The purpose of demand planning is to improve forecast accuracy and increase supply chain effectiveness. Using current and past forecast performance, demand planners can provide guidance on what adjustments are needed to improve future forecast accuracy. The next step in developing an effective demand planning process is to make the future forecast more forecastable, that is, to take actions that make it more likely that the forecast will be accurate. While there will always be factors outside our control that will influence forecast reliability, there are actions we can take to increase the odds that our forecasts will be accurate. In taking these actions, we move from merely planning demand to managing demand.
Most forecasting approaches are based on the idea that a forecast can be improved if the underlying factors are identified then forecasted separately. It stands to reason, then, that with conventional time series approaches that extract information from historical observations, we may gain insights by isolating and modeling elements like seasonality. In this article, I explore how transforming historical data with multi-cluster time series analysis allows us to gather additional information about seasonality. I discuss a newly introduced modelling approach that combines information from many different clusters of attributes and levels of aggregation for improved seasonal profiles, and consequently allows us to build more robust forecasts.
The second machine age is allowing us to understand and shape our environments using computers and other digital advancements. We’re now seeing unsupervised learning systems that learn faster, require less data, and achieve impressive performance. These supervised and unsupervised intelligent automation techniques can drive automation and enrich their domain experts — not replace them by helping them work more effectively. Intelligent automation driven by Artificial Intelligence (AI) and machine learning (ML) are disrupting the way companies do business. The rapid deployment of automation is helping us set new standards of efficiency, speed, and functionality. Intelligent automation will help demand planners to sense and synthesize vast amounts of information boosting the FVA process guiding demand planners with surgical precision to work smarter.
This article describes the various steps that General Motors has taken to improve forecasts for better decision making. These include reducing waste, using consistent methods and data each time to see what is working and what is not, employing more statistical tools and less judgment, and encouraging employees to share their learning with others. The forecasting team was also encouraged to constructively challenge analysis rather than accept anything at face value. Above all, the initiatives created a culture where senior leaders became champions of our forecasting efforts.
Many organizations have a Sales & Operations Planning process in place but face the challenge of maintaining the compliance and active engagement of participants within the process. Visibility and formal review of the processes within S&OP is critical to strengthen the value that S&OP brings to the organization. This article provides insight to the tools and initiatives that can be implemented to improve and sustain the S&OP process.
Cloud computing is slowly but surely establishing itself as the preferred platform and trusted IT operating model. It has both pros and cons, though the advantages far exceed the drawbacks. Through public clouds, organizations can significantly reduce upfront investments in IT, increase and decrease capacity according to their needs and pay only for what they use, and centralize data and systems which can be accessed from anywhere in the world. This will also enhance demand and supply chain planning by offering a platform that can quickly store, process and analyze large amounts of data (both structured and unstructured) to gain insight. However, it is not without risks, which include security, loss of control, and dependencies on cloud providers.
Predictive analytics for capital expenditures in the first half of the year confirm an ongoing renaissance of investment that begun two years ago with structural changes in the political climate favoring more entrepreneurship and less regulation. Recently published evidence from a 120-country survey of business executives show that in the second quarter of 2018, the investment boom continued to accelerate around the globe. e-forecasting.com’s forward-looking CAPEX predictive analytics provide solid evidence that the global economy will stay on its growth phase of the business cycle over the next two years.
Recent changes in Big Data and analytics are having a huge impact on the culture and organizational structure of demand planning, forecasting and related roles. Whilst most companies have recognized this, we still need a blueprint of what this new organization may look like and how we get there. This article discusses a new way to optimize the skills and responsibilities of these specialized functions and the steps you need to take based on your organization’s maturity. Additionally, for professionals who work in these fields or who are about to enter, this article discusses the types of career paths that can help maximize abilities and fulfil aspirations.
In the late 1960s and early 1970s, Kahneman and Tversky carried out a series of breakthrough studies that gave rise to the field of behavioral economics, and changed how judgment should be viewed. Few of us are aware of the significance of Kahneman and Tversky’s findings, namely that human judgment is flawed and, in terms of making predictions about the future, mathematics tends to outperform human judgment close to 100 percent of the time. They used data and analytics to find true patterns in human behavior that replace false assumptions in a number of areas of study. Similarly, demand analysts and planners need to find the true patterns in human judgment to replace the false ones that govern our demand forecasts and plans.
In my last column, I discussed the importance of sales forecasting at industrial product companies. Since sales organizations are the most important organization in creating and shaping customer demand, I advised managers in these companies to get more involved with sales managers during the demand forecasting and planning process. I briefly mentioned that integrating information from CRM systems would provide useful insight for improving accuracy as well as sales productivity. In this column I discuss how three components of CRM systems might improve these processes for both industrial and consumer product companies.