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. Evangelos Otto Simos is head of predictive intelligence at Kefallonia, Inc., a private research and consulting firm, and professor at the University of New Hampshire. This report does not purport to be a complete description of global economic conditions and financial markets. Neither the Journal nor Kefallonia, 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 Kefallonia, Inc. or any organization that the author may be associated with. You can contact Evangelos at email@example.com
Successful forecasting is about more than just extrapolating historical sales data.
Qualitative information from our colleagues, particularly in Sales and Marketing, are highly valuable in improving forecast accuracy. Getting this valuable information, however, is often a challenge. In this article I present methodologies for building strong and genuine connections with your Sales team by using the language they use and metrics relevant to them. I explore how salespeople think, with useful activities that tap into their competitive nature to get them to engage, and what it takes to
get them to trust you and consider you one of their own. I reveal how, when they consider you a trusted ally, they will provide valuable information that helps you drive real business value
Assuming a new role as a Demand Planner, whether it is your first job or a new job at a
different company, presents a steep learning curve, and it is crucial to have a plan for building the relationships and acquiring the knowledge that will allow you to succeed. In this article I discuss how success in a demand planning role requires a deep understanding of the product line, forging relationships with key stakeholders, and learning how to communicate the forecast to executives. I dive into practical ways to build trust with sales and marketing and other business partners to
facilitate the S&OP process, how to communicate with each department and executives using their own language, and the questions you should be asking in your first six months to truly understand demand for each product and the underlying demand drivers.
This article describes Covid-19’s profound impact on the overall retail market. Essentially, the
strongest e-tailers got stronger, and the weak ones got weaker. I discuss how food shortages played a key role whereby successful brick-and-mortar retailers were able to co-fulfill grocery pickup and delivery orders with online orders for household goods, especially cooking implements as more cooked at home. Given retailers are the leading industry in integrating omnichannel businesses, we in other industries have much to learn. This article reveals how companies have survived and thrived during the pandemic so that we may learn from their example and improve our own supply chain planning and order fulfillment.
Walmart’s new OTIF requirements that were announced in September 2020 pose real challenges for supply chains. These new rules are forcing organizations to improve operations to avoid fines for failing to meet the new 98% compliance threshold. I asked several supply chain leaders how they are reacting to these harsh new metrics. Those insights, combined with my own experience, are detailed in this article, complete with practical tips to improve your own OTIF metrics. Key responses include Root Cause Analysis to identify corrective actions, making the order process “lean”, revisiting your logistics strategy, and inventory optimization.
Integrating demand and supply for optimal supply responses is the core objective of S&OP. Designing and implementing such a process is a challenge, however—even for the most experienced planning practitioner.
Understanding the strengths that facilitate implementation and weaknesses that act as barriers is key to building an effective and sustainable process. In this article I reveal how SWOT analysis assists with this, and, when combined with outputs from an S&OP maturity model, helps build an extremely effective roadmap for implementation, complete with actionable insight.
Supply chain disruptions caused by Covid-19 created real challenges for retailers and consumer goods companies in meeting demand for essential products. It revealed serious weaknesses in the traditional
ERP platforms and on-premise systems that hampered critical supply responses. In this article, I reveal how, if retailers had invested in a Cloud-native supply chain solution, they could have easily scaled up their computing capacity to allow for the analysis, monitoring, tracking, and decision making required to locate product inventory at any point in the supply chain and efficiently expedite it to meet local consumer demand. I describe how moving on-premise enterprise operations to the Cloud allows us to obtain the transparency, scalability, flexibility, and analytical power needed to respond to whatever crisis
Anyone who has implemented or been part of an S&OP process knows that the further along we are on the S&OP maturity curve, the more wide-ranging its benefits. Many organizations can get started with a
process that delivers value—the challenge comes in sustaining that process and progressing it along the maturity curve towards a Vanguard process. In this article I draw on my experience implementing and leading S&OP at multinational brands to reveal the factors that act as barriers to progressing to the next level of maturity and what can be done to mitigate their impact, thereby allowing us to evolve and become best-in-class.
When a black swan event occurs, our demand assumptions are suddenly thrown out the window and our historical data will no longer help generate accurate forecasts. That is challenging enough to deal with when you are in the eye of the storm, as forecasters have experienced during Covid-19. But when the dust settles, what do we do with that historical data that contains multiple outliers? In this article I reveal how to treat this data, so it doesn’t provide false demand signals for the coming periods. The methods we have at our disposal include pruning or smoothing, causal models and adjusting parameters, and manual, qualitative overrides.
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. Evangelos Otto Simos is head of predictive intelligence at Kefallonia, Inc., a private research and consulting firm, and professor at the University of New Hampshire. This report does not purport to be a complete description of global economic conditions and financial markets. Neither the Journal nor Kefallonia, 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 Kefallonia, Inc. or any organization that the author may be associated with. You can contact Evangelos at firstname.lastname@example.org
In the current pandemic environment, many assumptions have changed and forecasting
and planning have become harder. It is a given that, as the environment around you changes, so should your approach to
planning. This does not, however, mean that you need to reinvent proven S&OP techniques and processes; rather, you should
understand what outputs have changed and trace these back to a changed correlation in inputs. Value Chain Management
—where we take a holistic view of the entire product lifecycle, combining demand and supply planning and supporting them
with S&OP—is our foundation for successfully navigating supply/demand shocks. This article will reveal how such a process is
well-placed to react quickly and effectively in such circumstances and, when complimented by a three-step framework that
considers input fidelity, S&OP frequency, and changed latency of levers and constraints, we can manage the unprecedented
disruptions caused by Covid-19.
This article deals with the difference between demand planning under uncertainty versus demand
planning under risk. It aims to define both and argues that demand planning under uncertainty means a planner knows
the outcomes that might happen after they make planning decisions; yet have no knowledge of the probabilities of those
outcomes. In the case of demand planning under risk, a planner knows both the outcomes and their probabilities. It also
presents a payoff matrix, to facilitate our understanding of different outcomes for a given decision, which should help us to
develop optimal responses to Covid-19 demand disruptions.
Research indicates that the winners and losers following a recession are differentiated
by the level of investment they make during the downturn. Here I present five strategies, that, while requiring capital
expenditure, will enable companies to better balance supply and demand during the current Covid-19 environment, and
emerge much stronger when Covid-19 abates and the economy returns to steady growth. I discuss the importance of proper
data management to forecast effectively during periods of extreme demand volatility; the value of a digital twin to analyze
trade-offs and what-if scenarios; demand sensing to enable quicker supply responses; integrating IBP to act on the latest data
and tie tactical plans to strategy; and Multi-Echelon Inventory Optimization to free up much needed cash.
Much of the difficulty we are currently experiencing in trying to balance supply and
demand is due to the bullwhip effect, whereby even slight changes downstream in consumer demand have very drastic
impacts upstream on supply. During times like these when demand continues to shift in ways that are difficult to anticipate,
the bullwhip effect is more pronounced than ever, and, for many companies without established demand planning processes,
it is wreaking havoc on our supply chains and our ability to meet consumer demand. At the core of this problem is ineffective
demand forecasting, with many companies forecasting orders/shipments instead of the real demand signal. Here I detail 5
practical responses that mitigate the causes of the bullwhip effect and will help us navigate the extreme uncertainty relating