Demand Collaboration
Companies indicate that their consensus forecasting processes are broken.
• Only 15% of companies report that their consensus forecast is arrived at after input by all departments, converted into end item level details, and actually adjusted based on near-term changes.
• 14% of companies indicate that their consensus forecast is arrived at after input by all departments and then allocated to customer sku-level for execution
• 22% of companies indicate that their consensus forecasting process involves little or no top management involvement and supply chain operations forces a plan with some sales input.
Table 6 shows how companies are currently communicating with trading partners as part of their S&OP process. It shows that phone/fax/email is still the primary mechanisms for getting market intelligence into the S&OP process.

Table 6: How Companies Communicate with Partners in the S&OP process

The key characteristics for demand collaboration technology are:
• Need to be web-based to allow disparate internal organizations distributed across multiple sites as well as external trading partners to provide their inputs.
• Need to support offline, bi-directional Excel integration
• Need to be integrated with the upstream sales revenue planning process as well as the downstream supply planning process. Companies have indicated that their demand collaboration process have failed due to lack of application integration. Composite application technologies are a great solution to resolve this issue.
• For large companies, scalability and performance are important requirements due to the presence of hundreds of users as well as the large amount of data that exists in the system.
• Role-based functionality is an important requirement because of the need to look at the data in different units based on the organization.

Demand Forecasting
Only 36% of companies say that their current demand forecasting tools satisfy their S&OP requirements. Among these, 50% of the companies that have deployed ERP systems say that their demand forecasting tools satisfy their requirements. By comparison,
82% of the companies that have deployed best of breed vendors indicate that their forecasting tools are satisfactory.

Despite demand forecasting being perceived as a mature technology, innovation is still to be found in this area. One example is intermittent demand forecasting. In the capital goods manufacturing industries, irregular or sporadic demand is a common problem.

Unlike most product sales and demand data, intermittent demand contains a large percentage of 0 values (often 30% or more of the periods will have zero demand), with nonzero values mixed in at random. Conventional forecasting algorithms rely on smooth demand and use techniques such as exponential smoothing and moving averages that ignore the special role of zero values.

In such environments, there are special technologies that are available to resolve this problem including the new Smart-Willemain method which is a patented solution combining statistical "bootstrapping" and Monte Carlo simulation techniques. This technology does not assume that the probability distribution of demand over time is a “normal” bell shaped curve but develops this curve as part of the forecasting process.

If you have a particular challenge with demand forecasting, such as troubles with intermittent demand or poor forecast accuracy on short-term demand (often found in CPG companies), be sure to look for add-on capabilities to help address these specific needs. Some vendors are now also offering new managed services in which their forecast experts will take care of forecasting problematic or challenging product lines for their enterprise customers.

Demand Shaping
Only 11% of companies indicate that their current capabilities to perform demand shaping fully satisfy their requirements. Demand shaping based on promotions and causal events is more prevalent in consumer packaged goods, distribution, food/beverage and
retail industries.

The industries mentioned above have some similarities:
• They usually over-spend on trade funds/incentives.
• They face out of stock situations on products.
• They face heavy margin and price pressures.

These industries can gain the most from using improved demand shaping technology. Look for these characteristics in a solution:
• Allow the management of trade funds and promotions in a centralized system and provide visibility to performance of promotion events as compared to sales targets.
• Support post-event analysis of promotion funds based on manufacturer margin, retailer margin etc.
• Can accurately predict promotion lifts to demand based on price elasticity.