Brian Hoey - April 03, 2018
The first hurdle for many businesses in improving their demand forecasting capabilities is to increase end-to-end (E2E) visibility. To that end, the first and perhaps most important step to take in improving the predictive power of your business is to move away from the use of pen and ink or Excel spreadsheets when creating forecasts. By managing your predictions in a manner that is effectively siloized and cut off from other business functions, your planning operations will necessarily suffer from low visibility, while offering no real opportunities for analytics integration. Not only will predictions made in such a disconnected environment be less accurate (due to limited stakeholder input and other factors), but they will be less likely to be acted upon by decision-makers throughout the organization. Once your predictions are being developed in a more open environment that promotes visibility and transparency throughout the entire value chain, your next concern will be the overall quality of the data that’s being used to drive your forecasting models. This will mean reducing siloes not just at the level of specific operations, but cross-organizationally. Businesses take varying approaches to removing siloes, but one of the most effect tactics can be the adoption of a Postmodern ERP mindset. Rather than leaving decision-makers to gather mission critical information from disparate, disconnected ‘shadow IT,’ Postmodern ERP ensures that a company’s IT solutions are all interoperable and interconnected, so that data from sales, production planning, transport operations, and all other functions are accessible to those who need them. As a result, predictions are based a more complete, data-driven picture of a given company’s operations. With Postmodern ERP in place that includes accessible forecasting processes, you’ve laid the groundwork for demand predictions that draw on a highly visible cache of informational resources. The past is instructive, and the more information at your disposal the better. At the same time, the value of an accurate forecast is that it helps keep disparate business functions within an organization on the same page about future expectations and areas for growth. To that end, it’s crucial to solicit buy-in from key stakeholders when developing forecasts. The input of various team members will help drive a more holistic understanding of the information being processed, as well as an increase in the likelihood that disparate teams will engage with and utilize the numbers being produced. This added degree of collaboration and team cohesion can act as a significant value added proposition, bolstering synergy and accuracy at the same time. In the era of modern manufacturing, data visibility can be leveraged for increasingly sophisticated workflows. To wit, E2E visibility is a necessary pre-requisite for integration with advanced analytics solutions. Prescriptive analytics in particular can have wide-ranging impacts across all supply chain touchpoints, enabling manufacturers, shippers, and freight forwarders to predict potential bottlenecks and slowdowns far in advance. The more complete a picture any given analytics solutions is able to get of one’s overall value chain, the more effectively it can forecast demand based on complex, interlocking factors while uncoveringareas of inefficiency or waste. Not only that, but advanced analytics can enable planners to create sandboxes for “what-if” scenarios, testing the potential effects of proposed supply stream changes. With increased visibility and big data analytics integration, it’s possible to gain much more value from demand forecasting workflows. One of the most crucial steps for extracting optimal value from your predictions, however, is to remember that they cannot and will not prevent every possible supply chain disruption. Supply chain planners must recognize the limitations of their predictions, and work towards building a supply chain that’s adaptable enough to weather bottlenecks and disruptions. Luckily, the increases in E2E visibility that drive improved forecast reliability can also help to create a more responsive supply chain. In particular, integration of real-time data into the planning process can enable sales and operations execution (S&OE) to manage day-to-day fluctuations from projected demand. In this way, it’s possible to keep mid-term plans on track even in the face of a volatile global supply chain.
No matter how sophisticated your methods, or how intimate your knowledge of the field, no demand or sales forecast will ever be 100% accurate. Just as supply chain disruptions are simply a fact of life in the world of manufacturing, deviation from a your expected outcomes are unavoidable. Given this state of affairs, you may be wondering if it’s worth expending resources on improving forecast quality. This feeling is understandable, but while there will always be a gap between expectations and reality, the rise of Industry 4.0 has improved our ability to predict future outcomes. With modern IT solutions and business processes, it’s possible to escape the past-oriented planning models of yesteryear (which fail to account for future developments) and drive towards a more future-oriented approach.1. Put Down the Pen and Paper
2. Adopt a Postmodern ERP Mindset
3. Get Buy-in from Key Stakeholders
4. Big Data Analytics Integration
5. Plan for Disruptions
FAQs
What are the five 5 steps of forecasting? ›
The major steps that should be addressed in forecasting include: Establishing the business need. Acquiring data. Building the forecasting model. Evaluating the results.
What are the five basic ways to manage demand? ›- Applying behavioural insight, and "nudge" techniques. ...
- Improving access and self-service options.
- Re-designing and co-designing service with customers.
- Developing the resilience of individuals and communities, and co-producing outcomes.
- Changing people's behaviour over the long-term.
Effective forecasting is not saying that this is what will happen, but what might happen based upon the likelihood of a series of events playing out in a specific way at a specific time under specific conditions – the more events you are having to take account of, the less likely any single outcome will occur.
How can forecasting improve efficiency? ›Forecasting allows businesses set reasonable and measurable goals based on current and historical data. Having accurate data and statistics to analyze helps businesses to decide what amount of change, growth or improvement will be determined as a success.
