groups are common ways to do qualitative forecasting. This kind of info is then used to build models by forecasters.
4. Quantitative forecasting: This uses past numerical data to predict future demand. The more data gathered, the more accurate the forecast usually is.

Technology-Driven Demand Forecasting

Advances in technology and available of abundance data have revolutionized demand forecasting. Businesses are increasingly leveraging machine learning, artificial intelligence, and big data analytics to gain deeper insights into demand patterns and improve forecasting accuracy. These advanced tools can analyze vast amounts of data, including sales data, social media sentiment, and weather patterns, to identify hidden trends and predict future demand with greater precision.

Role of data is utmost important in demand forecasting and biasness or error in data might lead to varied results. To mitigate the risk due to data error it is essential for the organization to improve the accuracy of demand forecasting.

Organization can improve the Demand Forecasting Accuracy by doing the following:

1. Use multiple forecasting methods: Combining multiple forecasting methods, such as time series analysis, regression analysis, and causal analysis, can help improve forecasting accuracy.
2. Incorporate real-time data: Incorporating real-time data, such as sales data, into the forecasting process can help organizations respond quickly to changes in demand.
3. Consider external factors: Organizations should consider external factors, such as economic conditions and competition when making forecasts.
4. Continuously monitor and adjust forecasts: Organizations should monitor and adjust forecasts to ensure accuracy.

Benefits of Integrating demand forecasting with Inventory Management

1. Improved inventory control: By accurately forecasting demand, organizations can ensure they have the proper inventory to meet customer needs.
2. Increased efficiency: Integrating demand forecasting with inventory management allows organizations to streamline processes and minimize waste.
3. Better decision-making: By accessing accurate demand forecasts, organizations can make better-informed decisions about inventory levels, restocking, and resource allocation.
4. Increased profitability: Accurate demand forecasting and inventory management can increase sales and reduce waste, improving profitability.

Conclusion

Demand forecasting is an essential component of efficient inventory management. By accurately predicting future customer demand, businesses can optimize their operations, reduce costs, improve customer satisfaction, and enhance profitability. As technology continues to evolve, demand forecasting is becoming increasingly sophisticated and accurate, empowering businesses to make data-driven decisions and achieve greater success in the competitive marketplace.

References:-

https://www.netsuite.com/portal/resource/articles/inventory-management/inventory-forecasting.shtml#:~:text=Improved%20demand%20forecasting%20improves%20your,other%20steps%20can%20be%20automated.

Demand Forecasting: The Key to Effective Inventory Management

https://www.netsuite.com/portal/resource/articles/inventory-management/demand-forecasting.shtml

The Newsvendor Model

With the onset of the digital era, the print media has become obsolete and using digital devices to stay updated has become prevalent. But have you ever wondered how a newspaper vendor accurately decides how many newspapers to carry to sell just the right number?

A key idea in supply chain and inventory management is the newsvendor model. Simple is the idea. Consider a street hawker peddling newspapers. They only have one opportunity each morning to purchase numerous newspapers from the printer. Given that unsold copies of today’s newspaper become worthless, how many copies should the vendor keep on hand?

All things considered, the newspaper vendor’s conundrum is simple to resolve, but the approach employed to do so can be utilised to resolve a variety of other, more challenging issues. Let’s go over the fundamentals of the newsvendor model today, talk about how to develop a more complex model, point out instances where it excels, and mention a few typical drawbacks.

Let’s say the vendor sells papers for $2.50 after purchasing them in bulk for $0.50 apiece. Every paper sold brings in a profit of $2.00, while at the end of the day, unsold papers are thrown away at a loss of $0.50. Since it hurts more to turn away a customer than to toss out a paper, it makes sense that the merchant would prefer to have some extra papers. We’ll need to have some idea of anticipated demand in order to determine just how many more are needed.

We need to evaluate some expenses while taking demand into account in order to get the profit-maximizing point. The Critical Fractile (C.F.), which is determined as follows:

C.F. = CU ÷ (CU + CO) = $2.00 ÷ ($2.00 + $0.50) = 0.8, or 80%

Where CU is the cost of Underage and CO is the cost of Overage.

The Cost of Underage, or CU, is the opportunity cost of rejecting a customer. In this case, the $2.00 profit margin represents the expense of being underage.

The Cost of Overage, CO, is the expense incurred when a newspaper is not sold. For the vendor, it is the price they paid per newspaper, $0.50 in this case.

We need to take into consideration additional variables in the actual world since newsvendor issues are more complex. Utilizing more sophisticated overage and underage fees is the first step.

Overage costs are typically simpler to calculate. The following are some typical additions:

Salvage Value: Is there any value you can reclaim? Metal components and perishable food waste can both be sold to scrap yards for recycling.

