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  • 1.
    Berling, Peter
    Lund University.
    Holding cost determination: An activity-based cost approach2008In: International Journal of Production Economics, ISSN 0925-5273, E-ISSN 1873-7579, Vol. 112, no 2, p. 829-840Article in journal (Refereed)
    Abstract [en]

    We consider the problem of choosing the holding cost in inventory models. Traditionally, the cost of holding inventory is assumed to increase linearly with a rate that is equal to a percentage of the product value. This since the capital cost is believed to make up the main part of the cost. However, recent research indicates that this is not necessarily the case.

    In the present work, we present a more general model of the cost of holding inventory based on a microeconomic framework. A method for determining a suitable holding cost per unit and time unit, h, which can be used in existing heuristics/formulas is derived. The method is based on the ideas behind activity-based costing (ABC).

    The suggested method works well in the considered numerical examples (maximum and average cost increase is 1.78% resp. 0.08%). There exist situations where the traditional approach, i.e., setting h as a percentage of the product value, gives rise to a significant cost increase (>15%).

  • 2.
    Berling, Peter
    Lund University.
    Real options valuation principle in the multi-period base-stock problem2008In: Omega: The International Journal of Management Science, ISSN 0305-0483, E-ISSN 1873-5274, Vol. 36, no 6, p. 1086-1095Article in journal (Refereed)
    Abstract [en]

    This paper analyzes the multi-period base-stock problem where there is a financial risk associated with a stochastic demand. For the single-period problem, it is known that the optimal inventory policy can be obtained with the Black and Scholes option pricing formula. This paper pushes the analysis further by applying the options valuation framework to the multi-period problem and presenting an algorithm for finding the optimal inventory policy. A computational study indicates that the effect of systematic risk is typically negligible (as for the single-period problem). Therefore, it can be concluded that systematic risk in demand is of little importance for optimal inventory control.

  • 3.
    Berling, Peter
    Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics.
    The capital cost of holding inventory with stochastically mean-reverting purchase price2008In: European Journal of Operational Research, ISSN 0377-2217, E-ISSN 1872-6860, Vol. 186, no 2, p. 620-636Article in journal (Refereed)
    Abstract [en]

    Most models of inventory control assume that the per unit purchase price is constant. The capital cost of holding inventory can then be taken into account by adding a fixed interest rate, r, times the purchase price, C, to the out-of pocket holding cost. However, it is not uncommon that the purchase price varies over time. How the capital cost then should be calculated is the focus of the present paper. The paper studies the common single-item inventory model with a fixed set-up cost and assumes that the stochastic purchase price follows the mean-reverting Ornstein–Uhlenbeck process. Methods for computing an adjusted interest rate, r, are suggested along with modifications of well-known heuristics and formulas for lot-sizing. Simulation tests, where the optimal policy has been compared to policies obtained using modified versions of the Silver–Meal method, the Part Period algorithm and the EOQ formula, suggest that r should be estimated as the sum of the unadjusted interest rate and the average expected purchase price decrease, measured over a period between 1/3 and 2/3 of the length of the order cycle.

  • 4.
    Berling, Peter
    et al.
    Linnaeus University, School of Business and Economics, Department of Management Accounting and Logistics. Lund University.
    Eng-Larsson, Fredrik
    Stockholm University.
    Environmental implications of transport contract choice - capacity investment and pricing under volume and capacity contracts2017In: European Journal of Operational Research, ISSN 0377-2217, E-ISSN 1872-6860, Vol. 261, no 1, p. 129-142Article in journal (Refereed)
    Abstract [en]

