What are the most common decision variables for aggregate planning in a manufacturing setting

21.1 What is aggregate planning?

Aggregate planning is an intermediate-term planning function. It is the process of planning the quantity and timing of output over an intermediate time of, say, from 3 months to 1 year. Within this range, the physical facilities are assumed to be fixed for the planning period. Therefore, fluctuations in demand must be met by varying labor and inventory schedules. Aggregate planning seeks the best combination to minimize costs by matching the supply and demand of output over the medium time range, generally for the next 12-month period. It tells management exactly when and in what quantum the materials and other resources are to be procured to ensure that the total cost of operations of the organization is kept to the minimum. According to Wikipedia, the term aggregate indicates that planning is done for a single overall measure of output or, at the most, a few aggregated product categories.

In other words, aggregate planning is the matching of the plant capacity with demand in such a way that production costs are minimized.

V Materials Requirement Planning, JIT, and KANBAN

Aggregate planning creates master production schedules for finished products. The objective of MRP is to translate those schedules into purchasing and production orders for the entire facility. The material requirements planning system also indicates material and capacity needs for each work center. This system is frequently called MRP-I to distinguish it from a more complex procedure called manufacturing resource planning, or MRP-II. We shall first discuss the MRP-I concept and then show how it is the heart of the MRP-II system.

MRP-I is best presented using a simple product component breakdown, as shown in Fig. 4. This product is a toy circus wagon and the diagram is called a product structure tree. Note that a wheel-axle subassembly is a component of the finished wagon. The numbers in parentheses indicate the quantity of each subcomponent required in the higher level assembly.

In addition to the product structure tree, MRP-I requires lead time values for each component and a master production schedule for the finished product. Lead time is the time needed to supply some quantity of the component. Suppose, for example, the toy wagon requires 1 day for final assembly while the wheel-axle requires 2 days to assemble and the wheels require 2 days to produce. Suppose 500 wagons are required by the master schedule. It follows that 2000 wheels must be scheduled for production at least 5 days before the completed wagons are due. If, however, there are wheels and wheel-axle assemblies in inventory, more wheels may not be needed. The system must take subcomponent inventory into consideration as it calculates the need for each part.

So far, we have considered a very simple product. Complex products like automobiles have thousands of components and a product structure tree that has many levels. When problems occur in production or with vendor deliveries, schedules need to be adjusted to minimize disruption on the shop floor. Even though MRP calculations are simple, realistic systems require large databases and long run times to keep track of the dynamic production environment. A typical MRP report is given in Fig. 5.

MRP-II stands for manufacturing resource planning and is a system that integrates the MRP-I, marketing, and financial systems with a factory simulator to form a strategic planning tool. MRP-II can be used to study alternative resource levels and market strategies relative to their manufacturing and financial feasibility. Hypothetical marketing strategies are evaluated to determine the possible future demand for various products. These demand estimates are fed through the MRP-I system to determine corresponding requirements at the various work centers. The factory simulator can then be used to determine bottlenecks and idle capacity. If necessary, production capacity can be adjusted and new simulations run. Once a satisfactory capacity level is reached, the financial system is used to estimate the dollar impact of the hypothetical market strategy. In this way, the most cost-effective manufacturing resource plan can be established.

JIT is an acronym for just in time, a philosophy which has the simple objective of having just the right amount of materials available at just the right time. The idea is to carry no more inventory than is absolutely necessary. To make JIT work, several things need to happen. Production levels have to be held constant for weeks at a time. Quality has to be superb so that scrap or rework never hold things up. Machines have to be arranged so that work moves quickly from one machine to the next. Preventive maintenance has to be regularly performed so that machines rarely break down. Tooling has to be redesigned so that changing over from one part to the next can be done very quickly. Suppliers have to be kept well informed so that material arrives exactly as planned. For JIT to happen, the focus of management must change from “get it done any way you can” to “do it right the first time.” In other words, material flows quickly, smoothly, and easily through the factory.

