Make To Order And Make To Stock Pdf

  • and pdf
  • Thursday, May 27, 2021 10:58:15 AM
  • 0 comment
make to order and make to stock pdf

File Name: make to order and make to stock .zip
Size: 1875Kb
Published: 27.05.2021

Motivated by issues at a manufacturer of customized- and standard messenger bags, we develop an iterative two-stage framework involving marketing and operations models to determine the conditions under which a firm benefits from production spackling. With spackling, the firm uses flexible capacity to produce custom products as demanded each period,. Spackling broadly addresses market preferences, while mitigating the effects of "bumpy" demand for custom products by smoothing production, thereby improving capacity utilization as compared to a focused approach, where standard items are made with efficient capacity and custom products with flexible capacity. The marketing model employs logit-based choice and, given product costs and customer preferences, identifies optimal pricing, expected demand, and demand variability. Interestingly, we find that, under certain assumptions, the firm should price all products to achieve a constant absolute dollar markup.

Lean Manufacturing + TPS + Production Scheduler + JIT + Lead Time + KAIZEN + 5S + KANBAN

To browse Academia. Skip to main content. By using our site, you agree to our collection of information through the use of cookies. To learn more, view our Privacy Policy. Log In Sign Up. Download Free PDF. Amr B. Rania Ghazal. Download PDF. A short summary of this paper. Integrated Production-Inventory planning with make to order and make to stock considerations.

The model is employed within an Enterprise Resource Planning ERP system to generate feasible, constrained operational production plans and replenishment schedules. The model is applied to a process industry case of a single line, multi-stage, multi-product environment. The existence of co-products and the combined make-to-order and make-to- stock MTO-MTS production planning strategy are the main features incorporated.

Introduction and Literature Review Process industries have significant economic contribution and global spread. These industries are distinct from discrete industries in terms of production system requirements and specifications. This lag is due to the unique problems encountered in process industries caused mainly by the handling of non-discrete materials, involving production yield variability, sequencing and the existence of co-products links.

To underline the differences between process industry and discrete manufacturing, Crama et al. Products are commonly defined by or identified with the succession of production steps which they undergo. On the contrary, routing information is usually disregarded until the scheduling phase in discrete manufacturing industries. They usually fall into two distinct classes, namely main materials and auxiliary or secondary materials.

Efficient management of the main raw materials is often a top priority in process industries. While there is no universal definition of the "co-product" term, it is generally agreed that co-products are desirable, planned products produced by the same process. Processes resulting in co- products are also called multiple-output or joint production processes see Wonderware, Examples of such processes are shown in Table 2. State nodes represent materials inputs and task nodes represent process activities.

The definition of the STN assumes fixed proportion among several process inputs and outputs. This assumption disregards the fact of yield variability in the process industry. Although the STN definition is provided as an input to the production planning processes, its application in computerized production planning systems is complex Wonderware They developed a lot-sizing model for the co-production case with stochastic process yield.

However, mathematical complexity remains a limit to the practical use of their model. The above mentioned facts are common for most of the process industries.

However, it is important to differentiate between different types of process industries when implementing production planning and inventory control systems. Two of theses variables concern the production planning decisions: materials requirements and capacity requirements variables.

They pointed out that many of the firms considered to be process industries, are actually hybrids. This is due to the fact that the non-discrete products of process industries become discrete at some point during the manufacturing process. For example, non-discrete products are usually packed into different packaging forms and brands as observed in food processing industries.

The first strategy is concerned with developing rules to distinguish the MTS and the MTO stages of a production system. Van Donk used the Decoupling Point DP approach to distinguish such stages; by locating the DP, the upstream production activities are typical make-to-stock. Such production activities can be optimized based on demand forecasts.

The downstream production activities are typical discrete, make-to-order activities. Another methodology is developed by Ramachandran, K. Their methodology is based on three criteria, the customization point, the bottleneck operation, and the ABC classification of items. A solution methodology is proposed by Xiong and Nyberg to manage a single product, multi-stage production facility.

The concept is to apply the MRP-II method for planning initial material input and to apply the Kanban control method for controlling mid-product differentiation. The third strategy deals with the case of multiple products, single stage production system with no common component. They used the model to allocate the base stock inventory capacity among products within a specific service window. The strategy is used to coordinate production capacity and inventory.

Rajagopalan developed a heuristic to solve a non-linear integer programming model that addresses a multiple items, limited capacity system with setup times.

Although different approaches are used to control the production of hybrid MTO-MTS systems, all of the approaches have a common target; lower inventory cost and better customer service.

This objective is achieved by integrating production planning and inventory control decisions. Typical production stages are illustrated in Fig. The line is composed of three units: preparation and extraction, refining and packaging. Although existing in the same facility, the production stages are distinct in terms of production process type. The first two stages are typical process flow lines while the packaging stage is a batch flow line.

The preparation and extraction unit produces crude oil as the main-product and meals as co-products. Co-products are packed and sold. Crude oil is further processed by the refining unit.

Refined oil is sold in bulk, mixed then packed, or packed directly into finished products. Also, it is important to incorporate th demand for co-products during the production planning stage in order to increase profits.

As discussed earlier in process industries production planning and inventory control decisions require a special type of decision support tools. Unfortunately, this is not provided by standard ERP systems.

MRP, and the production planning systems based on it, is an offspring of the traditional BOM concepts for discrete manufacturing. As a result, standard ERP systems fail to incorporate features of process industries. Few ERP vendors provide process ERP systems and fewer incorporate optimization techniques for production planning in such environments as discussed in Wonderware and Jacobs and Bendoly In this paper an integrated production planning and inventory control model is developed.

