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Client is a well-known name in automobile filters worldwide. Data optimization was executed in one of the country specific subsidiary of client. It supplies automobile parts to well known automobile companies like Suzuki, Ford, Daewoo etc. worldwide. |
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| The subsidiary has its own manufacturing units. They are grouped
into 5 Strategic Business Units (SBU's). These business units are created in tune
with customers and the products, which are supplied to them. |
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| Client depends on both purchase orders and sales forecasts for its
procurement, production planning and inventory control planning. |
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| In all subsidiary firm produces more than 150 different automobile products.
Subsidiary firm had also implemented ERP system from vendor BAAN. |
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This ERP was working fine for all of the crucial business processes but they were
still not satisfied with the way it was handling their production planning and production execution. So they were looking for a better solution for this problem.
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Problems |
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| 1. MPS and MRP systems of the Baan ERP were very difficult to operate. |
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2. MPS and MRP systems of the Baan ERP were very slow and were taking more than 4 hours each for making production n and material plans. |
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3. Not taking into account these constraints when making the production plan
Simultaneously (this resulted in un optimized plans)
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Capacity Constraint |
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Human resource constraint |
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Vendor Capacity constraint |
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Raw Material Constraint |
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| 4. Not taking nested Objectives with Priorities |
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| Overall Process Flow |
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| Benefits of our solution: |
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| 1. |
When Optimization Engine is implemented in an organization where there is already an implementation of any ERP system, Optimization Engine works as the brains behind the business operations whereas the ERP system works as the business enabler. |
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| 2. |
Our Optimization Engine software takes care of all sales orders and sales forecasts. It consolidates all sales demand forecasts made by different sales units as well as the purchase orders; the company receives. Based on this consolidated figure, the company can do any supply chain related planning.
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| 3. |
Our Optimization Engine Software is based on Genetic Algorithm based computation. The plan it generates is the most optimal solution for the given scenario and no other solution can easily beat it. |
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| 4. |
Optimization Engine software takes into account stock out cost (loss of order due to unavailability of stock), inventory cost and transportation cost, raw material costs, human resources costs, inventory costs, machine change over costs and it generates a plan to minimize all of these costs. User has a choice to assign |
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| 5. |
Weight age and the software generate the most optimal plan accordingly. |
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| 6. |
Optimization Engine software takes into account lead time and other constraints. |
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It takes them into account completely when it generates the most optimal plan. |
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| 8. |
Optimization Engine takes into account service levels for items, business units, clients and orders. User has a choice to give weight age to these service levels individually and the software generates the most optimal plan accordingly. |
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| 9. |
Our experience shows that even at places like big and reputed multinational companies; the efficiency of their business processes is not optimal even after good implementation of ERP systems. In most cases our software can improve their business processes by at least 10% and it can go even to 300%. This translates to corresponding cost savings for the company. |
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