KANBAN is a term often associated with JIT. KANBAN means “card” in Japanese and connotes a manual system as opposed to the computerized MRP-I approach to materials management. MRP-I starts with a master schedule for finished products and generates the workorders needed to meet that schedule. KANBAN uses the schedule to drive only the last manufacturing step. Between each step, a small amount of inventory is maintained in a bin or on a cart. A token, or KANBAN, is attached to the bin. When the inventory is taken for use, the token is removed and sent to the upstream supplier. The supplier refills the bins for which he or she has cards. As few KANBANs as possible are used to maintain the smooth flow of work. In this way, material is held to a minimum and generated only as needed.

Refineries

The MEFS refineries submodel is a simplified, aggregate-planning simulation that represents the conversion of crude oils (both domestic and imported) into seven major refined products: naphtha, gasoline, jet fuel, distillate, residual, liquefied petroleum gases, and other. Crude oils processed by refineries differ in physical and chemical characteristics, and consequently must be processed differently; processing costs vary among crudes and each crude produces a different mix of products. The MEFS refineries submodel differentiates crude oils by characteristics such as specific gravity and sulfur content. The refineries submodel also can distinguish approximately 25 different domestic and imported types of crude oil.

The MEFS refinery submodel represents the characteristics of existing refinery capacity by calibrating and adjusting the model to simulate the refinery configuration that is necessary to meet demand. Provision also is made for modeling the expansion of refinery capacity by providing for construction of new facilities. As with utilities, the inclusion of new capacity requires that capital expenditures be made. In MEFS, these costs are annualized capital charges for constructing new refinery capacity.

The MEFS simulation selects and transports specific crude types to refinery regions, specifies necessary capacity expansion, and produces and transports refined products to the consumers in a way that minimizes the refiner's costs. Final product prices are determined by iteration with a detailed process model as demand levels fluctuate in response to prices. The refinery regions used by MEFS are the five Petroleum Administration for Defense (PAD) districts, with PAD districts 1 and 2 divided into two regions, as depicted in Fig. 9. Within MEFS, crude oil is transported into the refinery regions from the oil production or import regions, and refined products are transported from the refinery regions to the utility or demand regions by pipeline, barge, or tanker.

3.3 MILP Model

The formulation developed in this work is an aggregate planning model based on the RTN (Resource-Task Network) process representation, as introduced by Pantelides (1994). All material requirements as well as all storage levels and production yields are precisely defined through the model parameters and decision variables. However, the detailed time and task-sequencing constraints are not modeled. The optimal plan will be determined considering that production resources are shared by products under development and products already in commercialization. The formulation is defined by the following main constraints: (i) resource balance constraints, to determine the materials availability over time (intermediates and final products); (ii) capacity constraints, to set the minimum and maximum boundaries for resources availability; (iii) demand constraints, to define the production requirements; (iv) batch size constraints, for ensuring that the total amount of material processed is within the capacities of the units; and (v) plant capacity constraints, to express the time availability of the processing units, including the possibility to expand capacity. The objective function is the maximization of the profit, as defined by expression (1) (i.e. the total income over the time horizon, minus operational costs, investment costs (associated with the increase in capacity), storage costs, changeover costs, costs associated with the selection of processing units, and costs associated with missing deliveries).

(1)max∑m∈P∑t∈TΠmtsm− ∑k∈Ke∑t∈Tc koper.ξk,t−∑e∈E∑t∈Tceinvest.δet −∑m∈Mt∑t∈Tcmstor.Rmt −∑e∈E∑p∈P∑t∈Tcechg.Ypet−∑m∈P∑t∈TαpΠmt slack

The key decision variables of the proposed model are: (i) selection of processing units - binary variables Ypet (Ypet = 1 if product p is assigned to equipment e, in time interval t); (ii) when and how much capacity to expand - integer variables δet; and (iii) total amount produced in each processing unit - continuous variables ζkt.

