Demand Forecasting and why it is necessary

For a long time, mankind has been captivated by investigating ways to foresee what’s to come. Likewise, business owners and executives regardless of industry, are always looking for ways to improve supply chain efficiency and future-proof their business strategy based on demand forecasting.

In the current ultra-competitive business climate, there is a clear distinction between success and failure. Only 79% of companies report significant, average yearly revenue growth despite having a high-performing supply-chain process in place. Although supply chain function seems integral to business success, it is fundamentally dependent on demand planning and forecasting.

Many businesses rely on forecasts that are based on general market trends, trends, cycles or variations. However, these qualitative trend forecasts can be critically inaccurate leading to unnecessary costs and challenges with regards to inaccurate inventories and improper supply chain management. This phenomenon is also known as ‘the bullwhip effect’ whereby small mistakes like inaccurate demand projections at the front have magnified ramifications in a long supply chain. Other consequences of inaccurate demand forecasting include unfulfilled orders, static inventory costs and unsatisfactory customer service leading to losses in potential business and revenue.

Accuracy in demand forecasting is a tough bolt to nail. But, the consequences due to inaccurate forecasting can be swerved if these forecasts were to be quantitative, based on real customer demand data as supposed to qualitative methods. Most companies of Semiconductor manufacturing industries are already using data-driven demand forecasting strategies to plan their inventories efficiently and improve their existing supply chain management.

Here, we shall understand what demand forecasting is and take a more in-depth look at the benefits of implementing it.

What is Demand Forecasting? 

Demand Forecasting, as the name suggests, is the process of forecasting an estimate of customers’ demands based on historical sales data. This customer-focused predictive analytics is essentially a quantitative, anticipatory strategy based on Machine Learning (ML) algorithms. It can be utilised to gain valuable insights about any particular customer’s demand patterns with the help of a historical database consisting of data collected about their order history. The data-driven technology thus helps businesses prioritise customers and accurately predict their demands to plan stock inventories for their foreseeable purchase and avoid any out-of-stock situations. It also virtually eliminates ‘holding inventory’ costs in time and money by allowing production to be adequate, not too much nor too little.

What are the benefits of Demand Forecasting?

1. Improve Supply Chain Efficiency and Finances

According to McKinsey, supply-chain performance is assessed by the Right Product, Right Time, Right Location (RPRTRL) metric. Amongst the three components (right product, right time and right location) that make up the metric, the first component, right product referring to demand planning is the key determinant of supply chain efficiency. It is thus utmost for this demand planning to be as close to accuracy as possible as its consequences affect the entire chain much like the ‘bull-whip effect’ mentioned above.

Demand forecasting projects accurate demand information and scheduled target alerts based on data-analytics to guide the supply chain function, from inventory production to delivery. It also includes giving businesses the leg up by allowing them to optimise their schedule to precisely synchronise supply and demand to match scheduled targets. This is especially helpful to businesses that depend on internal and external suppliers or distributors. These small tweaks in supply chain function can thus improve overall supply chain efficiency.

Finance is the most influential language in any business, and most times, planning and forecasting are done solely for financial benefits. Most companies aim to reduce unnecessary costs as it’s the most significant area to focus on when it comes to budgeting finances.

Holding excess and obsolete inventories due to inaccurate demand forecasts brings huge financial strains due to warehousing, insurance and taxes related costs. However, this strain can be alleviated with the help of this data-driven demand forecasting solution as it enables businesses to plan ahead with lead time and produce inventory adequately based on accurate demand projections. Thus, allowing for the preservation of cash flow by controlling production costs and unnecessary ‘holding costs’ that can be repurposed and allocated for expansion and growth.

2. Optimise Labour Management 

All businesses experience periods of high and low demands and this automatically impacts the labour management decisions. Businesses often hire workers at all times to evade circumstances of understaffing in the event of a large order. But usually, these workers are scheduled for short hours when there are lesser demands which leads to high turnovers. Likewise, finding new employees and hiring them especially during a spike of demand is extremely costly.

To illustrate, having large numbers of labourers employed during a period of limited sales or limited labourers employed during a high demand period consequently leads to wastage in resources and shrinkage in order fulfilment respectively.

Demand forecasting eliminates the uncertainty in demands and thus the resultant shrinkage or wastage due to improper labour management. Possessing a precise forecast of customer demands automatically allows business owners and employers to optimise labour schedules accordingly and set targets based on them. It ensures timely fulfilment of orders while enabling efficient management and recruitment of labour voiding the unnecessary costs of overtime labour with unstabilised production.

This is especially beneficial to businesses that depend on internal and external suppliers or distributors to communicate anticipated targets.

3. Increase Customer satisfaction 

Customer satisfaction is the end goal and forms the cornerstone for customer retention and referrals regardless of business or industry. It’s all about making sure the customers are delivered the right products at the right time without any delays or discrepancies in expectations. Mostly than not, customers know exactly what they want. Thus, the data-driven strategy forecasts demands based on their historical data of orders and facilitate business owners to prepare inventories for prompt delivery. Being a customer-focused strategy at every step of the way this technological solution guarantees increased customer retention and satisfaction in businesses, thereby promoting long term growth.

Deploy Demand Forecasting with groundup.ai

Although the business climate is volatile and the future is not specifically predictable in nature, forecasts still form the basis of almost every significant managerial decision now more so than ever, in any organisation or company. It is crucial for businesses to have an accurate and efficient demand forecasting system based on real-customer data, that aims to provide customers, the ‘right thing’ at the ‘right time’.

Demand forecasting is customer-focused, data-driven analytics based on quantitative analysis that tackles the root causes of customer churn and revenue loss mainly by increasing customer chain efficiency. The technological solution looks past market trends and cycles to focus on real customer-demand databases to project accurate inventory production. This fundamentally leads to greater customer satisfaction and retention. Hence, it is a strategy that businesses should immediately jump onto to not only budget finances effectively but also have a competitive advantage over their competitors.

Get in touch with us to discuss further and we could explore together the better data-driven strategy for all-round efficiency. At groundup.ai, we will support and guide you through the implementation process. Using our technology, we can efficiently pull data and behaviours from past customer behaviours to create evolutionary changes to your business and drive business value.