Since the e-commerce market is growing by leaps and bounds, companies need a
dedicated team within the S&OP process to take advantage of it. It is a different animal and requires a different skillset and
strategy to manage and grow demand. It presents major challenges and has already damaged brick-and-mortar retailers,
as well as a host of different industries—publishing being a prime example. At the same time, it has provided many new
opportunities to grow demand via target and subscription-based marketing.
The success of an S&OP process depends heavily on the behavior of its participants.
Unfortunately, the cross-functional teams required for S&OP often exhibit some form of dysfunctional behavior, resulting in
poor performance and reduced faith in the process. However, by setting behavioral expectations and refining the process,
the effect of these dysfunctional behaviors can be mitigated and reversed, enabling higher levels of performance.
This article deals with the differences between ‘responsible’ versus ‘ethical’ supply chains. It
is meant to be controversial. I believe that businesses need to address the seemingly uncivil public culture we are experiencing
today, in order to ensure that plans, forecasts and decisions are made collaboratively, as well as consensus-based. We have started
to see instances in which employees’ so-called ‘ethical’ opinions about politics and value-systems have fostered dysfunction in
some high-profile workplaces. It is important for management to address these issues as they arise, so that ‘responsible’ decisions
are made in the interest of the whole company.
It seems intuitively obvious that the companies who figure out how to best engage with
their consumers will get more than their fair share of growth. As a result, integrating consumer demand into the demand
forecasting and planning process to improve shipment (supply) forecasts has become a high priority for many companies.
Most supply chain professionals are quickly realizing that their supply chain planning solutions have not driven down costs
and have not reduced inventories or speed to market. Consumption-based modeling using a process called, “multi-tiered
causal analysis” (MTCA) which links consumer demand to supply (downstream data to upstream data), using a process of
nesting advanced analytical models. Although this process is not new in concept, it is new in practice. Consumption-based
forecasting using the MTCA approach is a simple process that links a series of causal models through a common element
(consumer demand) to model the push/pull (sell in/ sell out) effects of the supply chain. It is truly a decision support system
that is designed to integrate statistical analysis with downstream (POS and/or syndicated scanner) and upstream (shipment)
data to analyze the business from a holistic supply chain perspective.
Sir Ian Davis is the Chairman of Rolls-Royce Holdings-the world’s second largest manufacturer of aircraft engines-and is a non-executive director at Johnson & Johnson and BP. Ian spent much of his career in management consulting, joining McKinsey & Co. in 1979 and becoming Managing Director in 2003. He was knighted in 2019 for services to business.
Safety stocks are a crucial part of meeting customer demand. They are an important buffer
that protects businesses from unexpected spikes in demand, allowing companies to have inventory on hand to deliver to the
customer without waiting for (often lengthy) production lead times. Inventory, however, is expensive, and holding excess stock
puts companies in a position where inventory may become obsolete. It is crucial, therefore, that demand management and
inventory management professionals strive to protect their company’s bottom line and cash flow by finding the right balance.
Here I present effective safety stock calculation methods that, supported by forecasting and S&OP, can effectively reconcile the
need for high customer fulfillment rates with the need to minimize costs.
Dr. Eric Siegel is the founding chair of the Predictive Analytics World conferences series, is the author of the award-winning book Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, and is the host of the Dr. Data Show on YouTube. He is a former Columbia University professor and has become a defender of civil liberties in the age of Big Data. He is also the editor of The Predictive Analytics Times.
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.
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.
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?
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.
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.
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.
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.
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.