In both the past century and just the past few years, manufacturing processes have made leaps and bounds. This evolution in manufacturing began with automation and it is now being pushed by data. Access to data is allowing companies to created better automated processes, improve collaboration, and boost productivity in several ways.
Here’s how to achieve production efficiencies by leveraging data to produce better forecasting and improved demand management systems.
Moving Beyond Spreadsheets
Traditionally, companies determined their supply and inventory needs with a series of complex spreadsheets. This is still being done today, and it is a flawed system. The problem with using spreadsheets is that forecasting is based largely on historical sales numbers, not on market demand factors. More sophisticated demand management systems are needed that can analyze various data sets to give companies better forecasting figures.
When done properly, demand planning can provide a company with some significant savings. One commodity management solutions provider concluded that for every 1 percent improvement in forecast accuracy, a business might see a drop in inventory levels of 1 to 2 percent. For example, a company with inventory turnover of $1 billion might see savings of $5 to $10 million annually.
Elements of an Effective Demand Planning System
A forecasting and demand management system for a company doesn’t need to be complex. The level of complexity will depend on the type of business, its size, and the structure of the supply chain. Other demand planning factors that you’ll need to consider include the volatility in your product’s demand, the level of visibility in your supply chain, and your product delivery/transportation system.
Here are several of the key elements in an effective demand planning system:
- Trends and anomalies. The data analyzed should combine historical data with external econometrics, which can identify such things as seasonal trends, consumer sentiment, and even political risks. All of these items are used in demand forecasting.
- Structure and workflow. Companies can update their workflows as frequently as daily based on these continuously adjusted forecasts.
- Forecast accuracy. These systems can take a closer look at forecast accuracy data to determine why errors are made and ensure that mistakes aren’t repeated. Accuracy numbers improve due to this ongoing analysis.
- Complete integration. The purpose of a demand planning and forecasting system is to both improve service to customers and achieve greater efficiencies. When the system is integrated throughout the supply chain and delivery process, it becomes much more functional.
- Collaboration. This refers to both internal and external collaboration (see below).
Collaboration in Forecasting is a Must
One of the most important factors in forecast accuracy is collaboration. This is also one of the elements that most companies find a particular challenge. Forecasting is most accurate when data is assembled across multiple areas of an organization such as sales, customer service, production, and product management. Information from outside the company is useful as well, such as data from suppliers and customers. Yet, only about half of companies surveyed indicated that they were participating in this level of collaboration.
With today’s changing economy, profit margins become more narrow and it’s necessary to create savings and efficiencies and every way possible. The companies that will be successful are those who can implement these demand planning processes to boost productivity and gain the greatest competitive edge.