    Inspired by the observation that capacity contracts are used by some retailers to increase their transport provider's investments in green transport solutions, we investigate and compare a service provider's optimal investment, and its environmental implications under a volume and a capacity contract respectively. We solve the service provider's investment problem under the assumption that the retailer uses the service to replenish a warehouse with storable goods. We then show that a capacity contract leads to more green transports, but not necessarily a larger investment in green transport solutions. At the same time, the optimal solution involves heavy investment in inventory at the retailer. The investment in inventory is non-decreasing in the cost benefit of the green transports, which may have a significant negative environmental impact. The implication is that a capacity contract will lead to better environmental performance than a volume contract only when the green transports' cost benefit is within a given interval. Whether the capacity contract is the more profitable option for the service provider within this interval depends on inventory related costs and the relative environmental costs from transportation and inventory. Interestingly, owing to this, regulation that target the price of the conventional vehicles, such as a carbon tax, may lead to both an increase or a decrease in environmental performance. (C) 2017 Elsevier B.V. All rights reserved.

  • 5.
    Berling, Peter
    et al.
    Linnaeus University, School of Business and Economics, Department of Management Accounting and Logistics. Lund University.
    Eng-Larsson, Fredrik
    Massachusetts Institute of Technology, USA.
    Pricing and timing of consolidated deliveries in the presence of an express alternative: financial and environmental analysis2016In: European Journal of Operational Research, ISSN 0377-2217, E-ISSN 1872-6860, Vol. 250, no 2, p. 590-601Article in journal (Refereed)
    Abstract [en]

    Shipment consolidation has been advocated by researchers and politicians as a means to reduce cost and improve environmental performance of logistics activities. This paper investigates consolidated transport solutions with a common shipment frequency. When a service provider designs such a solution for its customers, she faces a trade-off: to have the most time-sensitive customers join the consolidated solution, the frequency must be high, which makes it difficult to gather enough demand to reach the scale economies of the solution; but by not having the most time-sensitive customers join, there will be less demand per time unit, which also makes it difficult to reach the scale economies. In this paper we investigate the service provider’s pricing and timing problem and the environmental implications of the optimal policy. The service provider is responsible for multiple customers’ transports, and offers all customers two long-term contracts at two different prices: direct express delivery with immediate dispatch at full cost, or consolidated delivery at a given frequency at a reduced cost. It is shown that the optimal policy is largely driven by customer heterogeneity: limited heterogeneity in customers’ costs leads to very different optimal policies compared to large heterogeneity. We argue that the reason so many consolidation projects fail may be due to a strategic mismatch between heterogeneity and consolidation policy. We also show that even if the consolidated solution is implemented, it may lead to a larger environmental impact than direct deliveries due to inventory build-up or a higher-than-optimal frequency of the consolidated transport

  • 6.
    Berling, Peter
    et al.
    Linnaeus University, School of Business and Economics, Department of Management Accounting and Logistics.
    Farvid, Mojtaba
    Linnaeus University, Faculty of Technology, Department of Mechanical Engineering.
    Lead-time investigation and estimation in divergent supply chains2014In: International Journal of Production Economics, ISSN 0925-5273, E-ISSN 1873-7579, Vol. 157, no SI, p. 177-189Article in journal (Refereed)
    Abstract [en]

    This paper investigates the delay experienced by retailers in a distribution system due to shortages at the central warehouse. Simple formulae are developed to estimate the mean and variance of this delay. The formulae are based on replacing the stochastic lead-time demand with a stochastic demand rate, and they differ in how this demand rate is estimated. An extensive numerical study shows improved accuracy compared to existing methods with similar computational complexity. The numerical study also shows that the batch quantities and the service level have a large influence on the delay. Both the mean and variance of a retailer’s delay decrease with the service level and increase with the batch quantities used at the warehouse and at the retailer. No other variables seem to have a significant impact on the delay.

