Guest Post: Eight Princip...

Guest Post: Eight Principles of Supply Chain Forecasting (And How They Can Help You Plan For Winter 2022)

Nov 07, 2022
Guest Post: Eight Principles of Supply Chain Forecasting (And How They Can Help You Plan For Winter 2022)

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Supply chain forecasting is vital to managing a business, keeping it thriving, and ensuring customers are happy. It is a complex activity that involves anticipating problems and being prepared for the unexpected, as well as keeping a close eye on what’s going on in the world and spotting developments that could impact the supply chain. 

It’s necessary to have a firm grasp of supply and demand, manage high-volume shipping, and head off anything that could drain money from the business or let down valued customers. Forecasting well means monitoring the past, present, and future simultaneously. The past is where experience comes from; it’s where you can see patterns and fluctuations and track where they lead.      

In terms of the present, what’s happening in the world and how this affects your business has the potential to impact supply and demand, which can, in turn, impact predictions for the future.

What is Supply Chain Forecasting?

Knowing when a product will arrive, whether it’s merchandise or raw materials, is the core of supply chain forecasting. But just as predictions for online retail sales are based on analyzing trends, supply chain forecasting involves looking at the available data on the product demand levels in previous months and years.

However, other factors influence forecasting, such as seasonal tendencies, the political situation in the world, product availability, and cost, which are all factors that can have an impact on supply. All this information is used to calculate and predict how the supply chain will function.

Guest Post: Eight Principles of Supply Chain Forecasting (And How They Can Help You Plan For Winter 2022) - Image 1

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The Challenges of Supply Chain Forecasting

From how peak fees may impact major shippers to dealing with the unknown ramifications of a global pandemic, accurate supply chain forecasting is challenging.

 One challenge we’ve touched on is world events, whether this is Brexit (the UK’s departure from the European Union) Russia’s invasion of Ukraine, or Covid-19; events happening on the other side of the world can heavily impact a business and its supply chains. Lead times can lengthen substantially leaving a business low on stock or lacking stock completely. In fact, recent events have accelerated the Supply Chain Digital Transformation in many companies. 

Sometimes world events are predicted and expected, but sometimes as with the global pandemic, they take the whole world by surprise. Contingency plans and having sufficient stock can ameliorate the shock to the system of something like a global health emergency, but it’s not as simple as stockpiling in case of emergencies. It can tie up a large amount of capital and is out of the question for perishable goods.

There can, of course, be other sudden changes that are nothing to do with disasters. Social media is a fertile ground for new interests and trends emerging among consumers. Companies may be over the moon at finding one of their products is the latest thing, but it can leave them scrabbling to get in enough stock before the tidal wave of demand subsides.  

Keeping an accurate record of what a business has in stock‍ is critical to managing a supply chain, but this presents challenges. Online shopping and the fact that customers can return items much more easily than they can via bricks and mortar shops mean a business may end up overstocked. 

It can also be challenging to keep an accurate tally of total stock when a business has an online as well as bricks and mortar component. These two areas often have separate data that is not automatically combined and integrated. For this reason, companies who invest in training data analysis for their staff certainly give themselves an advantage when it comes to accurate supply chain forecasting and management.

Supply Chain Forecasting Techniques

There are two types of forecasting: quantitative and qualitative.

Quantitative uses data about past sales and demand to make predictions. Qualitative forecasting, on the other hand, relies on making informed predictions based on new products and market knowledge when no historic data exists.‍  

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Eight Principles

Let’s look at eight quantitative and qualitative methods for supply chain forecasting in detail.  

Quantitative

1) Moving average forecasting

This method has advantages; it’s a simple method that involves looking at historical averages. The drawback of this technique is that it doesn’t place a value judgment on data, according to which year might be the most representative or if certain data is linked to seasonal trends. But it does give a broad stroke forecast based on past demand. 

Better technology can make this method more effective, for example, pandas DataFrame Python, but in general it’s a method better suited to lower volume items and inventory control.

