Rolling Statistical Forecasts, Detailed Marketing Campaigns and Promotions, Product Phase-In, Phase-Out, Budget Gap Management.

Historical Outlier Cleansing

Statistical forecasting algorithms look primarily at historical sales trends and seasonality to produce a future forecast. Therefore, a key process prior to engaging these algorithms for a demand planning solution is to “cleanse” the sales history from one-time events that are not likely to re-occur. Our solutions offer statistical and data mining methods of identifying candidate points to be cleansed, usually referred to as “outliers”. Advanced users could approve this cleansing and hence improve the accuracy of the statistical forecasting methods.

Demand Element Substitutions and Supersessions

Enabling the forecasting of phasing out of old products and introducing new ones, new selling locations or even customers after a merger or acquisition, requires re-aligning the sales history input to the elements that define the market outlook. Our solutions provide features that support these processes and help the users create the necessary data for the algorithms by just defining how these new elements will behave (e.g. new warehouse location will be selling to the same customers as the existing one and will be cannibalizing 20% of its sales while increasing it to 30%). High-level budgetary New Product Introduction (NPI) plans can be more detailed and precise to enable aligning materials and resources to serve demand.

Advanced Statistical Forecasting

Our solutions combine a list of proven statistical forecasting algorithms and automatically select the best-fit for the underlying data with minimal interaction by the user. Our users need not be statisticians to run a business and generate forecasts but they can rest assured that they are utilizing state-of-the-art algorithms that provide reliable forecasts for both sparse and non-sparse data.

ABC Analysis and Data Mining

When asked to review the plan or the budget, management cannot go through each SKU while it should not only be constrained to top-level reports. With many parts of the product or customer portfolio competing for attention, a true planner should ensure that the “important” items are reviewed. Our solutions can help companies define this importance (by measuring volume, revenue, profit, strategy, etc.) and enabling drilling down to ensure that attention to high-value or high-risk parts of the business are not overlooked. Analysis like multi-level ABC pareto analysis, Coefficient of Variance analysis, data mining clustering techniques etc. are included and can be configured according to each case.

Collaborative Market Intelligence and Demand Overrides through Writebacks

Unfortunately, sales history does not contain all the information to supply an accurate demand forecast signal. It is missing planned marketing campaigns that are planned to increase sales, future events like big government projects closures, pricing changes, and other information about customer behavior that are known usually by the sales representatives in the field and not by central planning teams. Thus, collaboration with these key stakeholders is essential for a demand planning application. Our solutions use write-back functionality with flexible slicing and dicing and break-downs to incorporate this information and consolidate it.

Budget Gap Analysis & Management

Supply Chain Planning through the Sales & Operation Planning process is the conversion of strategic goals into managed execution. Therefore, a key part of the process is to monitor how the tactical planning and execution compares against strategic goals and what actions need to be taken to manage potential gaps. This shifts the business to pro-active planning against financial targets that are reviewed and scrutinized by management and enterprise stake holders.

Supply Demand Balancing. Plan Procurement, Sourcing, Distribution, Capacity and Inventory Position.

Sourcing Distribution & Procurement Planning

Generate an optimal supply and sourcing plan against the given demand to manage decisions like make vs. buy and optimally propagate demand across the enterprise's supply network.

Capacity Production Planning

Calculate an optimal global capacity, product mix and inventory plan that will help the enterprise position its resources to meet the planned demand. Cost-driven decisions on what the right investment in allocating capacity should be to meet demand against cost of holding inventory and outsourcing in the most profitable manner.

Inventory Planning

Systematic optimization of inventories to avoid waste and eliminate excessive stock while keeping customer service level high. Inventory safety stock right-sizing (how much extra inventory should we hold in case something goes wrong) along with optimal placement (where is the best place to keep it between plants, central distribution centers and local warehouses, to minimize overall costs).

Advanced Optimization Algorithms

Sourcing, distribution, inventory and capacity planning create an optimal plan against cost objectives while meeting concurrent capacity and material constraints. To achieve this a set of advanced optimization algorithms through state-of-the-art mathematical programming solvers blended with efficient heuristics is utilized as per the peculiarities of the problem to be solved.

Daily Netting of Forecast against In-Month Shipments and Open Orders. Demand sensing and alerts that kick-off ad-hoc processes.

Advanced Demand Netting

Provide a real daily demand signal to the daily operational processes that correctly consumes forecast out of real customer orders and deliveries. Complex rules can avoid forecast double-counting if the actual orders miss the forecast target either in time or in product configuration but still they need to consume it as to avoid extra inventory or unnecessary production.

Sense & Alert Demand Exceptions

Continuously monitor the performance of the forecast against In-Month actuals and issue alerts and exceptions in case forecast is being consumed more rapidly than expected or the other way around. Quickly react and re-adjust the supply chain to reality by taking corrective measures and avoiding extra costs or low customer service.

Optimally Schedule resources, materials, assets and continually adjust per changes and disruptions.

Generation of Production or Distribution Proposals

MRP-like creation of production and/or distribution proposals considering minimum, maximum and incremental lot sizing, downstream and upstream pegging to customer orders and supplier receipts. Synchronization with ERP systems in reading released production/distribution orders and/or firmed proposals and apply either incremental or regenerative scheduling. Automatically consolidate proposals via shared id’s with the ERPs during publish and refresh of the schedules.

Lot Size & Routing Optimization

Optimization of the lot size for production and/or distribution proposals in order to balance meeting due dates, inventory build-up and setup costs. Choose between alternative routing options for production even if each routing has a different number of manufacturing stages (e.g. between many manual steps with longer times and less setup vs. full automatic fast machines with larger setups). Optimally select the best routing either based on optimizing throughput, minimizing wip, or balancing both criteria.

Production Scheduling & Sequencing

Allocate optimal routings and lot sizes on machines in order to realize an optimal schedule that would balance contradicting costs like setup, lateness, inventory and production. Simulate the schedule and calculating projected inventory levels. Generate and simulate material and capacity feasible schedules by considering plant and resource-specific maintenance schedules and shutdowns as well as raw material availability and arrivals. Allow manual drag and drop actions from users and re-simulate schedule to report possible issues in either capacity, material or customer order due date violations. Allow manual edits and manual schedule on top of optimal ones to support required adjustments to the schedule before publishing for execution.

Managing Demand Allocations

Support multiple modes in production and inventory allocation to demand. FIFO, LIFO rules can be applied but also hard-pegged rules in strict configure-to-order environments where customer orders need to be pegged to either inventory or specific production and inherit their planned dates. Complex production and demand allocations allow for more detailed calculation of optimization costs especially in many-to-many scenarios where many production proposals can be pegged to many demands. The solution allows for users to override automatic first-come-first-serve allocations and lock them in with full alignment with the ERP.

Publish Schedules to ERP/WMS/TMS for execution.

Full Integration with Execution & Transactional Systems like ERP, WMS, TMS, MES.

Our daily scheduling solutions generate executable schedules that are communicated to execution systems to manage their execution. Either manufacturing execution systems (MES), ERPs, Warehouse Management Systems (WMS), Transportation Management Systems (TMS), can take the production, distribution orders with full demand allocation information and use it to support and monitor execution while in the same time provide feedback of the starting position for the next calculation.