Case Study (Optional)

Collaboration is a tedious process between a company and its suppliers. Manual processes are error-prone, which results in oversights and mistakes.

Customer service levels are impacted by supply chain disruptions; opportunities to take corrective actions are missed.

When constraints are encountered and cannot be corrected, ACME struggles with how to allocate available quantities.

ACME has many locations in their supply chain, which necessitates a tool to determine when / how much of a product to deploy to each location.

ACME relies on a variety of systems to run the day-to-day business. This creates significant challenges for integration.

Using SAP IBP, ACME has better insight into supply chain disruptions before they happen. They can manage customer orders in a way that keeps the highest priority customers happy. This also helps ACME optimize inventory turns and maintain a high level of customer satisfaction.

The monthly demand review process acts as management control for demand management, ensuring sales and marketing strategy and activities are aligned, and continue to align, with the overall business strategy.

Consensus demand is agreed upon using a statistical forecast as input in tandem with the SAP Integrated Business Planning for sales and operations module.

This consensus demand then serves as input for Supply Planning.

After completion of initial planning, heuristics for supply plan is executed (Unconstrained Planning), which propagates the demand through the supply chain network. After heuristics, all demands are fulfilled on time with receipts and there are no shortages.

In time series, planners typically look at the plan in weeks. However, on the order series side (operational planning) the root planning level is in days.

In a typical S&OP cycle, rough-cut capacity planning is performed, using heuristics, to get an infinite view of how much capacity is required to meet the demand. After tactical planning is completed, the next step is to share critical component forecast to the Supplier using SAP Ariba and get their consensus.

The Supplier commits from SAP Ariba are fed into Supply and Response (operational) planning and act as a constraint for constrained planning run.

In SAP Ariba, the Supplier using the Customer filter sees the collaborated product demands from ACME. Only the critical components are shared with the supplier.

Unfortunately, the supplier is not able to fulfill the entire demand, due to a production shortage.

The supplier performs a short commit for periods requested and sends his commit to SAP IBP.

Alerts in SAP IBP provide the supply planner visibility on short commits from the supplier.

The unconstrained demand is passed onto the response side using the SAP IBP Copy Operator.

An unconstrained forecast is available on the response side since the planning algorithm has not been executed yet.

Unconstrained forecast values received from the tactical planning area are disaggregated to daily level in SAP IBP for response and supply.

After reviewing the supplier constraint, the Supply Planner decides to execute the Supply Planning with the Allocation Creation run to realize the impact of the supplier constraint on finished product level demands.

The purpose of the Supply Planning with Allocation Creation run is to evaluate the impact of constraints on the supply plan and help derive allocations.

This algorithm does NOT use any kind of sales order consumption logic because it does not make sense to use the higher forecast or sales orders in the context of allocation calculations. Therefore this is executed in a planning version.

The rule prioritizes sales orders by the customer or any other customer attribute. Forecast and any other demands not specified in the rule are picked up at the end of the prioritization rule.

Since we are running the Supply Planning with Allocation Creation run, only the forecast segment is relevant. A forecast is prioritized by the requested date.

Within a requested date, if the supply is not enough, the forecast is satisfied in order of sold-to party. For example, customer CU01, CU02, and CU03. The naming convention helps to define the sequence.

Due to a short commit from the supplier, every demand would be fulfilled with a delay which results in a penalty cost that affects all the demands post short commit. This results in a huge revenue risk.

Due to the short supply commit from the supplier, there is a huge revenue risk.

There is an Emergency Supplier which is blocked by default, due to long lead time and high costs.

To see if the revenue risk could be mitigated, the use of Emergency Supplier is considered.

The planner realizes that it is essential to escalate and see if there were options to mitigate the revenue risk arising out of short commits.

The planner shares gating factors information and its potential impact on revenue to the manager.

The manager gets back with a suggestion to simulate the situation with the emergency supplier, which is presently in a blocked status because of its long lead time and cost.

The planner welcomes the alternative option and prefers to give it a try and analyze the situation whether it will bring in improvement on planning results. The algorithm tries to use the main supplier for most of the cases, and in cases wherein there were constraints with the main supplier, it will shift orders to an emergency supplier. The emergency supplier is used for periods where there were short commits from the main supplier

All the demands are fulfilled without delay, values for Forecast Constrained (Late) vanish, and there were no periods wherein revenue is at risk

By introducing an emergency supplier, exceptions were mitigated, and the planner prefers to go with this approach and aligns with the purchase manager on the decision

As a next step, he copies the constrained forecast results into the allocation KF, which are further used in Supply Planning Run with Order Confirmation when sales orders are considered.

The unconstrained forecast from time series was already populated in the base version. Together with sales orders and unconstrained forecasts, Supply Planning Run with Order Confirmation can be executed in the base version. The alert chart reveals the situation of Sales Order quantities exceeding Allocation quantities. In the case of a sales order (allowing partial confirmation) exceeding an allocation quantity, SAP IBP will confirm the remaining quantity late.

Production receipts are also generated, equivalent to the combined forecast from all DC’s.

The purpose of the Supply Planning Run with Order Confirmation is to confirm sales orders and replan supply. This algorithm does use the sales order consumption logic, meaning it uses the higher forecast or sales orders to determine demand in a period. The Supply Planning Run with Order Confirmation would be a regularly scheduled job (daily).

The gating factor also reveals the reason for sales order late fulfillment, and it is because of sales order quantities being higher than Allocation quantities.

In the SAP S/4HANA sales order display, you can see the split confirmation date due to the allocations cap as planned by SAP IBP for response and supply.

Confirmation details that were determined in SAP IBP can be visualized here as well.

ACME uses the output of the Supply Planning Run with Order Confirmation (purchase requisitions, stock transport requisitions, and planned orders) to SAP S/4HANA. The planned orders are input to the embedded S/4 - PP/DS order scheduling to precisely plan the production horizon.

The production planner receives information on possible downtime. They introduce downtime on the production resource and executes a simulative optimizer run to understand the possible impact on orders due to downtime. On analysis, the planner senses certain planned orders that were deallocated due to resource constraints and decides to delete those and proceed with results. This brings in a deficit situation and the planner passes this information back to SAP IBP, which is realistic.

The last step is to execute deployment after getting the updated production orders from SAP S4 PP/DS.

The rule associated with deployment run fulfills the demand for date and within a date, it prioritized Customer 1, Customer 2, and finally Customer 3.

The planner reviews the situation and realizes that Customer 3 associated with DC3 is affected the most.

Even though prioritization is customer 1, 2, and 3, it is agreed with the customer that fulfillment should not be more equally deployed. Therefore, the planner wants to re-plan the deployment in a different manner.

The planner then executes the deployment using the Fair Share method.

A comparison reveals that fulfillment across the customer base has improved with fair share logic and the planner decides to go with this approach to comply with minimum fulfillment % agreement with customers.

The summary of the entire process sequence is shown in the following figure.