IBP supply is supply chain planning tools that include highly integrated solutions that can be used to have an effective supply chain plan that extends the principles of S&OP throughout the supply chain.
To plan the supply chain effectively it is provided with two algorithms S&OP heuristic and optimizer.
A heuristic is a kind of an algorithm that will not explore any supply chain constraints or will begin by exploring the most likely constraints. Typically used when not searching for the best solution, but for any solution fitting some constraint. A good heuristic would help to find a solution in a short time, but may also fail to find any solutions that should not be tried for the supply chain.
To find the cost-optimal solution, the optimizer transforms the IBP for Supply model into a complex mathematical representation. It searches the best solution by ensuring to explore any manufacturing and distribution supply chain constraints. To find a good solution some times have to compromise with time and it rarely fails to provide the inferior solution that should not be tried for the supply chain. The function to be optimized is called the objective function. This usually refers to profit maximization or cost minimization. In optimization problems, constraints are given by inequalities that respect functional attributes
- Production Capacity
- Material Availability
- Lead Time
- Lot Sizes for Transport and Production
- Frozen Horizon
- Inventory Target
- Quota Calculation
- so on..
Let us do analysis on some scenarios that the algorithm optimizer generates the best solution compared to the solution generated by algorithm heuristics.
Let us understand through the definition of the key figures that are used here in our analysis.
Customer Demand – Requested or forecasted demand of a customer for a certain product for each period of the planning horizon.
Total Customer Receipts – Total amount delivered for customer demand on time.
Non-Delivery Cost Rate – Costs per unit of a customer demand quantity per period that is not met by the supply plan stored in total customer receipts.
Capacity Supply – Amount of capacity of a resource available per period, for example, defined in hours, tons, or pieces.
Capacity Usage of Production Resource – Amount of a resource’s capacity supply consumed by the production of a product.
Capacity Utilization – Capacity utilization is a measure of the extent to which the productive capacity of a business is being used.
Dependent Customer Demand – Downstream key figure corresponding to the upstream key figure Outbound Customer Demand which is Demand for a customer product combination per period to be met by a ship-from location.
Customer Supply – Downstream key figure corresponding to Constrained Demand which is the Quantity of the product supplied per period from a location to a customer.
Customer Supply Adj. – Downstream key figure corresponding to the upstream key figure Adjusted Customer Receipts which can be Manual adjustment that enables you to fix the values of customer receipts.
Customer Transportation Cost Rate – All costs proportional to the transport quantity of a product from a location to a customer. The transportation costs are defined per unit of product per period for customer receipts. Used only by the optimizer.
All necessary master data and transactional data can be uploaded using the Data Integration App or using CPI. The irregular assumption in these test scenarios that no initial inventory and no late deliveries allowed.
Heuristic and optimizer profile is created using S&OP Operator Profiles App.
Case Scenario 1: Demand Planner is anticipating a sudden increase in orders from certain customers, hence the demand planner has sent revised numbers to the supply manager to check if the new orders can be fulfilled or not. The supply planner checks for the available capacity with him and whether the orders can be satisfied. The supply planner can do the capacity checks by running the optimizer profile in IBP planning view in simulation.
Capacity utilization before changing the consensus demand and running the heuristic operator, all consensus demand is met that can be seen in the “Total Customer Receipts” key figure. Now, let us increase the consensus demand for periods from 2019W36 till 2019W38 to 50,000. Run the SOP Heuristic operator under simulation and it can be observed in planning view that the entire consensus demand gets fulfilled and shows that overcapacity utilization to fulfill the customer demand. It also concludes how much-extended capacity required to fulfill the increase in the demand.
It is observed that utilization increases to a very high value (>100%) in order to fulfill the entire consensus demand.
Now, let’s run the optimizer profile in simulation from planning view.
It is observed that the utilization doesn’t exceed 100% in any of the locations/Time as a capacity constraint is respected by the Optimizer.
Case Scenario 2: The company has various Distribution Centers(DC), each having different transportation costs associated. Delivery to the customer is thus made through DC which has the least Transportation cost.
Original Demand at each DC can be viewed in the supply planning view.
Due to an oil price increase in a region, some DC’s are not able to fulfill the demand. This scenario in the IBP system is depicted by increasing the Transportation Costs of particular DC in that region. For example, let us increase the Transportation Cost Rate at DC02 and DC03 to 100.
Run the SOP Optimizer operator under simulation in planning view, due to the Transport Cost differential, the system will choose to fulfill the demand from DC’s which is having least costs. Hence the entire Customer Demand is fulfilled from DC01 only.
Case Scenario 3: A XYZ company has various customers for a product. In the situation of no stock, limited supplies, delivery of the product can be prioritized on the basis of the non-delivery Cost Penalty associated with each customer. The customer demand which is having the highest non-delivery cost rate is fulfilled on priority
Product PRODFG01 having three customers CUST01, CUST02 and CUST 03, each having a demand of 10,000, 5000 and 10,000 units respectively. Let us edit the non-delivery cost rate for the same time period to 5000 for CUST02 and CUST01. CUST03 non-delivery cost rate remains the same.
Run S&OP Optimizer in simulation, having limited supplies it is observed that the demand for CUST02 is fulfilled entirely on first priority. Now CUST01 gets the second priority with the partial fulfillment of demand. There are no receipts for CUST03 at all since it has the least non-delivery cost associated.
Conclusion: Roughly concluded optimizer generates the optimal and feasible production, distribution for the supply chain network taking into account the constraints.