  • 7.
    Berling, Peter
    et al.
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics.
    Farvid, Mojtaba
    Linnaeus University, Faculty of Science and Engineering, School of Engineering.
    Ledtidsbestämning i distributionssystem: [ Lead-time determination in distribution systems ]2011In: Plans forsknings- och tillämpningskonferens 2011: logistik i praktisk tillämpning, Stockholm: Logistikföreningen , 2011, p. 205-216Conference paper (Refereed)
    Abstract [sv]

    I den här artikeln presenterar vi en lösning på problemet att skatta ledtiden förväntade värde och variation i ett distributionssystem. Det är ett väsentligt problem att lösa då en snabb och korrekt skattning av ledtiden är ett måste för att kunna dimensionera säkerhetslager rätt. Nyare forskning indikerar dessutom att man skall ha en förhållandevis låg servicenivå uppe på en centrallagernivå vilket accentuerar problemet då ledtiden då blir längre och mer osäker på lägre nivåer i försörjningskedjan. Detta då centrallagret oftare får brist vilket leder till mer frekventa och långvariga förseningar av utleveranserna.

    Vi angriper problemet med hjälp av matematiskt modellering varvid vi tar fram slutna uttryck för att beräkna väntevärdet så väl som variansen av förseningen ut från centrallagret. Denna metod utvärderas med hjälp av simuleringsstudier vilka indikerar på en god överenstämmelse med verkligheten.

  • 8.
    Berling, Peter
    et al.
    Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics.
    Forslund, HelenaVäxjö University, Faculty of Humanities and Social Sciences, School of Management and Economics.
    Effektiva och lönsamma försörjningskedjor: Konferenspublikation från Plans forsknings- och tillämpningskonferens i Växjö2009Conference proceedings (editor) (Other academic)
  • 9.
    Berling, Peter
    et al.
    Linnaeus University, School of Business and Economics, Department of Management Accounting and Logistics.
    Forslund, HelenaLinnaeus University, School of Business and Economics, Department of Management Accounting and Logistics.
    Proceedings från logistikföreningen Plans forsknings- och tillämpningskonferens2016Conference proceedings (editor) (Other academic)
  • 10.
    Berling, Peter
    et al.
    Lund University.
    Johan, Marklund
    Lund University.
    Heuristic Coordination of Decentralized Inventory Systems Using Induced Backorder Costs2006In: Production and operations management, ISSN 1059-1478, E-ISSN 1937-5956, Vol. 15, no 2, p. 294-311Article in journal (Refereed)
    Abstract [en]

    In this paper, we investigate a one-warehouse multiple-retailer system, where the inventory control decisions are coordinated using a near optimal induced backorder cost, β*. All installations use continuous review installation-stock (R, Q) policies. The analysis builds on an approximation model where the stochastic warehouse delays are replaced by their correct averages. The contributions include insights as to how β* is influenced by system parameters, and the determination of simple closed form β* estimates. The latter offering a practical means to achieve coordinated control of large size systems.

  • 11.
    Berling, Peter
    et al.
    Lund University.
    Kaj, Rosling
    Växjö University, Faculty of Mathematics/Science/Technology, School of Technology and Design.
    The Effects of Financial Risks on Inventory Policy2005In: Management science, ISSN 0025-1909, E-ISSN 1526-5501, Vol. 51, no 12, p. 1804-1816Article in journal (Refereed)
    Abstract [en]

    The effect of financial risks on (R, Q) inventory policies is analyzed in a real options framework. Simple adjustments of the usual formulas for R and Q are suggested and tested. Stochastic demand and purchase costs are considered, both with known systematic (business-cycle-related) risk.

    The systematic risk of stochastic demand has typically a negligible effect on the optimal values of R and Q, although an improvement may be achieved by a simple adjustment of R.

    The systematic risk of the purchase price, c, has a significant effect on R and Q. The capital holding cost should be estimated as r · c, where r is the sum of the risk-free interest rate, the expected price decrease, and the risk premium associated with the systematic risk of c.

    For goods quoted on commodity exchanges, r may be estimated directly from the prices on forward contracts. Its size (and sign) varies considerably for different commodities.

