With the development of large-scale lean manufacturing, there are more and more small-batch multi-batch orders, and order management is becoming more and more complicated. Before the start of production, order distribution is the first hurdle, especially for enterprises with multi-factory production mode, order distribution has a greater impact on production and delivery efficiency, and different distribution methods will also lead to significant cost differences.

The traditional suborder mode usually relies on manual decision-making or follows a fixed standardized mode. The suborder efficiency is low, and it is difficult to fully consider all factors, resulting in a large amount of production resources and cost waste. So how do enterprises achieve sub-order optimization?

Multi-plant order: delivery time, production capacity and revenue decision game

Before analyzing the solution path, let’s take a look at the difficulties of multi-factory distribution. I believe that each enterprise in the sub-order, will consider the three issues: delivery, production capacity and income, strictly speaking, they are very important, but each enterprise is different, the priority of consideration is not the same, often there will be a situation.

At the same time, due to technical limitations, it is a great challenge for enterprises to calculate all the factors of each problem. Whether it is delivery time, production capacity or revenue, they are not just a single dimension of the problem, they are interrelated and impact, how to systematically comb and quantify these impact factors is also a huge challenge. Especially for those large enterprises whose production may involve hundreds of factories, thousands of workshops and production lines in different regions, the difficulty of single division is self-evident.

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In summary, enterprises often face the following problems in order allocation:

First, the blind pursuit of delivery, ignoring production capacity and costs, there is an increase in orders but income may not increase. Delivery is very important, but you can’t produce at any cost. If the delivery date of an order is at the end of May, due to a shortage of certain raw materials, additional cost procurement is required, so that the total cost exceeds the order income, at this time, the enterprise should consider whether to communicate with the customer to postpone the delivery.

Second, the factory capacity matching is not reasonable, resulting in a waste of resources and costs. The quantity of products ordered is more or less, the capacity of the factory is large or small, they are not necessarily one-to-one correspondence, some orders need to specify the factory, and some factories can undertake the output of several orders at the same time. For example, if there are 10 factories that can be produced for a certain batch of orders, in fact, 10 factories will not be allowed to produce, and some factories have a relatively large capacity and can undertake the production of 5 orders, and enterprises can have more choices when ordering. In standardized mass production, usually the more the number of production, the lower the cost, the factory opens a machine production, if you can produce as much as possible, you can reduce the loss of machine operation.

Costs and benefits are hard to calculate. In the calculation of income, can not only look at the amount of orders, but also consider the cost behind the situation, many enterprises when calculating costs, usually consider the material, labor and other obvious costs, easy to ignore the potential cost of transportation logistics. For example, the labor costs of factories in remote areas are low, but the transportation costs of raw materials are high, and the shipping costs are also high. On the contrary, labor costs are high in nearby areas, and transportation costs are lower and faster. Therefore, in order to compare costs comprehensively and scientifically, raw materials and labor costs cannot be considered separately.

Is there a way to take these factors into account and assign orders? The intelligent decision technology based on operation research optimization and machine learning can transform sub-order problems into mathematical problems for solving optimization, which brings new ideas for solving sub-order problems.