Demand Forecasting – What, How, and Why answered.

Forecasting is the modern-day gamble!

This is what I had read somewhere the other day, which I believe is totally untrue. Gambling is when we make decisions on unforeseen probabilities, and while only having a partial idea of the outcome or risks. Whereas Forecasting – especially demand forecasting in businesses – is when calculated decisions are made using historical data and research. Here, trusted techniques like deep learning from ML are used.
With a study of different features of demand forecasting and its company-specific applications, one can profitably use it. It enhances not only the supply chain of the company but also enhances profits in terms of fulfilled customers – which is necessary for today’s consumer-driven markets.

First, let’s take a deep dive into exploring Demand Forecasting and then we’ll see how it works and why it is important for businesses to upgrade to it.

What is Demand Forecasting?

Demand forecasting can simply be stated as reading the customer’s mindset in order to predict future demand for products and/or services. This can help companies strike a balance between overstocking – investing all the cash flow into inventory, or understocking – only investing when there are orders, which leads to the ‘Out of Stock’ condition.

To get this done accurately, statistical and machine learning algorithms are fed with accurate time-series data – a historical series of data relevant to the operation at hand. Since the outcome totally depends on the extensive and massive data that we feed to these algorithms, it should be kept in mind that along with the historical trends, other impactful variables like the change in prices, feedbacks and seasons should also be taken into consideration. With product demand, the applications of forecasting can include resource planning, labour demand, and financial performance for the company.

Difference between Predictive Analytics and Demand Forecasting:

Demand forecasting is a field of advanced technology known as “Predictive Analytics”. Where the former focuses on the prediction of sales demand, labour requirement, and inventory requirement, the latter goes well beyond them. It takes into consideration factors like socio-economic trends, geopolitical changes, and global behavioural sensitivities to predict what will occur in the future. This can thus be used to decipher a downward trend in the economy or falling interest in an organization’s stock/ product – well before it would be visible as a risk.
So basically, the former is all about making deductions based on what has occurred in the past and the latter is capable of predicting what new can happen, which we as humans cannot as it has never happened before.

A Deep Dive into the Methodology:

1. Research:

   While data is of prime importance, there can be two approaches to collect that data – Qualitative and Quantitative.

• Qualitative forecasting research: As we know that factors like customer experience and trust can never be measured in numbers, only understood. Thus, taking into consideration factors like feedbacks and impressions becomes critical. This forecasting method involves using professional experience to interpret data. While using this technique to project sales outcome understand the drive of their customers, companies generally focus on the impact of the advertisement campaigns, recent market trends and the review section.
• Quantitative forecasting research: In here, the hard data which includes numbers and facts like the previous sales history or peaks and downfalls, are taken into consideration. Data plots including infographics are made to analyse the underlying trends. The important factor while using this method is having long term recorded data to draw reliable conclusions from. Thus, this method is more useful to established companies rather than the newer ones.
  With the right combination of these two, all the data that you have can be put to use – in one way or the other – resulting in profits for the company.

  2.Approach:
  With the research done, we must then identify the approach that would suit us the best.

• Passive Approach: Well-established companies have a planned course for years, which does not keep changing with trends. For such companies, the passive approach is the correct choice. Here marketing campaigns, the introduction of new products, and adding sales channels is all determined keeping in mind the previous continual trends obtained from the massive data.
• Time-Series approach: This approach includes pinpointing the highs and lows on the time series data and planning the course of action based on it. It is suitable for medium-sized businesses where a lot of historical data is not available and demand change according to seasons.
• Casual approach: This is the most advanced approach which takes into consideration both qualitative and quantitative research data. With the past sales data, factors like the external economic forces and competitors are also included in the datasets. With the increase in the dataset, the research time and extensiveness also increases. Thus, this approach should be used by those companies that are planning to enter the market or diversify their market.

Why leverage Demand Forecasting:

To have fulfilled customers and a near to exact required inventory – Isn’t that every business owner’s dream? Well, forecasting doesn’t just help you with that but has other noteworthy advantages too.

1. ML can help figure patterns, identify demand signals and spot relationships in different data samples – with accuracy. So, instead of spending the human task force on it, making algorithms work for you can simply save time, increase efficiency, and give out reliable forecasts.
2. Loss incurred after buying slow-moving/dead products or not buying products that would be in demand can reduce liquidity – because of cash being frozen in stocks. Instead balancing both using the forecasts can optimize the supply chain.
3. Peak times bring along unsatisfied customers if engagement is improper. With accurate predictions, the workforce can be optimized. Companies can hire staff only when there would be a need in peak sale times. Accurate predictions can thus help both the company and the customers.

Challenges:

While there a lot of advantages, there certainly are some challenges that have to be coped up with. Witnessing the rapid growth in technology, we expect to solve these challenges soon. Let’s have a look at some of them.

1. With the rapid impact that is happening in the technology, market trends and customer demands change by the day. Keeping up with them using algorithms can be a tedious task. For addressing this volatility, the algorithms must be able to re-train themselves from time to time.
2. The one-size-fits-all approach cannot be used here. With the change in store locations, target audience, and trends, algorithms have to be tailored to every end user’s application. This tailoring needs expertise and undivided time.
3. While these forecasting methods can produce numbers and figures, they lack intuition and experience. To have them think like humans, we’d have to inculcate all the impactful facts into the predictive datasets from time to time.

Conclusion:

Demand forecasting is not a new concept, traditionally it was done manually by honing the skill of intuition, with a blend of knowledge. With forecasting software and tools, the amount of data being processed can be multiplied manifolds – easily. By using these techniques, companies can enhance their supply chains and plan – budgets, employees and resources. With demand forecasting, bagging potential opportunities can be your next chance!