23.1 Introduction

We have seen in the earlier chapters that after aggregate planning, the next step in production planning is the preparation of master production schedules (MPSs). A MPS is a translation of the production planning into schedule charts and details. It expresses the overall plans in terms of specific end items or models that can be assigned priorities. MPS is meticulously drawn up, after the planning stage, to determine when specific products groups will be made, when customer orders will be filled, and what manufacturing capacity is still available for new customer demand. It provides the basic foundation for

Planning for the material and capacity requirements,

Making good use of manufacturing resources,

Making customer delivery promises,

Resolving tradeoffs between sales and manufacturing and

Attaining strategic objectives in the sales and operations plan.

It forms a key link in the manufacturing planning and control interfacing with marketing, distribution planning, production planning, and capacity planning.

19.3.1 Supply Chain with Pinch Analysis

Singhvi and Shenoy (2002) presented a novel extension of the targeting methods from Pinch Analysis to aggregate planning in supply chains. This method is then further enhanced with larger case study by Singhvi et al. (2004). Aggregate planning aims at meeting demand over a specified time horizon in a way that maximises profit through optimal levels of production, capacity, subcontracting, inventory and stock-outs.

A schematic diagram for aggregate planning in a supply chain is shown in Fig. 19.3. Dk represents the demand forecast for each period tk in a planning horizon that extends over T time periods, maximising the profit over the specified time horizon. Other symbols shown in Fig. 19.3 are: production rate, Pk, number of units produced in-house in time period tk; subcontract, Ck, number of units subcontracted (outsourced) in time period tk; inventory, Ik, inventory at the end of time period tk; and, stock-out, Sk, number of units stocked out (backlogged) at the end of time period tk.

These variables are plotted in demand and production composites (Fig. 19.4) on a time vs material quantity plot (Singhvi and Shenoy, 2002). A simple balance of the flow of materials at time tk in a particular stage of the supply chain with I0 as the initial inventory can be written as:

[19.1]I0+Pk+Ck=Dk+Ik

As shown in Fig. 19.4, the Demand Composite Curve D(tk) is simply a plot of the cumulative demand and needs to be matched by a supply Composite Curve P(tk). Based on the fundamental principle of material balance, the demand has to be met by products supplied either by in-house production or outsourcing.

Singhvi et al. (2004) noted that the vertical difference between the demand and supply composites is the lead time, which is the time interval between producing an order and servicing the demand. The point where P(tk – T) = D(tk) is the Pinch. The minimum lead time separates the two composite curves. Compared to the original Heat Pinch Graph, minimum lead time is analogous to the Minimum Temperature Driving Force (∆Tmin) in a heat recovery system. When T = 0, the Pinch will be the point where P(tk) = D(tk). The horizontal distance between the two Composite Curves at any given time gives the total inventory in the system. The Pinch is defined as the point of minimum inventory. The area between the two Composite Curves gives the measure of inventory in the system, which when multiplied by the inventory holding cost factor provides the actual inventory cost.

This work is then extended by Foo et al. (2008). The concept of supply chain cascade analysis is illustrated in Fig. 19.5.

27.12 Production planning and control systems and formats

The four formats cited in Section 27.3 form the basis for the subsequent PP&C activities like product sequencing, routing, and scheduling. The subsequent sections indicate the systems and formats used by the planning department for the actual PP&C activities.

27.12.2 Monthly production planning

On the 25th of every month the production targets for the next month will be prepared based on the sales targets statement and physical stock statement as on 24th and the excepted production and dispatches during the last few days of the month (Fig. 27.7). All the production shops are given the above-detailed assembly-wise program on the first of every month.

Figure 27.7. Monthly production planning chart.

Planning for the press shop: Since the press shop has to work on batch production while the other shops in general have continuous flow, the planning of the machine loading for each press is more critical. The batch quantity for each item may be decided depending upon the inventory carrying costs and setup costs. However, initially we will decide subjectively the batch size between 5000 and 6000, which will be confirmed later by experience.

