IBF’s recent benchmarking report, Benchmarking New Products Forecasting and Planning, revealed a continuation in the major headwinds facing companies today. The shift in consumer demand has forced companies to innovate and release new products like never before, with this category now making up 17% of all products. With this comes in creased costs, shorter planning horizons, and greater forecast error. Combine this with increased volatility and the complexity of plan-ning for multiple channels, the role of demand planner is arguably the hardest it has ever been. In spite of these unprecedented challenges, two-thirds of companies report operating margins and revenue growth of 15% or more. The question is, what tools and methodologies are behind this remarkable growth, and how can they be translated into greater efficiency and profitability?
Every professional intuitively knows that Sales and Operations Planning/Integrated Business Planning (S&OP/IBP) are struggling to show the results they signed up for 30 years ago when the process first emerged. It may not be that the process is broken; rather, it is stuck for many organizations trying to keep up with the new business environment of today. By rethinking the people, process, technology, and mindset, you can transform a business process that may keep up with the business landscape of tomorrow. In this article, we will discuss the evolution of S&OP/IBP to an evaluated business process, Business Efficiency Planning (BEP), to help focus an organization, and achieve measurable results.
The U.S. economy is on its longest cycle of post trough recovery and is continuing to grow both in employment and output. A recently released report by the U.S. Census Bureau shows a 5-percent boost in inflation-adjusted median income in 2016, following a 3.2-percent increase in 2015. The middle-class household income is approaching the 2007 pre-Great Recession level. The female head of households experienced the largest jump in income by 7 percent.
Analytics-driven forecasting means more than measuring trend and seasonality. It includes all categories of methods including more advanced methods, such as artificial intelligence, but not necessarily deep learning algorithms. Expert systems are an integral part of artificial intelligence, also known as вЂњsymbolic artificial intelligenceвЂќ (AI), which is designed to think like a human. AI is differentiated from вЂњsub-symbolic artificial intelligence,вЂќ which includes the newly revived research area of neural networks and deep learning. Even with all the buzz about AI and machine learning (ML), not many are using either for demand forecasting, if they do, itвЂ™s on a one-off basis, not on a large scale across the entire business product hierarchy. In fact, many companies feel that AI and ML is the new demand forecasting вЂњeasy button,вЂќ which I remind them only works in those Staples commercials. You still need data scientists to monitor and tweak models using analytics-driven methods to make corrections if something dramatically changes. And demand planners are needed to own and manage the demand planning process.
Over the past 10 years, I’ve noticed a growing dialogue around the subject of forecastability. It seems to be one of the hot topics these days, and represents what some believe to be futuristic thinking—the next step-change advancement—in the field of demand planning. I recently attended the latest APICS/IBF Best of the Best S&OP conference where the topic surfaced again in side conversations and hallways. I have always been hesitant to comment in such moments, because I believe the exercise of understanding and learning about demand attributes is worthwhile regardless of whether or not I agree with the wisdom of a concept. For some reason, this time I came away with a different perspective—I wondered whether it was time to answer the question that has been on my mind for a while now: Is forecastability a real thing?
The BASF Group's Chemical Intermediates Division develop a portfolio of about 700 intermediate products around the world. Its most important product groups include amines, diols, polyalcohols, acids, and specialties. Intermediates are used as starting materials for coatings, plastics, pharmaceuticals, textiles, detergents, and crop protectants.
This column discusses a lesson to be learned from Sears of long ago when it was a bi-channel retailer. The company successfully operated its legacy catalog business in conjunction with store operations as two separate supply chains. The column also discusses that many service arms of durable-product companies typically do the same in order to balance short-term sales versus long-term service-customer satisfaction. Forecasters and planners finding themselves in these situations may need to support multiple ‘efficient’ versus ‘responsive’ supply chains.
What can one do when forecast accuracy is good at an aggregate level, but deteriorates at a disaggregated level? Why is the S&OP process run by the Demand Planning group and not by the Supply Planning group? Our company uses bias as a measure of forecast performance instead of WMAPE. Is it wrong to do that? And many other questions to answer by Chaman L. Jain.
The U.S. economy remains on a steady and gradual growth trajectory, and appears to be decoupling from Washington’s politics. The combination of steady job growth and low inflation are contributing to the strength of the equity market. The Fed is conducting itself predictably with a data-based gradual and cautious interest rate normalization strategy. Both the business community and consumers have lost their taste for bubbles, preferring stability and predictability in their decision-making.
Every so often, a dialogue will start about the traits that make a good S&OP leader. Some folks will offer specific educational requirements, while others will discuss job experience. Still others will define softer skills such as persuasion and likability, or even effective communication skills. I am not sure there is a specific skill set required, although there are many traits that S&OP leaders have in common.
Why should a company consider forecastability when applying forecast methods? Doesn’t a company’s demand forecasting system conduct automatic diagnostics and apply the appropriate forecasting method? Treating every data the same way may decrease the accuracy of the forecasts, as you might apply only one method across the product portfolio, not realizing that each group of products has different data patterns, based on how they were sold and supported over the product’s life cycle. It is also important to educate senior managers in the company on forecast accuracy expectations based on data availability, value set by the company, and method chosen.
This column discusses outsourcing a business forecasting process to external consultants. It starts by covering the functions that might be reasonably outsourced. It recommends that all collaborative-related functions reside inside the walls of a company, while quantitative and computer system capabilities might be outsourced. However, there are risks involved in having demand forecasts reside outside the protection of a company’s firewalls.
Despite financial markets’ euphoria about the future, global economic growth remained lethargic in the first half of 2017, and remained in the familiar “normal” speed of the current recovery. Our forecast for growth in 2017 was revised downwards to 3.2%, nearly the same as in 2016. A new feature in this phase of the global business cycle is the re-emergence of inflation, despite stable-to-falling energy prices. It is expected that global inflation this year will increase to 6.3% from 3.7% in 2016.
An important undertaking for any company to serve its customers is to determine how much inventory is necessary to satisfy customer demand. This article reviews some of the key supply and demand elements influencing this decision. It explores these in the context of their expected levels and the importance of considering variation around their expected levels. It further evaluates how well a widely used metric affecting the inventory decision actually affects the customer and the company—days of inventory on hand and or inventory turns.
What is the difference between demand planning and demand forecasting? To whom should the demand forecasting function report within an organization? How can input from social media help in demand planning? And many other questions to answer by Chaman L. Jain.
Several weeks into the new administration, a new sense of optimism about future economic growth pushed the equity market into a new high. The Fed has hinted strongly on plans to hike the interest rate as often as necessary in 2017 to thwart overheating of the economy. The question is whether to expect inflation or reflation in light of the glut in the energy market as well as the considerable time that it will take for infrastructure spending to translate in higher employment and income. The market remains uncertain as to how to process risks and rewards associated with the much-anticipated trade restrictions on real output, employment, and inflation. It is much easier to remove or reduce regulations on producers or implement tax cuts, while identifying and funding shovel-ready infrastructure projects with a significant impact on demand for workers and material is substantially time-consuming. The proposed immigration policies will adversely affect many sectors of the economy, including construction, food services, agriculture, and retail.