The U.S. economy is expected to remain on a positive growth trajectory to achieve the longest recovery cycle in history, surpassing the 120-month expansion of 1991- 2001. The slow but steady economic growth has produced a lasting imprint in the psyche of both employers and workers that, in part, explains tamed wage and price inflation. The unemployment rate stands at 3.8 percent in February, a rate last seen in 1966. The broader measure of unemployment (U6) that includes discouraged and part-time workers stands at 7.3 percent in February, a sharp drop from 8.1 in January.
Time series forward-looking GDP predictive analytics highlight the risks to the near-term outlook for turning points in the global growth cycle. Evidence from the first quarter of 2019, modeled from real-time data, combined with business executives’ expectations up to the third quarter of 2019, provide a rolling four-quarter assessment of the future path of GDP. Compared to the official GDP data for the third quarter of 2018 published by national statistics agencies around the world, e-forecasting. com’s GDP predictive analytics foresee a high risk of substantial global economic slowdown in 2019.
All too often, skilled Demand Planners and Forecasters fail to convey the value of their insight to key stakeholders, failing to get buy-in and establishing demand planning as a key business tool. Demand Planners often make the mistake of thinking that the value-proposition of forecasting is self-evident, when in fact it must be clearly explained. Without explaining the “why,” stakeholder buy-in and executive sponsorship will remain elusive. Here I explain how to reframe our own perception of forecasting to change the way others perceive us and our field and, critically, how we must embrace the change management element of our roles if we are to ensure forecasting becomes integrated into our businesses.
The S&OP process is one of the key decision-making forums for an organization. The conflicting objectives from various departments can prove to be a challenge, especially when seeking alignment on decisions at the Executive S&OP meeting. This article provides insight into stronger decision-making methods to support the financial and strategic goals of an organization.
Accurate forecasts are needed to support the success of new product launches so that Sales can make decisions regarding sales support materials and training, and so Finance can make decisions surrounding corporate budgets and financial expectations. In the absence of historical data, companies typically over-project sales volumes for new products to avoid back orders in the case of sales exceeding projections. This article discusses how advanced analytics and machine learning have shown significant improvements in new product forecasting, analyzing unstructured data to respond quickly to market changes and consumer acceptance, thereby improving the success rate of new product launches.
If you are looking to take your Demand Planning career to the next level, gaining influence is critical to success. Achieving influence will move you from just having a seat at the table to having a voice at the table. In this article, I will outline six tactics you can use to cultivate this skill. These strategies will combine analytical, communication and relationship building skills.
Since 2013 I’ve been writing a series of articles in my Insights column, in Supply Chain Management Review (SCMR) magazine, about e-tailing. It chronicles the evolution of e-commerce vendors—mostly aboutmarket-leader Amazon - as they’ve captured more of the retail business from the traditional brick-and-mortar retailers, largely market-leader Walmart. This column draws from the latest in the SCMR series because it focuses on ‘successful’ inventory management planning, buying, and fulfillment processes. In short, Amazon excels in operating a ‘responsive’ supply chain, while Walmart’s success is as an ‘efficient’ one. In both cases, their strengths become their weaknesses when competing for the other’s core business.
Customer service leaders have direct access to a wealth of demand impacting information. They know more than anyone else how customers are behaving and any upcoming events or issues that will impact demand for any given period. The information they hold alters the shape of your demand curve, your supply requirements, financial pacing, and fill levels. Why then is Customer Service seldom included in the S&OP process? This article discusses what kind of customer information they have access to and how you can leverage it for more accurate forecasts and demand plans.
This article discusses the important AI subset of Machine Learning and its application to the area of supply chain planning and optimization. It defines machine learning and how it relates to other advanced analytic methods including AI; predictive, prescriptive and cognitive analytics; algorithmic optimization; and cognitive computing. It also provides a list of potential improvement areas to help practitioners build a business case for machine learning to secure the investment required to benefit from this game-changing technology.
We are currently working on improving our forecast accuracy. One of the ideas is to create a separate group of customers with low volume. While it improves the forecast, my concern is the visibility we lose at the lower level downstream. For procurement planning, we need customer level forecasts because each customer gets its own labels. Any idea on how that can work? Has anyone else used this method and how did they make it work?