Existing icons on multiple menu bars replaced with a tab structure where options are grouped together to reflect the user workflow.
Quicker and simpler user navigation.
In the same way you attach documents to an e-mail, documents can be attached to any cell within sofco Demand Planning.
Using cell notes can be helpful in storing text against a value. By attaching documents more information in various formats can be held for reference and updates.
Now part of the standard software rather than an a separate ”Add On” module. Dials using Red/Amber/Green to indicate severity, highlight key exceptions e.g. forecast accuracy, events, incomplete data etc. A single click will take the user to the details and screens to resolve.
Focus directed to key products/customers ensuring the highest priority receive the appropriate attention to achieve the best possible forecasts.
Automated Algorithm Association
System automatically groups products to “Forecast Types” and associates them to the appropriate algorithm type i.e. Regular, Sporadic, Sparse, New.
Reduced administration for users. Previously this was a user activity where regular reviews of product/algorithm relationships were evaluated and updated through the lifecycle of the product. This process is now automated using sales history and allows for user override.
Sales based detection of Historical Time Horizon
System automatically detects when the first sale of a product takes place and sets the start of the historical sales horizon accordingly.
Reduced administration for users. Now an automated activity. Previously users would set the historical horizon against different algorithm instances then change as the historical horizon extended. Users can control when they believe a product has reached a mature sales level.
Classification of Products by importance and volatility for filtering and exception purposes.
Focus on the most important yet difficult products to forecast. This can be done within a product and or market peer group for more detailed analysis.
Remote Collaborative Planning
Excel User interface allowing users to “Check Out” a sub set of data, work offline in Excel updating the Forecast and “Checking” the changes back in.
Efficient use of time. Remote users such as Account Managers can update the forecast e.g. whilst in a meeting with a Customer, then send the changes back to the main system when next connected to the Network.
Manage the full event cycle of creating the new event, determining the uplift, assessing the value, profiling the shape, managing in flight and Post Event Analysis.
Improved Accuracy, execution and Profitability of Events.
Sorting & Allocation Algorithms
Methods to deploy stock to Customers/Store in Supply Shortage Situations.
Effective distribution of products ensuring the best use of limited supply considering customer priorities and rules.
Forecasting Strategies for New Product Launches
Profile based Forecasting and specialist Reforecasting Algorithm working with very limited sales history.
Maximizing early sales opportunities. Highly responsive forecast algorithm reading real sales in the short term
Simplified Linking to support Product Supersession
Products are linked together via a “Phantom” parent where Forecasting takes place. The Phantom Forecast is then allocated to the new product.
Sales History is not lost when minor specification changes mean that items are allocated new SKU codes, whilst essentially still being the same product. This results in simplified and more accurate forecasting for supersession products.
Phase In/Phase Out
Phase Out Products Forecasts are generated accounting for existing stock, thus restricting the overall volume accordingly. Phase In Forecasts are then dovetailed in with the Phase Out.
Reduced Stock Obsolescence of Phase Out Products and less stock overall as Phase In and Out are synchronised.
Development of Multiple Forecasts with side by side comparisons before deploying the selected Forecast.
Predict different possible business outcomes. Ability to evaluate multiple possibilities e.g. Optimistic, Pessimistic, Most Likely Case Forecasts before deploying the agreed Forecast.
Demand Sensing Support
The import and forecasting of multiple other sources of information relating to customer demand e.g. EPOS Sales, Stock, Market Intelligence Data.
Early Warning Signals that demand elsewhere in the supply is changing thus allowing the anticipation of the knock-on effects of these changes. This in turn contributes to greater forecast accuracy.
Import of Pipeline/Opportunity information from CRM systems.
Improved Collaboration and Efficiency. The Pipeline can be compared to the current Forecast with variances highlighted via alerts in Exception Dashboards. Bringing the two closer together results in improved forecasts.
Integration of Customer Forecasts
The import of customer supplied forecast data. This can be done at a global or specific customer level.
Sense check your own forecasts against that of a customer. Offers the ability to switch to a customer forecast where appropriate.
Designed to work for organisations where there is no manufacturing or bill of materials to consider. Will allow store level to depot level replenishment feeds.
Accurately calculate a purchase order plan based on respecting desired safety stock levels, forecasted requirements, order minimums, maximums and multiples.
Statistically calculated variable by Demand and Seasonality profile. Safety stock based on either past sales, future forecast or a combination of both.
Drive down stock levels by statistically calculating the correct amount of stock to hold at any time.
Customer designed workspaces that allow the management of this years and the creation of next year’s budget. The ability to roll the budget by quarters and refresh where necessary.
See at a glance how existing forecasted requirements are performing to budget expectations, both in terms of value and volume. Allows adjustments to be made to get things back on track.
An optional VBA front-end assisting the choice of which products to load into the workspace.
Allows flexible session filter creation based on hierarchical levels, attributes and transactional data such as sales in the last time bucket, customers who bought it etc.