Th emodel is best suited for the food processing industry. The model inputs are the aggregate demand forecast for discrete end-products. Considerations of co-production are incorporated in the model resulting in optimized schedule for all outputs.

The rest of the paper is organized as follows: section 2 describes problem definition and model formulation. Section 4 states recommended future work and conclusion is stated in section 5. Problem Definition and the Production Planning Model The proposed model considers a single line, multiple products, two stages production system.

Demand is time varying for all products. Products yield factors, and the production line capacity are fixed. Changeovers, setup times and costs are not considered at this level of planning.

The model incorporates the issue of multiple outputs. Holding cost is assumed to be a constant charge per unit of any product. The backordering charge depends on the type of co-product and the time period. This cost parameter is used to imply customer orders priorities. For example, export and strategic customer orders are assigned a huge backordering cost to enforce delivery commitment. Total of input quantities equals the sum of quantities processed by each route.

This relation is illustrated by constraint 2. Each product has a yield factor which is defined as the quantity of raw material required to produce one unit of the product. The relation between raw materials, products, and yield factors is described by equation 3 and 4.

This condition is expressed by constraint 5. In the second stage, the main-product output of stage 1 is further processed in order to produce the end-products Mit. A minimum batch quantity of the main-product should be produced before proceeding to stage 2. Constraint 5 ensures that the minimum batch quantity of stage 2 is considered.

However, backorders are allowed for co-products. Constraint 7 guarantees that co-products demand is met during the planning horizon. Constraints 8 to 14 represent the product quantity balance in each period for main-product, end-product and co-products respectively. Also, quantities of outputs, main-product and end-product, are limited by WIP storage capacities as shown in constraint 17 and Thus, the resultant optimum Master Production Schedule MPS provides the quantities of raw materials to be processed in each period by a certain production route.

The combined MTO-MTS planning strategy: As shown in section 2, the proposed linear programming model is used to determine production quantities of a two-stage production line. Each type of product package, i. Packages are classified as make-to-stock and make-to-order items according to demand characteristics. The ELSP approach addresses the scheduling problem of multiple items competing for capacity of a single machine.

The basic assumptions of the problem are that demand is constant and continuous for each product, no backordering, production rate is constant, only one product can be produced at a time, setups are sequence independent and carrying cost rate is constant. Several search procedures are used in literature.

Make to Order Vs Make to Stock: Key Differences

Presents empirical evidence which has been collected from 22 companies in three European countries — the UK, Denmark and The Netherlands. Finally, two new labels for this sector of industry are proposed. Amaro, G. Report bugs here. Please share your general feedback. You can join in the discussion by joining the community or logging in here.

Since accuracy of the forecasts will prevent excess inventory and opportunity loss due to stockout, the issue here is how to forecast demands accurately. MTS Make to Stock literally means to manufacture products for stock based on demand forecasts, which can be regarded as push-type production. MTS has been required to prevent opportunity loss due to stockout and minimize excess inventory using accurate forecasts. In the industrialized society of mass production and mass marketing, this forecast mass production urged standardization and efficient business management such as cost reduction. As an economy expands, the income of consumers increases and so demand also continuously increases.


PDF | We study the optimality of make-to-order (MTO) versus make-to-stock (MTS​) policies for a company producing multiple heterogeneous.


Make-to-order versus make-to-stock in a production-inventory system with general production times

Make to order MTO and make to stock MTS are both implemented by different companies, so please sit back and get ready for the main event: Make to order vs Make to stock , which is best for scaling manufacturers? You may discover that your business is more of a hybrid, and depending on circumstances, can switch processes for whenever your company needs to evolve to cope with the demand. Join us as we uncover the advantages and disadvantages if you employ one of these lean manufacturing tactics in your business. Make to order MTO is the process of products manufactured upon a business receiving a customer's order. A make to order business also applies to companies that sell products that are built to order, such as a bespoke manufacturing company.

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI:

We consider a single-stage multiproduct manufacturing facility producing several end-products for delivery to customers with a required customer lead-time.

A Make to Order Example

 - Вы оба. - При всем моем уважении к вам, сэр, - сказала Мидж, - я бы порекомендовала послать в шифровалку бригаду службы безопасности - просто чтобы убедиться… - Ничего подобного мы делать не будем. На этом Мидж капитулировала: - Хорошо. Доброй ночи.  - Она двинулась к двери.

 Извините, сэр… Бринкерхофф уже шел к двери, но Мидж точно прилипла к месту. - Я с вами попрощался, мисс Милкен, - холодно сказал Фонтейн.  - Я вас ни в чем не виню. - Но, сэр… - заикаясь выдавила.  - Я… я протестую. Я думаю… - Вы протестуете? - переспросил директор и поставил на стол чашечку с кофе.

Сначала он предназначался для использования в ходе избирательных кампаний как способ создания в режиме реального времени моделей данной политической среды. Загруженная громадным количеством информации программа создавала паутину относительных величин - гипотетическую модель взаимодействия политических переменных, включая известных политиков, их штабы, личные взаимоотношения, острые проблемы, мотивации, отягощенные такими факторами, как секс, этническая принадлежность, деньги и власть. Пользователь имел возможность создать любую гипотетическую ситуацию, и Мозговой штурм предсказывал, как эта ситуация повлияет на среду. Коммандер относился к этой программе с религиозным трепетом, но использовал ее не в политических целях: она служила ему для расчета времени, оценки информации и схематического отображения ситуации, выработки сложных стратегических решений и своевременного выявления слабых мест. Сьюзан не оставляло подозрение, что в компьютере шефа кроется нечто, чему в один прекрасный день суждено изменить весь мир. Да, я была с ним слишком сурова, - подумала Сьюзан. Ее мысли были прерваны внезапным звуковым сигналом входной двери Третьего узла.

0 Comments