22.7.1 Route sheet

Route sheets, also called sequence cards, detail the sequence of events and necessary materials for component production. They are more useful for planning than for tracking.

Routing is done at two different levels. At the aggregate planning level, the route card or the process planning layout is used, while at the machine loading level the route sheet used; each of these are further explained later.

The product forms the basis for the route sheets.

Here, the number of various parts to be produced in order to complete the product is given by routing.

The department they have to be worked on and the time for completing each product in each department is also indicated.

This is also called process planning layout (as in Rallis group) or quality process chart (as in Lucas TVS).

Please refer to Fig. 27.9, which illustrates the process planning layout or the route sheet.

2. INTEGRATING CAPACITY PLANNING AND LINE DESIGN WITH PRODUCTION PLANNING

Capacity planning is usually motivated by reducing operation costs, meeting market demand fluctuation and/or pursuing better customer service. The major decisions in current capacity planning literature are machine replacement decisions and capacity expansion decisions. In machine replacement literature [2], “demand for capacity is generally expressed in equipment units” [3]. An example of this class is a firm such as United Parcel Service (UPS), which delivers packages of various sizes across the world, planning to replace and possibly expand part of its large vehicle fleet with demand measured in number of vehicles. In the capacity expansion literature, demand for capacity is usually expressed in output units [3–8]. An example of this scenario is a fertilizer plant that could be built in one of several sizes, with demand measured in thousands of pounds of fertilizer. In these literatures, capacity is treated as discrete or continuous variable, but structure of products (bill of component) is not addressed.

From the point of view of a production planning and control (PPC) hierarchy [9], capacity/facility planning based on demand forecasted works as the main input of all other production planning issues, e.g., aggregate planning, demand management, sequencing or scheduling and shop floor control as they are shown in Figure 1. In Figure 1, each rectangular box represents a separate decision problem. The ovals represent inputs to decision problems that are generated outside the planning hierarchy.

The PPC hierarchy of Figure 1 is divided into three basic levels, corresponding to long-term (aggregate level), intermediate-term (disaggregate level), and short-term (control level) planning. The basic function of the aggregate planning is to establish a production environment capable of meeting a firm's overall goals. This begins with a forecasting of future demand based on marketing information. Capacity planning uses these demand forecasts, along with descriptions of process requirements for making the various products, to generate capacity plan. Aggregate planning makes rough predictions about future production mix and volume according to demand forecast and capacity plan.

Taking plans from the aggregate level, disaggregate planning generates a general plan of action that will help the firm prepare for upcoming production. The WIP/quota setting problem decides the quantity of work in process or periodic production quotas according to aggregate plan and capacity's characteristics (e.g., setup time, changeover time among batches, production process type etc.). Based on orders on hand and demand forecasts, the planner prepares a budget master production schedule. The shop floor control problem controls the real-time flow of material through the plant in accordance with the work schedule, while the production tracking measures actual progress against the schedule.

As shown in Figure 1, production and process parameters are usually treated as inputs outside of the planning hierarchy. In the face of shorter product lifecycles, higher product variety, increasingly unpredictable demand, and shorter delivery times, manufacturing facilities dedicated to a single product line cannot be effective any longer. Investment efficiency now requires that manufacturing facilities be able to shift quickly from one product line to another without major retooling, resource reconfiguration, or replacement of equipment. Investment efficiency also requires that manufacturing facilities be able to simultaneously make several products mixes and volumes can be more easily accommodated.

All these requirements can be interpreted two kinds of suggestions: increase manufacturing flexibility, integrate capacity planning and line design with production planning. Many researches have suggested that a firm must link its technology choice to its total manufacturing strategy and business unit's goals to gain competitive advantage [10], and that flexible manufacturing system is desirable to response to a wide variety of future demand [11–12]. Manufacturing flexibility does not only depend on machine flexibility, but is also constrained by decisions made at system design stage and planning decisions made during preproduction setup [13]. Thus to accomplish manufacturing agility, capacity planning (decide capacity in equipment units or output units) and production line design (decide production line structure relative to products’ structure and processes’ requirements) should be integrated with production planning. Specifications of products and process are desirable to be considered in capacity planning. In shorter, the decision of product and process parameters should included in the PPC hierarchy as a decision module as shown in Figure 2.

