Barış Tan

Production and Subcontracting Strategies for Manufacturers with Limited Capacity and Volatile Demand

We study a manufacturing firm that builds a product to stock to meet a random demand. If there is a positive surplus of finished goods, customers make their purchases without delay and leave. If there is a backlog, the customers are sensitive to the quoted lead time and some choose not to order if they feel that the lead time is excessive. A set of subcontractors, who have different costs and capacities, are available to supplement the firms production capacity. We derive a feedback policy that determines the production rate and the rate at which the subcontractors are requested to deliver products. The performance of the system, when it is managed according to this policy, is evaluated.

Variance of the throughput of an N-station production line with no intermediate buffers and time dependent failures

In this study, the variance of the throughput of an N-station production line with no intermediate buffers and time dependent failures is analytically determined. Time to failure and time to repair distributions are assumed to be exponential. The analytical method yields a closed-form expression for the variance of the throughput. The method is based on determining the limiting variance of the total residence (sojourn) time in a specific state of an irreducible recurrent Markov process from the probability of visiting that state at time t given an initial state. This probability function is the instantaneous availability of a production system in the reliability context. A production line with no interstation buffers and time-dependent failures is basically a series system with hot standby. The same procedure can be applied to determine the variance of the throughputs of various arrangements of workstations including series, parallel, series-parallel systems provided that the instantaneous availabilities of these systems can be written explicitly. Numerical experiments show that, although the expected throughput decreases monotonically, the variance of the throughput may increase and then decrease as the number of stations in the line increases depending on the system parameters. Numerical experiments that investigate this phenomenon and also the dependence of the coefficient of variation on the number of stations are also presented in this study.

Managing manufacturing risks by using capacity options

In this study, we investigate the strategy of increasing production capacity temporarily through contingent contractual agreements with short-cycle manufacturers to manage the risks associated with demand volatility. We view all these agreements as capacity options. More specifically, we consider a manufacturing company that produces a replenishment product that is sold at a retailer. The demand for the product switches randomly between a high level and a low level. The production system has enough capacity to meet the demand in the long run. However, when the demand is high, it does not have enough capacity to meet the instantaneous demand and thus has to produce to stock in advance. Alternatively, a contractual agreement with a short-cycle manufacturer can be made. This option gives the right to receive additional production capacity when needed. There is a fixed cost to purchase this option for a period of time and, if the option is exercised, there is an additional per unit exercise price which corresponds to the cost of the goods produced at the short-cycle manufacturer. We formulate the problem as a stochastic optimal control problem and analyse it analytically. By comparing the costs between two cases where the contract with the short-cycle manufacturer is used or not, the value of this option is evaluated. Furthermore, the effect of demand variability on this contract is investigated.

Modeling and analysis of an auction-based logistics market

We consider a logistics spot market where the transportation orders from a number of firms are matched with two types of carriers through a reverse auction. In the spot market, local carriers compete with in-transit carriers that have lower costs. In order to analyze the effects of implementing a logistics spot market on these three parties: firms, local carriers, and in-transit carriers and also the effects of various system parameters, we develop a two-stage stochastic model. We first model the auction in a static setting and determine the expected auction price based on the number of carriers engaging in the auction and their cost distributions. We then develop a continuous-time Markov chain model to evaluate the performance of the system in a dynamic setting with random arrivals and possible abandonment of orders and carriers. By combining these two models, we evaluate the performance measures such as the expected auction price, price paid to the carriers, distribution of orders between local and in-transit carriers, and expected number of carriers and orders waiting at the logistics center in the long run. We present analytical and computational results related to the performance of the system and discuss operation of such a logistics spot market in Turkey.

A multiperiod stochastic production planning and sourcing problem with service level constraints

We study a stochastic multiperiod production planning and sourcing problem of a manufacturer with a number of plants and/or subcontractors. Each source, i.e. each plant and subcontractor, has a different production cost, capacity, and lead time. The manufacturer has to meet the demand for different products according to the service level requirements set by its customers. The demand for each product in each period is random. We present a methodology that a manufacturer can utilize to make its production and sourcing decisions, i.e., to decide how much to produce, when to produce, where to produce, how much inventory to carry, etc. This methodology is based on a mathematical programming approach. The randomness in demand and related probabilistic service level constraints are integrated in a deterministic mathematical program by adding a number of additional linear constraints. Using a rolling horizon approach that solves the deterministic equivalent problem based on the available data at each time period yields an approximate solution to the original dynamic problem. We show that this approach yields the same result as the base stock policy for a single plant with stationary demand. For a system with dual sources, we show that the results obtained from solving the deterministic equivalent model on a rolling horizon gives similar results to a threshold subcontracting policy.

