1) The document summarizes discussions from a demand planning peer group meeting about challenges with demand forecasting and opportunities to improve processes. 2) Key topics included how to forecast demand for new products, coordinating marketing and sales to shape demand, using different supply chain strategies depending on a product's lifecycle phase, and balancing investments in demand forecasting versus supply chain agility. 3) The group was particularly interested in advancing both the "art" and "science" of demand forecasting, evaluating the value of more accurate forecasts, and determining whether to focus on forecasting improvements or product/supply chain designs that reduce the need for accuracy.

1. 1
2018 Demand Planning Peer Group
SCLA 2018
4 June
After introductions, we went around the table so each participant could get his/her most
interesting challenge out on the table. We decided to have a single peer group. Initial topics of
interest to the group:
1. New product introduction – how to forecast demand?
2. Timing of new product launch – what is the impact on current product and on
competitors’ products?
3. What are the marketing and sales implications? How can marketing and sales help
shape demand? What should the incentives for marketing and sales be? How to best
coordinate marketing and sales with the supply chain and supplier base?
4. More generally, what are tools and techniques for demand shaping?
5. How to insure demand is shaped and forecasted in order to eliminate obsolescence and
stock outs?
6. How does the supplier base serve as a constraint to satisfying demand? For example,
how can a powerful supplier (e.g., Intel) affect product demand and supply?
5 June
We began by summarizing the above points and then broadened the context of the discussion
to include impact of product and supply chain design on the need for demand forecast
accuracy.
Assume the phases of a product are: new product, product on the market for long enough to
better understand demand, product phase out, product after market. Perhaps each phase
needs a different product and supply chain design and different supplier requirements. For
example, auto is contemplating moving toward a completing pull supply chain with additive
manufacturing for after market parts.
An alternative to focusing on more accurate demand forecasting: design the product for
postponement and reduce the need for highly accurate demand forecasts. Requires significant
reduction in lead times and a more flexible/agile supply chain. Loss of economies of scale, but
reduction in obsolescence and stock outs. Which is better? Depends on the product
architecture (modular versus integrated).
A fact: more accurate demand forecasts may not create improved productivity. Sometimes
more forecast accuracy can reduce productivity (a counterintuitive and little known fact).
Demand may vary geographically and trend differently in different parts of the market, which
suggests the need to reposition/transship WIP and/or finished products based on demand data.

2. 2
What are the tools for the future for improved demand forecasting: blending longer term
forecasts? Other? Need for natural language processing, blending data and natural language,
CRM systems, impact of the sales teamon demand shaping and concomitant incentives.
Correlation for better demand forecasts – the need for a literature search on demand
forecasting for new product launches. The need for tools and techniques for new product
launches where information about the new product is limited but is correlated with well
understood correlates (e.g., macro-economic data).
The art and science of demand forecasting:
The science: new data analysis tools (e.g., AI, Machine Learning), better understanding of
correlations, etc.
The art: how to best incentivize sales and marketing to help shape demand. Coordination with
sales and marketing.
Correlations: weather, season, price, macro-economic conditions, customer inventory
levels/forecasts, product color, social media, time of day, promotions, sales of complementary
products (perhaps in other industries).
What are career paths for people who forecast demand?
6 June
Final comments: what the group would like to see/hear:
1. The details of Machine Learning
2. What are the tools and techniques that can be used to automate the demand
forecaster?
3. What are the tools that can be used to balance inventories?
4. Need to better understand where to invest: more accurate demand forecasting or more
agile supply chains that reduce the need for more accurate demand forecasting.
5. Case studies: ‘here’s how we did it’
6. New produce introduction and demand planning.
A summary of discussion items that generated the greatest interest throughout the meeting:
 The art versus the science of demand forecasting/planning/sensing: how to advance
both?
 How to evaluate the value of more accurate demand forecasts.
 Integrated product and supply chain design to reduce the impact of demand forecast
error.
 Correlation and improved demand forecast accuracy.
 Impact of sales and marketing incentives.

3. 3
 New product introduction.
 Old product phase out.
 Different supply chain strategies for product introduction, mature product, product
phase out, after market.
More detail on what appeared to be the two hottest topics:
The art and science of demand forecasting. How can both the art (e.g., developing an incentive
structure for sales and marketing) and science (e.g., understanding correlation between
demand and other factors, such as macro-economic conditions, season, weather, price,
competition) of demand forecasting be improved?
Demand forecasting versus product and supply chain adaptability. This goes back to product
design. If lead times are short and the product is designed to be differentiated late in the supply
chain (BOT or almost BOT; pull supply chains), then accurate forecasts may not be necessary.
Where should we invest: in product and supply chain design that do not need accurate demand
forecasts, or in improving the quality of the forecasts?