In the trading environment, it is necessary to control and organize the flow of materials and services from vendors to customers. The principle of the business is to match demand from the customer with supply from the vendor. This type of business is known as triangular business, principal business, or back-to-back business. The industry defines this type of business as business where purchasing and sales are carried out in parallel, in the same trading department. For example, you get a sales order from a customer, search for the best vendor to fulfil the customer’s requirements, and issue a purchase order, all in one transaction. You coordinate shipping, transportation, and customs clearance for the goods. The vendor then delivers the goods directly to the customer. The trading company gets an invoice from the vendor, and bills the customer. Finally, you receive payment from the customer and make a payment to the vendor.
In the SAP system, Global Trade Management is the software which is mainly built for traders. The backbone of Global Trade management is Trading Contract Management. It integrates the sales and purchase side ERP system to facilitate traders with trading. It also helps traders with profit analysis in each deal. It has basic feature of monitoring the trade contract and actual of sales side and purchase side is captured in the GTM system.
Overall Global Trade Management has four main components such as Trading Contracts, Logistics Execution, Expense Management and Data Association.
The Expense Management function in SAP Global Trade Management enables you to handle various kinds of costs and commissions incurred by a trading business. There are various types of expenses in the trading environment, all of which are realized at different times and posted to different accounts, and these are often not synchronized with the main flow of a trading contract. Trading companies must control trading-related expenses, such as freight, loading charges, storage costs, insurance, customs duty and commission efficiently. It is important to accurately assess costs, including purchasing costs and all trading charges associated with the trading contract.
The Data Association function in SAP Global Trade Management enables you to create links (associations) between sales and purchasing data at item level. This enables not only for the trading company to mitigate risk associated with handling single sided trading contracts but also for the trading company to clear its inventory. This allows the trader to associate various sales side trading contracts with various purchase side trading contracts in a 1:M, N:1 and N:M basis and also re-associate if required. However, this approach could lead to a higher cost if we do not consider cost affecting factors while associating and this makes this task a tedious and tiring one.
Global trading companies work on low margins ranging from 5% to 7% and it becomes utmost important to take care of the trading cost. Operational margins are one of the most important financial measures for any company and mostly all trading businesses look for ways to increase the margin.
Our proposal covers the cost aspect and improves the current association model. There can be two types of costs: Fixed costs and variable costs.
Fixed cost: Cost mainly associated with the transfer good from one location to another irrespective of the quantity such as documentation, minimum freight charge, commission etc.
Within GTM these are easy to extract from the GTM historical data for the given vendor and material for the same source and destination location.
Variable Cost: Cost which are associated with the quantity of goods transferred from inventory location to sales order location. Such as packaging cost, labor cost, rail cost, shipment cost, etc.
Data extraction technique is applied to gather the fixed and per unit transfer cost from the historical data of trading contract for the optimization problem. Based on the total cost incurred we will recommend the trader/user to choose the most optimal association.
The optimization approach can be modelled as:
Minimize ∑∑ (Ci,j + qi,jCqi,j)
∑qi,1 >= Qj (for the first each sales orders) (in this case i is from 1 to N(inventory locations), j is for sales order )
∑q1,j <= P1 (for inventory or purchase order, not exceed the inventory, in this case j is from 1 to M (M is sales order) )
(Above example shows only for first row and first column, similarly write for each row and column for the matrix)
For each, 0 <= qi,j <= Qj (quantity allocated should not be more than sales order)
For each, 0 <= qi,j <= Pi (quantity allocated should not cross the inventory)
i,j are inventory and sales order index respectively. And we have N inventory locations and M sales orders.
Ci,j = Fixed expense/cost to transfer goods from inventory to sales order location
Qj = Demand for sales order j.
Pi = Inventory at location i.
Cqi,j = per unit cost/expense to transfer good from inventory i to sales order location j.
By applying the optimization model on association, we can find the most optimal associations each time the trader has to make the association. Trader does thousands of such associations daily and using this would reduce his operational cost and increase the operation margins drastically.
– Original idea – Sudhir Verma