Make to Stock versus Make to Order

The growing variety of products with distinct characteristics, market demands, logistical constraints, and increasing costs pressures lead companies to decide on the policies to manage their production process. Particularly, finding the optimal production strategy for each product is a paramount decision on the planning process of every manufacturing company. To help to answer this question, two main policies arise: make-to-order (MTO) and make-to-stock (MTS).

MTO systems are tailored to offer higher flexibility and responsiveness to customers’ specifications. Still, they are highly dependent on order execution and performance measures not to harm delivery lead-time. On the other hand, MTS systems typically offer a lower variety of products. They are characterized by non-customizable production processes, aiming to achieve higher efficiency levels. The success of this approach relies on demand forecasting, inventory planning, and lot size determination.

However, to assure the optimal strategy not only for each product but to the entire production environment, companies tend to operate under hybrid MTS/MTO systems. Throughout this insight, we look not only at the main costs and decisions involved but also to analytical approaches to define the optimal production strategy mix concerning a company’s product portfolio.

 

Which are the costs and dimension that should drive production strategy?

The definition of the most suitable production strategy relies on the evaluation of two main cost categories: stock and orders’ fulfillment. The implications on stocks should be analyzed by considering the effects on cycle stock (stock to face expected demand) and safety stock (buffer stock against uncertain demand). The influence on orders’ fulfillment should consider the consequences on setup costs, lot sizes, and late or lost sales.

Therefore, which are the key dimensions that should be included in this analysis? See our exhibit.

 

From a sales profile perspective, products with higher demand can benefit from a make-to-stock strategy. It reduces the delivery lead time, by increasing product availability, and increases production throughput, since larger lot sizes are possible. However, demand variability and service level are also relevant. Erratic and uncertain demand would expand the safety stock levels, increasing the overall stock and costs of a make-to-stock strategy.

The production strategy is also influenced by the production and order lead times. Typically, products with an average order lead time greater than the production lead time are suitable for a make-to-order strategy. However, these products cohabit with other products in the factory, introducing uncertainty in the production capacity, which may lead to late or lost sales if production capacity is scarce.

Besides production lead time, the impact of introducing products on the production line should also be measured. Low compatible products with high setup times for hygiene may not fit a make-to-order strategy. Hence a make-to-stock policy should be adopted.

Finally, products’ margin and production, holding, and storage costs are also critical variables on finding the optimal MTS/MTO decision. Typically, less expensive products are prone to a make-to-stock strategy. In contrast, a make-to-order strategy is preferable for products with a higher risk of obsolescence and damage.

 

How analytics can boost the decision making on determining the optimal MTS/MTO mix?

An analytical model can leverage the MTS/MTO decision and provide the decision-maker with the combination that maximizes the overall benefit for the business. The development of a digital twin depicting the idiosyncrasies of the shop floor activities is the first step to address the topic.

More specifically, a typical approach relies on the modeling of the operations around the bottleneck operation. A queuing model represents the orders’ flow throughout the factory. For instance, more MTO products in the system mean smaller production batches and more setups, which leads to an increase in production lead time. On the other hand, it brings a reduction in the stock levels.

After that, costs and margin information on the key dimensions described in the previous section are used in the simulation to define the optimal strategy for each product. Finally, the decision-maker may fine-tune automatically generated solutions and evaluate the expected benefit of different MTS/MTO combinations.

 

How to develop such a model?

All in all, the development of a model and solution approach for deciding whether a set of items should be made to stock or made to order requires:

  • A thorough mapping of the shop floor activities, with the identification of the production and setup times, especially of the bottleneck operations;
  • Historical orders information, with details of the ordered products and required delivery lead times;
  • Operational costs and products’ margins to determine the most economical decision for each product;
  • Analytical expertise to develop the decision-support model.

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