2) Exponential smoothing

For short-term forecasts, this method is ideal. It’s similar to moving average forecasting, but uses recent data giving this more weight than is the case with moving average forecasting. It also takes into account seasonal trends, although its main focus is still historical data.

3) Multiple Aggregation Prediction Algorithm (MAPA)

Although it doesn’t have the same track record as other methods that have been around longer, this method is ideal for businesses trading in seasonal products. The advantage is that this technique can consider trends and ensure that businesses don’t underestimate demand or conversely estimate more demand than really exists. 

4) Auto-regressive integrated moving average (ARIMA)

This method is known for its accuracy, especially over the short term. Its drawbacks are that it can be time-consuming and it isn’t the most economical of methods for supply chain forecasting. Another drawback is that it can be inaccurate during unexpected events such as a financial crisis.

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Qualitative

5) Salesforce composition

Without hard data, this method of qualitative forecasting relies on the expertise and experience of the business’s staff. The opinions of experienced employees are relatively easy to collect and can be fairly accurate. The information is often gathered through team consultations or panels.

6) Historical analogies

Longer term this method can be extremely accurate, although it is less so for short-term forecasting. It’s based on predicting sales of a new product, by looking at how well a current product sells. This comparison can be made between products from the same company or by looking at products belonging to competitors. 

7) Market research

Market research can give valuable insights into how much demand is likely for a particular product or product range. The target customers are generally polled or questioned in focus groups and data is collected. There is a fairly substantial time investment in this method. It also needs staffing and planning. But it can ensure up-to-date and accurate information about likely demand on which to base forecasts.   

8) The Delphi method

This method is based on gathering information from experts. In some respects, this is superficially somewhat like collecting market research, except that instead of customers, the information comes from professionals likely to give helpful insights based on their industry knowledge. The advantage of this method is that it is generally unbiased as panel members don’t share their responses. It’s an effective technique for long-term forecasting.

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The Focus of Forecasting

Forecasting, by its very nature, is based partly on facts and partly on best guesses. It is something that all the available methods have in common. Supply chain forecasting is imperfect but nonetheless valuable; without it, it would be impossible for businesses to anticipate changes and capitalize on upcoming trends and seasonal surges in demand.

 Quantitative forecasting is based on concrete data and is generally more accurate and reliable. But qualitative forecasting also has its place and can be very useful where no historical data is available. 

With all methods of forecasting, handling the data gathered; storing, and accessing it, can be streamlined by using technology such as Elasticsearch documentation. Data, whether it is gathered from expert panels, market research, or collected from historical sales figures, is at the heart of forecasting.

Guest Post: Eight Principles of Supply Chain Forecasting (And How They Can Help You Plan For Winter 2022) - Image 5

(Image Source: Pexels)

Both types of forecasting can be effective, depending on the type of business and its products. Before opting for a particular method, as well as considering if the business needs a technique for short or longer-term forecasts, it’s important to look at the demands of the business itself. 

For example, a business selling perishable goods will have a far greater need for accuracy when it comes to timescales of demand than, say, a business selling clothing. Likewise, businesses that sell seasonal products may need to look a year ahead in terms of predicting the volume of products that will be needed. 

Supply chain forecasting is ultimately about making sure products reach the hands of customers when and where needed. In an unpredictable world, forecasting can help predict what demand is likely to be and where a business should focus its efforts. 

About the author 

 Ultimate E-Commerce Strategies for the Summer Season - Author Bio Pohan Lin

Pohan Lin - Senior Web Marketing and Localizations Manager #1:

Pohan Lin is the Senior Web Marketing and Localizations Manager at Databricks, a global Data and AI provider connecting the features of Databricks HDFS, data warehouses and data lakes to create lakehouse architecture. With over 18 years of experience in web marketing, online SaaS business, and ecommerce growth. Pohan is passionate about innovation and is dedicated to communicating the significant impact data has in marketing. Pohan Lin also published articles for domains such as SME-News.

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