Supply chain forecasting is, in essence, an exercise in supply and demand analysis. Building an accurate forecast very much depends on historical data you’ve collected, types of products you’re selling, the industry you’re in, and specific product lifecycles, including which new products are being launched and which old products are being discontinued.
By researching your competition, collecting and analyzing real-time data from your suppliers, and carefully studying market trends and consumer behavior, you’ll get increasingly precise forecasts that you can use to optimize your supply chain.
In this article, we’ll explain some of the other benefits of supply chain forecasting, different forecasting techniques you can use, and how a system like DATASCOPE WMS can keep your supply chain lean and scalable.
WHY BOTHER FORECASTING?
Good forecasting models can make it easier to predict and react to potentially disruptive events in your supply chain. Without forecasting, you end up with manufacturing facilities that underproduce, supply shortages that force your customers to buy elsewhere and supply surpluses that cause cost overruns and lead to unnecessary inventory control expenses. Forecasting can help you:
- Keep warehouse costs down by eliminating product laying around on the floor.
- Prepare for promotions, product launches and any other events that might result in price fluctuations.
- Maximize production capacity and operational efficiency by minimizing machine and employee downtime and more effectively allocating manufacturing resources.
- Facilitate all types of supply chain planning, including budgeting and financial planning, distribution planning, resource planning, expansion planning, raw material planning, and risk mitigation.
- Meet increased customer demand and improve customer satisfaction. By filling your customers’ orders on time, delivering those orders more quickly, and offering on-demand support, that will go a long way towards getting you more referrals and better reviews.
- Better organize scheduling and staffing to guarantee you have enough labor on hand at all times.
POPULAR FORECASTING TECHNIQUES
Modern quantitative forecasting methods are highly technical, relying on technologies like artificial intelligence (AI) and machine learning to quantify as many data points as possible. These methods include regressions and econometric analyses, adaptive and exponential smoothing, time series analysis, and lifecycle modeling.
Qualitative forecasting techniques on the other hand, while not as technical, can still help you prepare for situations that your software can’t, such as possible competitor mergers or acquisitions within your industry that will affect your business. Examples include intuitive planning, market research, focus groups, expert opinions and panels, or historical analogies.
The 4 main supply chain forecasting techniques we’re focused on are primarily quantitative:
SUPPLY FORECASTING
Supply forecasts are an estimation of the availability of product — how much product will be available and when it will be available. As the focus is on production, a detailed supply forecast requires analyzing factors that influence production, such as delivery capacity, weather conditions and manufacturing technology used.
DEMAND FORECASTING
A demand forecast predicts how much product your customers are likely to want over the course of a month, quarter, year, etc. These types of forecasts focus on sales trends and the likelihood of potential events that can affect future demand. Demand forecasts help with the optimization of inventory levels by preventing inventory excesses or obsolescence due to out-of-season stock, rapidly changing consumer trends, or stock spoilage.
PRICE FORECASTING
Looking at a combination of supply and demand data, a price forecast will factor in things like disruptions from natural disasters, cultural trends, seasonal trends, or employment levels, all of which can affect the price of goods produced and moved through your supply chain.
SEASONAL FORECASTING
Often used in retail supply chains, seasonal forecasting is a variation of demand planning where you anticipate spikes in buyers’ interest during holiday times. These trends tend to remain constant year-to-year which makes it easier to figure out staffing requirements in warehouses, or how best to structure delivery routes. Seasonal forecasting can help you add lots of product to your inventory in a short period of time and avoid having too much stock in inventory for when products go in and out of style.
USING SUPPLY CHAIN FORECASTING TOOLS LIKE DATASCOPE WMS
Many supply chains are still “forecasting” the old-fashioned way: old-time spreadsheets, manual data entry and clunky calculators. Nowadays, supply chain management software and automation can completely transform the quality of predictions you make. Successful forecasting boils down to collecting the right data. With the data analysis and visualization tools in our KPI Reporting module, you can start tracking the KPIs and metrics that are most relevant to your supply chain. You can even customize your KPI reporting dashboard with your own KPIs.
We’ve designed our solution to be fully customizable so our customers and partners can quickly and easily make modifications as they see fit. Modules are scalable and able to be integrated into every stage of your supply chain. Combined with SYSPRO’s Cloud First focus, DATASCOPE has developed our Microsoft Azure cloud-based solution that is scalable, secure, mobile and allows you to collect data and keep your teams in sync from anywhere in real-time. Every department and every tier of your supply chain can easily stay up to date with your organization’s forecasting goals and use the collected data to build incredibly accurate forecasts.
DATASCOPE WMS is a premier provider of custom WMS software built specifically for the SYSPRO ERP. Schedule your software demo today to find out how you can use it to build better forecasts for your supply chain operations