Opportunity Cost of Capital: What else might you do with the money than buying inventory? Maybe your company has other ideas it wishes to work on. At worst, you may invest in US treasury notes and get some income without taking any risks.

Less terms are added to the cost of underage, but they might be difficult to understand:

The price paid in damaged goodwill for refusing a customer by a business. The client can become angry or dissatisfied and reconsider making an order the following time. You may consider this a decrease in the customer’s lifetime value.

Expedite Fees – On sometimes, stock-outs are just unacceptable, and you may be required to pay a supplier expedite fees to address a looming shortfall.

Increased Costs from Flexible Suppliers – Some businesses employ a dual supplier approach, with a first choice with a lengthy lead time and cheap cost and a secondary alternative with a short lead time and high cost. The underage cost might be adjusted to account for this cost differential.

The demand distribution is the model’s additional element. Demand was expected to be normally distributed in our scenario, which is a critical assumption. We should be prepared to support our use of the normal distribution with facts. The typical distribution is frequently incorrect for slow-moving items because sales can never fall below zero (consider the gamma distribution instead). Alternatively, construct the issue so that you can apply the normal distribution by using the central limit theorem.

The newsvendor model can assist in the resolution of a wide range of issues with a little imagination. It is possible to take into account tier pricing, different levels of security, discounts, and many other factors. It really is an effective method!

Where does the newsvendor model succeed?

When Uncertain Demand Occurs.

The newsvendor’s fundamental tenet is that you should seek less painful outcomes. Which would you rather have, if you were going to be wrong, overstock or understock? How far are you really willing to go? The newsvendor model offers a methodical approach to considering these options and picking a stocking point in the face of uncertainty. When there are many stakeholders and a variety of priorities, structured decision-making is very helpful. If something goes wrong, the newsvendor model and a sound business procedure may put everyone on the same page and cease blaming one other.

Seasonal or one-time decisions

The newsvendor concept is fantastic if you must select and stick to a choice. Since orders must be placed months in advance, many businesses use the model to determine how many Halloween costumes or Christmas decorations to carry. The newsvendor may also benefit from previous purchases of electronic components. Many components become outdated, forcing businesses to make “last-time-buys.” While engineering improves the design, the newsvendor is an excellent way to decide how many older parts to purchase.

Competitive markets

The newsvendor model works effectively for products supplied in competitive, liquid markets because overage and underage charges are straightforward to assess. Since customers have very low switching costs and may not even be aware of stock-out incidents, goodwill cost is frequently overlooked. Price elasticity also has a significant impact. Given a large enough discount, many things are nearly guaranteed to sell, which aids in identifying overage expenses. Profit-maximizing actions are encouraged by the current state of the market.

Common Challenges with the Newsvendor Model

Determining overage and underage expenses can be challenging, particularly for goods without a shelf life.

Nobody ever wants to refuse service to a customer due to a stock shortage. You might permanently lose their business! There may be a significant goodwill cost connected with underage events for industries that rely on strong client relationships (sometimes with hefty switching prices). How would you assess the possibility that a stock-out will be “the straw that broke the camel’s back” and prompt your customer to start exploring for alternative suppliers? The idea is simple, but coming up with a number is difficult, which frequently causes businesses to be overly risk-averse.

Overage expenses are typically easier to understand, but it’s a common mistake to believe that all inventory will be used eventually. Another mistake is relying on the engineering division to come up with ideas for using surplus stock. Engineers must prioritise a broad list of projects when parts become obsolete and consumer preferences shift. Although scrap is a reality, it might be challenging to estimate probability-weighted scrap costs or engineering expenses.

Sometimes, you can’t predict how a need will appear.

Demand ignorance and demand uncertainty are very different from one another. In the former situation, projections for both high and low outcomes are based on knowledge about the shape and nature of the demand. In the latter scenario, there is a much wider range of possible outcomes, and demand projections are speculative rather than supported by facts. In brand-new products with no track record, demand ignorance is most prevalent. Businesses should search for chances to get early signals of demand and utilise those to guide their actions. Making irrational assumptions will only lead to failure.

It is preferable to be unlucky than lucky.

The news seller maximises projected revenues, but this does not guarantee the most profitable results. Imagine flipping 1,000 coins; you would anticipate getting heads almost 50% of the time. However, there is a possibility of seeing 100% heads if you flip a coin five times. When making important, infrequent judgments, especially ones that have all-or-nothing repercussions, use extra caution. Your preference for betting on heads or tails can be determined using the newsvendor model, but the story shouldn’t end there. Engage in effective stakeholder communication and search for original approaches to lessen uncertainty.

References:

https://www.projectclue.com/mass-communication/project-topics-materials-for-undergraduate-students/the-role-of-newspaper-vendor-in-the-newspaper-process