The supply chain is the manufacturing and e-commerce circulation system. It transports products from suppliers to fulfillment centers and finally to a customer’s front door.
Because there are so many moving parts, even a minor disruption can devastate the system. Disruption is any sudden change or crisis that harms any part of the supply chain.
Although businesses understand how the supply chain works, customers often need to be made aware of how many steps it takes to get a product from point A to point B. Even when packages are loaded onto a truck for delivery, the obstacles that can arise to complete the final delivery step can derail all previous efforts.
Many major supply chain disruptions, such as seasonal delays during the holidays, are predictable, whereas others, such as a global pandemic, are not.
Learning how to overcome supply chain disruptions through proper supply chain forecasting and network optimization will allow you to continue operating and maintaining profitability even when chaos erupts.
- What exactly is supply chain forecasting?
- Why is supply chain forecasting necessary?
- Forecasting supply chains using various methods
- Disruptions of Various Kinds
What is supply chain forecasting?
Supply chain forecasting combines data from the previous supply with insights and understandings about a demand to help you make the best business decisions, whether it’s stock inventory, cargo booking, budget planning, or expanding into new markets.
The majority of supply chain forecasting is spent on supply analysis. It entails analyzing data about your suppliers to determine when you need to order products from them, whether they are finished goods or raw materials that will be assembled further down the supply chain.
Demand analysis is also essential for understanding how much of your product your customers want during any given week, month, or quarter. This is influenced by various factors, which can be predictable, such as seasons and holidays, or unexpected, such as global events and natural disasters. Such events frequently impact multiple modes of transportation, such as ocean freight or inland transportation.
Why is supply chain forecasting important?
Supply chain forecasting can play an essential role in ensuring an efficient supply chain and a thriving business:
Strategic planning – Businesses can be built or destroyed by their strategies in areas such as market expansion, budgeting, and risk assessment. Improving supply chain forecasting provides you with the information you need to make wise decisions, ensuring that your suppliers can meet your demand.
Keeping inventory in check – If you better understand the demand for your products in different markets, you can work more closely and efficiently with suppliers to keep your inventory levels consistent throughout the year. This reduces shortages, which pleases your customers, and keeps warehouse fees under control without the need to store unneeded stock.
Improved customer experience – Customer experience will define supply chains in the coming years. You can manage your suppliers to ensure orders are fulfilled on time and always in stock if you can predict customer demand. As a result, your customers and your company have gained trust.
Methods of forecasting – Quantitative
There are two main approaches to supply chain forecasting: quantitative and qualitative. Quantitative forecasting uses complex algorithms and computer programs to predict future sales based on historical data.
The methods listed below may be encountered in quantitative forecasting. Each has advantages and disadvantages and should be carefully considered to determine its best application:
Moving average forecasting is a simple method of predicting that uses historical averages. However, it treats all data equally and does not account for the fact that more recent data may be a better predictor of future trends than data from three or five years ago – and it does not account for seasonality or trends.
Exponential smoothing also considers historical data but prioritizes recent data and accounts for seasonality. This makes it ideal for making short-term predictions.
The auto-regressive integrated moving average (ARIMA) forecasting method is well-known for its accuracy, but it is also time-consuming and expensive. It works well for forecasting up to 18 months or less.
Multiple Aggregation Prediction Algorithm (MAPA) is a newer quantitative forecasting method designed explicitly for seasonality, making it ideal for businesses that produce seasonal items.
Methods of forecasting – Qualitative
When historical data is difficult to come by, such as when launching a new product, a new approach is required – and this is where qualitative forecasting comes in handy. It is based on the insights, expertise, and experience of industry experts, as well as more in-depth research:
Historical analogies forecast sales by assuming that the sales of new products will mirror the sales of an existing product produced by you or a competitor. While it can be helpful in the long run, it is not recommended for short-term forecasting.
Internal insights are a bottom-up forecasting approach that uses the insights and opinions of experienced staff members to inform predictions. It is not known for its accuracy, as one might expect, but it is an option when quantitative methods are not available.
Many businesses will be familiar with market research, the process of researching, surveying, polling, or interviewing a specific demographic.
Different kinds of disruptions
When managing supply chain disruptions, it is critical first to assess the various types that can occur. The main supply chain challenges or potential disruption scenarios are listed below, along with the steps to resolve them.
- Natural catastrophes
- Transportation Delays and Price Changes
- Cybersecurity Attacks
- Pandemics Around the World
What is the foremost method of supply chain forecasting?
There is no one-size-fits-all approach to supply chain forecasting – no matter which you choose, you will never be 100% accurate – because at least some of the forecastings are based on assumptions; and there will always be unforeseen events that defy those assumptions – such as a pandemic!
However, while qualitative forecasting has been used when historical data is unavailable or unreliable, it is widely agreed that quantitative forecasting is the most effective method. It employs concrete data and statistical techniques to eliminate bias while producing more evident and accurate results.
We don’t have a crystal ball, so we’re all preparing for an uncertain future. Only complete visibility into all tiers of your supply chain can ensure your company’s resilience, agility, and profitability in the future.