  • 12.
    Berling, Peter
    et al.
    Lund University.
    Marklund, Johan
    Lund University.
    A model for heuristic coordination of real life distribution inventory systems with lumpy demand2013In: European Journal of Operational Research, ISSN 0377-2217, E-ISSN 1872-6860, Vol. 230, no 3, p. 515-526Article in journal (Refereed)
    Abstract [en]

    This paper presents an approximation model for optimizing reorder points in one-warehouse N-retailerinventory systems subject to highly variable lumpy demand. The motivation for this work stems fromclose cooperation with a supply chain management software company, Syncron International, and oneof their customers, a global spare parts provider. The model heuristically coordinates the inventory systemusing a near optimal induced backorder cost at the central warehouse. This induced backorder costcaptures the impact that a reorder point decision at the warehouse has on the retailers’ costs, and decomposesthe multi-echelon problem into solving N + 1 single-echelon problems. The decomposition frameworkrenders a flexible model that is computationally and conceptually simple enough to beimplemented in practice.A numerical study, including real data from the case company, shows that the new model performsvery well in comparison to existing methods in the literature, and offers significant improvements tothe case company. With regards to the latter, the new model in general obtains realized service levelsmuch closer to target while reducing total inventory.

  • 13.
    Berling, Peter
    et al.
    Linnaeus University, School of Business and Economics, Department of Management Accounting and Logistics. Lund University.
    Marklund, Johan
    Lund University.
    Green Inventory Management2017In: Sustainable supply chains: a research-based textbook on operations and strategy / [ed] Yann Bouchery, Charles J. Corbett, Jan C. Fransoo & Tarkan Tan, Springer, 2017, 1, p. 189-218Chapter in book (Refereed)
    Abstract [en]

    Managing inventories, and thereby material flows, is of key importance for achieving efficient and sustainable supply chains. Green inventory management is characterized by complementing the traditional economic (cost) focus with environmental (emissions) considerations. In this chapter we identify and discuss key questions and challenges for green inventory management research. We do so by categorizing the costs and emissions of operating an inventory system into those associated with: ordering and transporting items, holding items in stock, and not satisfying customer demand on time. A literature overview illustrates what issues have been addressed so far in this emerging field. We conclude that there is a promising potential for green inventory management practices to reduce both costs and emissions, but much remains to be done. Not least in terms of developing more general green inventory models for practical use.

  • 14.
    Berling, Peter
    et al.
    Lund University.
    Marklund, Johan
    Lund University.
    Multi-echelon inventory control: an adjusted normal demand model for implementation in practice2014In: International Journal of Production Research, ISSN 0020-7543, E-ISSN 1366-588X, Vol. 52, no 11, p. 3331-3347Article in journal (Refereed)
    Abstract [en]

    This paper presents an approximation model for coordinated control of one-warehouse multiple-retailer inventory systems, where all locations use continuous review (RnQ) policies. The motivation stems from close collaboration with a supply chain management software company, Syncron International, and one of their customers. A core objective has been to develop an accurate method for determining near-optimal reorder points that can be directly applied to real-life systems. The approach is based on decomposing the complex multi-echelon problem into N + 1 single-echelon problems, using a near-optimal-induced backorder cost at the central warehouse. Important extensions made compared to earlier work include the addition of procedures to adjust for lead-time variability, and for undershooting the reorder point when customers’ order sizes vary. The result is a flexible model that is computationally and conceptually simple enough to be implemented in practice. A numerical study, including real data from the case company, illustrates that the new model outperforms existing methods in the literature. Compared to the current methods used by the case company, it offers significant improvements in both service-level fulfilment and system-wide inventory holding costs. Implementations of the model into the Syncron software are in progress.

