Disruption is quite the buzzword these days. Tech startups and product developers everywhere want to “disrupt” the market and create new demand. But disruption means something different to you. 

Supply chain disruption strikes fear into the hearts of managers, executives, and all involved in running a business. Like oxygen to the brain, a lack of supply can cause serious damage or signal the death rattle of an organization.

Instead of waiting around for the inevitable, like horror-film fodder, you are hoping for a proactive approach to battling the unexpected. The solution you’re looking for is supply chain forecasting. 

What is supply chain forecasting?

Supply chain forecasting involves using historical data and developing trends to create predictions about the movement of your goods to, through, and from your business. It uses both quantitative data and qualitative information to build a predictive model tailored to a company and the market it is in.

With supply chain forecasting, organizations can be better prepared to meet supply and demand as well as mitigate the costs of supply chain logistics and disruption. 

Quantitative vs. qualitative data

Quantitative data is that which can be absolutely counted or quantified. An example would be the sales performance of past similar products or total market revenue. In forecasting, this type of data is used for building predictive algorithms.

Qualitative data cannot necessarily be given an absolute numerical value. This type of data is based more on subjectivity relating to the quality of a topic. 

An example would be a survey of industry leaders regarding the market’s upcoming trends. This information is used to hedge for the future with mixed results. 

Why is supply chain forecasting important?

Whether it’s virtual computing resources or raw materials, the supply chain is the lifeblood of any business. Past disruptions from a global pandemic have had many organizations reeling from the effects. Recent biases aside, there are several benefits to improved supply chain forecasting:

• Increase sales: Bbetter able to meet demand and not lose sales via stock-outs.
• Decreased expenses: Fforecasting helps you optimize warehousing and storage costs and reduce overstocking fees. This frees up business cash for other areas.
• Better efficiency: Ssupply chain forecasting allows you to improve every logistical step or process. This means stock spends minimal time in any location, and manual tasks can be automated.
• Improved customer experience: Aavoid disappointment by running out of hot items or product types, and facilitate fast shipping and easy returns. 

7 Methods of Supply Chain Forecasting

When building a supply chain forecasting model, your business has plenty of options. Find the method best suited to your business goals, product type, and industry. 

1. Examine historical data analysis

The first approach in supply chain forecasting is to look back at what happened in the past. Historical data can be a good predictor of the future, but it depends on many variables. Data is collected in areas such aslike the performance of your business, similar products, and the market itself. 

Regardless of what type of modeling you choose to implement, the more data, the better accuracy of the predictions. 

The most basic historical analysis is a simple moving average. Relatively straightforward, the simple moving average makes a forecast based on the mean of past data from a set duration. As time progresses, this rolling average adjusts with the most recent data. 

For example, Q4 predictions can be based on Q1-Q3 numbers, while the following Q1 predictions would be based on the previous Q1-Q4. 

Another type of historical data analysis method is what’s known as adaptive smoothing or exponential smoothing. This technique uses exponential algorithms to smooth out predictions by giving more weight to the most recent data. Adaptive smoothing is typically more accurate than simple moving averages with short-term predictions such as seasonal demand.

2. Collaborative Planning, Forecasting, and Replenishment (CPFR)

A framework, collaborative planning, forecasting, and replenishing is a holistic approach to supply chain management. It involves the entire architecture of an organization and its suppliers and trading partners. 

The main aim is to improve information-sharing across the supply chain and implement joint decision-making processes. CPFR can be a massive project to tackle but reaps big benefits for large organizations and enterprises.

Historical data is shared among business partners, so you have deeper layers of information for analysis. With insider datasets from suppliers and traders, you can build more predictive forecasting models to meet demand and optimize areas like warehousing and inventory replenishment. 

Like other forecasting approaches, CPFR continually evolves as information is shared and more data pours into the system. With so many channels to manage, you will want to use the best e-commerce ERP solutions available. 

ERP solutions help your business scale with growth and adjust to market demand on the fly. Access to real-time data such aslike sales reports will go a long way to improving and optimizing your supply chain forecasting.

3. Use demand sensing and demand shaping

Demand sensing and demand shaping are common methods for improving supply chain forecasting. Demand sensing involves analyzing real-time data capture to reduce the error of short-term supply chain forecasting.