Modelling and analysis of Markovian continuous flow systems with a finite buffer

In this study, a Markovian fluid flow system with two stages separated by a finite buffer is considered. Fluid flow models have been analyzed extensively to evaluate the performance of production, computer, and telecommunication systems. Recently, we developed a methodology to analyze general Markovian continuous flow systems with a finite buffer. The flexibility of this methodology allows us to analyze a wide range of systems by specifying the transition rates and the flow rates associated with each state of each stage. In this study, in order to demonstrate the applicability of our methodology, we model and analyze a range of models studied in the literature. The examples we analyze as special cases of our general model include systems with phase-type failure and repair-time distributions, systems with machines that have multiple up and down states, and systems with multiple unreliable machines in series or parallel in each stage. For each case, the Markovian model is developed, the transition and flow rates are determined, and representative numerical results are obtained by using our methodology.

Effects of variability on the due-time performance of a continuous materials flow production system in series

Abstract In this study, we consider a specific continuous materials flow production system with N unreliable stations in series and no interstation buffers. Processing times of the stations are deterministic and identical. We assume that the failures are time-dependent and the time to failure and time to repair distributions are exponential and two-phase balanced coxian ( C 2: b ), respectively. The effects of repair time variability on the performance of the production system are investigated by changing the parameters of C 2: b distribution. We obtained closed-form expressions for the asymptotic mean and variance of the amount of materials produced in this production system. It is shown that the amount of materials produced in a fixed time interval is normal as time approaches infinity. The asymptotic distribution of the amount of materials produced is used to derive the probability of meeting a customer order on time. We used this probability to evaluate the due-time performance of the production system. Numerical experiments that investigate some relationships among the performance measures and the system parameters are also presented.

Production control of a pull system with production and demand uncertainty

We consider a continuous material-flow manufacturing system with an unreliable production system and a variable demand source which switches randomly between zero and a maximum level. The failure and repair times of the production system and the switching times of the demand source are assumed to be exponentially distributed random variables. The optimal production flow control policy that minimizes the expected average inventory carrying and backlog costs is characterized as a double-hedging policy. The optimal hedging levels are determined analytically by minimizing the closed-form expression of the cost function. We investigate two approximate single hedging policies. It is empirically shown that an approximate policy that uses a single hedging level which is the sum of a production uncertainty term and a demand uncertainty term gives accurate results for the expected average cost.

Asymptotic variance rate of the output in production lines with finite buffers

Production systems that can be modeled as discrete time Markov chains are considered. A state‐space‐based method is developed to determine the variance of the number of parts produced per unit time in the long run. This quantity is also referred to as the asymptotic variance rate. The block tridiagonal structure of the probability matrix of a general two‐station production line with a finite buffer is exploited and a recursive method based on matrix geometric solution is used to determine the asymptotic variance rate of the output. This new method is computationally very efficient and yields a thousand‐fold improvement in the number of operations over the existing methods. Numerical experiments that examine the effects of system parameters on the variability of the performance of a production line are presented. The computational efficiency of the method is also investigated. Application of this method to longer lines is discussed and exact results for a three‐station production line with finite interstation buffers are presented. A thorough review of the pertinent literature is also given.

Agricultural planning of annual plants under demand, maturation, harvest, and yield risk

In this study we present a planning methodology for a firm whose objective is to match the random supply of annual premium fruits and vegetables from a number of contracted farms and the random demand from the retailers during the planning period. The supply uncertainty is due to the uncertainty of the maturation time, harvest time, and yield. The demand uncertainty is the uncertainty of weekly demand from the retailers. We provide a planning methodology to determine the farm areas and the seeding times for annual plants that survive for only one growing season in such a way that the expected total profit is maximized. Both the single period and the multi period cases are analyzed depending on the type of the plant. The performance of the solution methodology is evaluated by using numerical experiments. These experiments show that the proposed methodology matches random supply and random demand in a very effective way and improves the expected profit substantially compared to the planning approaches where the uncertainties are not taken into consideration.