5 Ways to Improve Your Demand Forecasting
Brian Hoey - April 03, 2018
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
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.
2. Adopt a Postmodern ERP Mindset
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.
3. Get Buy-in from Key Stakeholders
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.
4. Big Data Analytics Integration
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.
5. Plan for Disruptions
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.