Dr. Nahavandi is Associate Professor of economics at Pfeiffer University School of Graduate Studies, specializing in Business Economics, International Business, and Healthcare Economics. The information in these forecasts is gathered by the Journal from sources it considers reliable. Neither the Journal nor the individual institutions providing the data guarantee accuracy, nor do they claim that use of the data appearing herein will enhance the business or investment performance of companies or individuals who use them.
Dr. Simos is Director of Forecasting and Predictive Analytics at e-forecasting.com, a division of Infometrica’s Data Center, 65 Newmarket Road, Durham, NH 03824, U.S.A. and professor of economics at Paul College, University of New Hampshire, www.infometrica.com, email@example.com. This report does not purport to be a complete description of global economic conditions and financial markets. Neither the Journal nor Infometrica, Inc. guarantee the accuracy of the projections, nor do they warrant in any way that the use of information or data appearing herein will enhance operational or investment performance of individuals or companies who use it. The views presented here are those of the author, and in no way represent the views, analysis, or models of Infometrica, Inc. or any organization that the author may be associated with.
Programmatic advertising allows companies to advertise to target markets and understand demand drivers and consumer behavior like never before. What’s more, this kind of advertising allows demand planners to shape demand in near real-time. By connecting programmatic advertising with demand and supply planning, we can use it to boost demand at will. It represents the next stage of demand planning and analytics, and offers forward-thinking companies the opportunity to gain a competitive advantage in the years to come.
This article asserts that standard integrated planning implementation requires three generic templates: a process model, a transformation framework, and an information systems roadmap. It makes the case for S&OP and IBP program managers to tailor these templates to their own organizations in order to assess current capabilities and guide the next phase of their business transformation. The results of implementation are improvements in financial results and key performance indicators.
This column is a modified version of an article I wrote, “Sales and Operations Planning (S&OP) Mindsets,” published in the JBF back in April 2007. While the content is largely the same as the one written 12 years ago, it is probably even more relevant today. Back then, S&OP was not as widely used as it is now but many of the challenges of running a well-functioning S&OP process remain the same. The major recommendation here is that one should establish clearly defined roles for various functional managers on an S&OP team—ones that are based on their psychologies or mindsets. I present a framework for understanding different stakeholders’ personalities in the S&OP meeting that can foster collaboration and help in achieving consensus.
The digital economy refers to an economy that is based on digital computing technologies where business is conducted through online and mobile devices using the internet-of-things (IoT). In the digital economy, value is created through the technology-enabled links between people, machines, channels and organizations. All this is giving rise to an awareness and willingness to apply analytics to everything, not just to strategic initiatives, but to day-today tasks. Advanced analytics aided by machine learning algorithms will automate the repetitive work demand planners do regarding managing data and information as well as uncovering key insights allowing them to work smarter and more efficiently. As such, digitalization of the supply chain will require companies to manage product replenishment based on actual consumption rather than transactions.
There’s a lot of excitement lately about AI, new models, and machine learning algorithms and the accompanying idea that they will replace all human judgement. This misconception may be due to lack of understanding about how all the tools and methods now available fit together, and how we need all of them if we’re to forecast all datasets accurately. In this article we will look at the full spectrum of forecasting methods from pure judgment to machine learning, and classify each of them so that they are easy to understand. I also provide an explanation of each of the broader classes of methods, so demand planners can add different models to their toolkit, knowing when to use which one for maximum effect.
As the Federal Reserve vacillates between tightening and loosening monetary policy, signals from economists suggest that we are approaching the end of the current economic cycle and that a recession is likely approaching. When the slowdown will happen remains unclear, but a recession is a meaningful event for most organizations, which necessitates that we, as demand planning and S&OP leaders, prepare for the inevitable impact on our businesses. Recessionary times can lead to many changes in consumer behavior such as shifting to different and lower grade products, inventory contractions, as well as lower retailer and consumer acceptance of new products. This article hopes to present practical ways to prepare before a recession starts, including ways to identify what specific economic indicators affect which products, using predictive analytics and econometric data to correlate your demand curve with different severities of economic downturn, and digging into your own company’s data to understand how your portfolio fared in previous downturns.
How would you plan for a supply chain with the following characteristics: API (active pharmaceutical ingredient) manufacturing with 9-month firm forecast requirement. API Product is sent to CMO (Contract Manufacturing Organization) with 3-month firm forecast requirement. Final product is then sent to 3PL who also requires a 3-month firm forecast. How should demand planning consider all these different requirements from the point of view of the supply chain?
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.