What are the 6 steps to forecasting? ›The following slide highlights the six steps of business forecasting process illustrating key headings which includes problem identification, information collection, preliminary analysis, forecasting model, data analysis and performance review.
What are the 4 basic forecasting method? ›While there are a wide range of frequently used quantitative budget forecasting tools, in this article we focus on four main methods: (1) straight-line, (2) moving average, (3) simple linear regression and (4) multiple linear regression.
What are the 4 basic types of forecasting? ›- Time series model.
- Econometric model.
- Judgmental forecasting model.
- The Delphi method.
Demand forecasting allows manufacturing companies to gain insight into what their consumer needs through a variety of forecasting methods. These methods include: predictive analysis, conjoint analysis, client intent surveys, and the Delphi Method of forecasting.
What are the 5 types of demand? ›- i. Individual and Market Demand: ...
- ii. Organization and Industry Demand: ...
- iii. Autonomous and Derived Demand: ...
- iv. Demand for Perishable and Durable Goods: ...
- v. Short-term and Long-term Demand:
- Product portfolio management. Effective demand management requires a comprehensive understanding of products and their respective lifecycles. ...
- Statistical forecasting. ...
- Demand sensing. ...
- Trade promotion management.
What is a most successful forecasting method? ›
Multivariable Analysis Forecasting
Incorporating various factors from other forecasting techniques like sales cycle length, individual rep performance, and opportunity stage probability, Multivariable Analysis is the most sophisticated and accurate forecasting method.
A firm considers various factors, such as changes in income, consumer's tastes and preferences, technology, and competitive strategies, while forecasting demand for its products.
What are the elements of good forecasting? ›-The forecast should be timely. -The forecast should be accurate. -The forecast should be reliable. -The forecast should be expressed in meaningful units.
What are the 5 benefits of forecasting? ›Demand forecasting also helps reduce risks and make better financial decisions that increase profit margins, cash flow, improve resource allocation, and create more opportunities for growth.
Why is forecasting improved? ›More extensive observations, much faster numerical prediction models, and vastly improved methods of assimilating observations into models led to the improvement. As more advanced weather satellites come online and faster supercomputers are used to crunch weather data, forecasting will grow even more accurate.
How can forecasting problems be solved? ›- Forecast demand frequently and for short periods.
- Understand which steps of production you can stagger for flexibility.
- Have a plan for responding to expected variations.
- Have a plan for handling extreme variations and edge cases.
There are three basic types—qualitative techniques, time series analysis and projection, and causal models.
What are the 7 steps in a forecasting system? ›- Determine what the forecast is for.
- Select the items for the forecast.
- Select the time horizon. Interested in learning more? ...
- Select the forecast model type.
- Gather data to be input into the model.
- Make the forecast.
- Verify and implement the results.
- List out the goods and services you sell.
- Estimate how much of each you expect to sell.
- Define the unit price or dollar value of each good or service sold.
- Multiply the number sold by the price.
- Determine how much it will cost to produce and sell each good or service.
There are two types of forecasting methods: qualitative and quantitative.
What can be done to reduce forecasting errors? ›
The simplest way to reduce forecast error is to base demand planning on actual usage data vs. historical sales. The difference: Usage reflects actual consumption of an item. In other words, just because a product was sold to a customer doesn't mean that product was used.
How the demand forecasting can improve production and productivity? ›Demand forecasting is the primary tool for manufacturers to accurately determine the optimal supply rate and build adequate resources accordingly, henceforth, minimise expenses. Furthermore, it enables the collaboration between outbound and inbound process of the manufacturing process, such as sales and production.
What are the four components of effective demand forecasts? ›Preparing data for analysis; Measuring data currency, coverage and accuracy; Understanding how order fulfilment impacts your forecasts; and. Managing spikes in the data that may or may not be real demand.
What is the most important thing in demand forecasting? ›One of the most impactful factors is price, because customers are likely to demand different quantities of a good or service as the price goes up or down. Forecasters should have the most and best information about these factors, because they're decisions made by the company.
What are three 3 types of forecasting errors that could occur? ›- Error Caused by Data Problems. Wrong data produce wrong forecasts. ...
- Error Caused by the Wrong Forecasting Method. ...
- Error Caused by Flaws in the Forecasting Process.
There is probably an infinite number of forecast accuracy metrics, but most of them are variations of the following three: forecast bias, mean average deviation (MAD), and mean average percentage error (MAPE).
What are the problems faced in demand forecasting? ›Demand forecasts often lack sufficient historical sales data for analysis especially when it comes to newer products. Those often don't offer enough historical sales data. Therefore forecasting new products is complex and causes problems like e.g. poor inventory management, which leads to higher inventory costs.
What are demand forecasting techniques? ›There are two methods in which demand forecasting can be done i.e (A) Survey Methods and (B) Statistical Methods. In the market research technique, consumer-specific survey forms are sent out in tabular format to get insights that an organization can't get from internal sales.
How can forecasting improve a company's operations? ›For small business owners, forecasting is the process of looking at past and present data, as well as marketplace trends, to predict the company's future financial performance. It enables you to gauge how much revenue you'll potentially earn in a particular period and plan for big expenses.