The vendors who offer these services implement demand sensing using artificial intelligence and machine learning. Demand sensing is geared towards making informedaccurate predictions for areas such aslike promotions, seasonality, market shifts, and local differences, e.g., between the UK and USA, natural disasters, and other potential disruptions.

Demand shaping is exactly what it sounds like. While some market disruption (such aslike a pandemic) is harder to predict, promotional offers and new product launches are much more under your control. With demand shaping, you can try and shape consumer demand and change the market landscape with a well-planned marketing strategy.

4. Conduct market research and customer insights

Qualitative data is very useful for demand forecasting and optimizing supply chain management. The first task of any qualitative implementation is to gather the data. There is a wide range of sources for information about your industry, the market, and consumers. 

Conduct market research by way of competitive analysis and look deeper at market trends in the country you’re in, whether it’s the UK, USA, or UAE. You can conduct research and gather responses from industry leaders, subject matter experts, and your target audience.

Customer feedback questionnaires are also useful for predicting customer behavior with product launches or upgrades. This will help you be better prepared to manage demand through supply chain forecasting.

 A market research method that attempts to remove bias from qualitative research is known as the Delphi method. With this technique, all surveys are answered anonymously by participants. The answers are collected, recorded, and shared with the same participants. Then another round of questions is conducted with the specialists or subject experts. 

This testing cycle is repeated until a general consensus is reached. Because no one knows who else is responding, no individual has any added influence to affect the opinions of others. Despite its rudimentary nature, the Delphi method has proven reliable for some types of long-term forecasting.

5. Implement advanced technology and automation

Depending on how you plan to use quantitative and qualitative data, you will use one or more methods for supply chain forecasting. With business process management software, you can implement AI-powered technology and automation to boost your organization’s supply chain management. 

With the right tools, you work smarter with real-time information about your organization’s finance, HR, and daily operations. Data capture on every business process is always up to date so managers can make the best-informed decisions.

Business process management software can also automate the complex processes of the supply chain. Silos are broken down, and finance, operations, sales, marketing, and more all work together to adapt your business to rapidly changing market conditions. 

You will have better supply chain forecasting through collaboration and digital automation—which means higher growth.!

Other supply chain forecasting approaches employ various forms of advanced technology. 

One example is the multiple aggregation prediction algorithm (MAPA). This method uses machine learning to analyze multiple time series datasets. 

These ever-changing data sources increase the accuracy of long-term forecasting by creating and maintaining complex mathematical models. With MAPA, many predictions are generated in real -time and continually adjusted to reflect recent trends. 

6. Utilize supplier information

Making use of supplier information can boost any predictive modeling or forecasting for your business. The aforementioned CPFR method is only one of many that utilize supplier information. 

Life cycle modeling is another supply chain forecasting technique that gleans data from manufacturers, suppliers, retailers, and every logistical stop from raw material to consumer. Supplier information is an important contributor to building an overall picture of the life cycle of new products. 

Using modeling with supplier information is especially useful for startups and new products in the conceptual development phase. This is because of the lack of available historical business data.

7. Regularly monitor and create adjustments

This last method is less of a technique and more of a best practice. Regardless of the analytical models and market research methods you implement, the data collection process must not stop. Information is continually aggregated, and collaborative decision-making gleans insights from analytical reporting.

World events will always keep you on your toes. You want to use enterprise-level business intelligence tools to streamline the process of collecting, analyzing, and reporting data for supply chain management. 

Prepare for the future with supply chain forecasting

Supply chain forecasting is the best tool you have to navigate the storms of disruption. By using the right combination of modeling approaches and market research, you can build a proprietary forecasting model that elevates your business. 

Not only will you be better prepared for rapid market changes, but you will also be able to fully capitalize on trends and leave your competitors in the dust.

 

Author Bio: Stacey McIntosh is the editor-in-chief of Sage Advice UK and Sage Advice Ireland. He has more than 18 years of editorial, PR and social media experience and has worked across print and online for national newspapers, magazines, PR and marketing agencies including Metro, GQ, Men’s Fitness, International Business Times UK and Cool Blue.