Figure 2 is different from Figure 1 mainly on two aspects: 1) product and process parameters are not exogenous; 2) the decision method that first decide aggregate planning and then take it as the input of disaggregate planning is not necessary or sufficient for agile manufacturing. Rather, aggregate and disaggregate planning decisions are tightly correlated and should be integrated to attain better customer responsiveness and business goals.

Integrating aggregate capacity planning with disaggregate production planning is usually a difficult problem. Yet for flexible manufacturing systems (FMS), there still are some researches on integrating these two levels of planning problem, for example, [14–16]. While their results are interesting and insightful, machine flexibility and the configuration of production capacity are not incorporated in their models. Benjaafar and Gupta [17] developed a model to explicitly express the number of facilities, the number of products assigned to each facility and their corresponding capacities. Eppen and Martin and Shrage [4] proposed a scenario based approach for capacity planning, which integrated the overall level of capacity, the type of facility (e.g., the level of flexibility) and production assignment. Although product parameters are not explicitly involved in their models, their researches can be viewed as initial attempts of integration of aggregate and disaggregate planning.

4.1. Major Requirements and Activities

The goals of contingency-driving shop floor control are to develop and demonstrate a monitoring and decision support concept for improving planning and control of manufacturing resources and materials more efficiently.

Several effective manufacturing systems that have evolved with the evolution of computer technology, such as CIM, CALS, Virtual Manufacturing and philosophical techniques such as Lean or Agile manufacturing are firmly based in the fundamentals defined in the model for a production management system which comprises the basic concepts of planning, controlling, measuring, and evaluating. Effective planning should be future-oriented and flexible. Well-designed and long-range robust planning, in most all cases, is basically necessary for developing an effective production control system when considering the situational contingencies affecting.

The concepts of contingency-driving shop floor control is based on the need to coordinate the plans and control all the way from customer order to shipping products. Successful control process is a matter of establishing a good plan and adhering to it. Thus, good aggregate planning and cellular layout design are a prerequisite to good shop floor control. Key to the operation being successful are accurate forecasting, aggregate planning, inventory control, scheduling and gathering of shop floor status data to allow revision of plans as the status of conditions changes in the shop floor.

Some attempts were made to improve production planning and control or logistic plans by applying computer systems [27, 28, 29, 30, 31]. They provided an overview of current research projects and a commercial system for shop floor control system as well as the state-of-the-art survey. Although both predictive layout design based planned orders and reactive planning for random orders from customers are successfully achieved at strategic planning stage, shop floor control is most significant production activity to provide trend and performance information to management relating to daily quality and cost status, performance to schedule, dispatching effectiveness, and material flow fitness between the various machining cells. A contingency-driving shop floor control involves the actual execution of the monitoring of capacity and work in progress, and reaction to shop floor contingencies as far as possible within the limits of the original plans.

To increase the ability of reacting to shop floor contingencies, real-time information on the conditions of the shop floor will be analyzed to support continuous improvements for sustaining the optimal or near optimal factory-wide operating control in dynamic changing environment. The contingency-driving shop floor control operates through the repetition of the following five sequential functions: monitoring, data acquisition and analysis, diagnosis, simulation optimization, and periodic evaluation and performance measurement. These five functions evolving major shop floor should be integrated closely with long-range planning concepts to control quality, cost, and delivery by quickly spotting the cause of production defects.

In the simulation optimization function combining generic optimizes and simulation model, to be useful in supporting of contingency-driving reactive shop floor control, an alternative plan selected in diagnosis will be examined by all possible simulation parameters through the user interface. Finally periodic evaluation and performance measurement are needed to ensure or predict the consequences of implementation of suggested potential solutions.