  • 15.
    Berling, Peter
    et al.
    Linnaeus University, School of Business and Economics, Department of Management Accounting and Logistics. Lund University.
    Martinez-de-Albeniz, Victor
    University of Navarra, Spain.
    A characterization of optimal base-stock levels for a multistage serial supply chain2016In: Naval Research Logistics, ISSN 0894-069X, E-ISSN 1520-6750, Vol. 63, no 1, p. 32-46Article in journal (Refereed)
    Abstract [en]

    In this article, we present a multistage model to optimize inventory control decisions under stochastic demand and continuous review. We first formulate the general problem for continuous stages and use a decomposition solution approach: since it is never optimal to let orders cross, the general problem can be broken into a set of single-unit subproblems that can be solved in a sequential fashion. These subproblems are optimal control problems for which a differential equation must be solved. This can be done easily by recursively identifying coefficients and performing a line search. The methodology is then extended to a discrete number of stages and allows us to compute the optimal solution in an efficient manner, with a competitive complexity. (c) 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 32-46, 2016

  • 16.
    Berling, Peter
    et al.
    Lund University.
    Martínez-de-Albéniz, Victor
    University of Navarra, Spain.
    Dynamic Speed Optimization in Supply Chains with Stochastic Demand2016In: Transportation Science, ISSN 0041-1655, E-ISSN 1526-5447, Vol. 50, no 3, p. 1114-1127Article in journal (Refereed)
    Abstract [en]

    In this paper, we analyze how to continuously adjust the speed in a supply chain with stochastic demand. For each unit (e.g., truckload, shipping container) in the chain, one must decide at which speed it should be moved downstream, given the state of the system, to minimize total supply chain costs. We decompose the problem into a set of one-dimensional subproblems that can be easily solved and characterize the optimal variable speed policy: under some assumptions, we show that it is optimal to set a speed that is first increasing in the distance to the market, and then decreasing. As a result, at optimality a given unit will experience an accelerating speed and then it will be slowed down, unless a demand occurs, in which case, the speed will be adjusted upward. We finally provide a transportation case study where we estimate the benefits of a variable-speed compared to a fixed-speed policy and show them to be significant both financially and from a CO2-emissions perspective.

  • 17.
    Berling, Peter
    et al.
    Lund University.
    Martínez-de-Albéniz, Victor
    University of Navarra, Spain.
    Optimal Inventory Policies when Purchase Price and Demand are Stochastic2011In: Operations Research, ISSN 0030-364X, E-ISSN 1526-5463, Vol. 59, no 1, p. 109-124Article in journal (Refereed)
    Abstract [en]

    In this paper we consider the problem of a firm that faces a stochastic (Poisson) demand and must replenishfrom a market in which prices fluctuate, such as a commodity market. We describe the price evolution as acontinuous stochastic process and we focus on commonly used processes suggested by the financial literature,such as the geometric Brownian motion and the Ornstein-Uhlenbeck process. It is well-known that under variablepurchase price, a price-dependent base-stock policy is optimal. Using the single-unit decomposition approach, weexplicitly characterize the optimal base-stock level using a series of threshold prices. We show that the base-stocklevel is first increasing and then decreasing in the current purchase price. We provide a procedure for calculatingthe thresholds, which yields closed-form solutions when price follows a geometric Brownian motion and implicitsolutions under the Ornstein-Uhlenbeck price model. In addition, our numerical study shows that the optimalpolicy performs much better than inventory policies that ignore future price evolution, because it tends to placelarger orders when prices are expected to increase.

  • 18.
    Berling, Peter
    et al.
    Lund University.
    Stokkedal, Roger
    Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics.
    Demand and sales value considerations for differentiating the service levels2007In: Presented at the 19th International NOFOMA conference, 2007, 2007Conference paper (Refereed)
    Abstract [en]

    How to choose the appropriate service level has been described by many authors and logistics professionals. The common basis for differentiating the policy parameters between products used by many professionals is a traditional ABC-analysis based on the sales volume in SEK, i.e. the demand times the price per unit. Even if this basis is suitable for several issues in inventory management it is not the best for differentiating the service level. This since the value of having a high service level for a high-price product is comparatively low whereas the value of having it for a high demand product is high. There hence exist arguments both for a high and a low service level for A products.

    The purpose of this work is to 1) estimate the potential savings from differentiating the service level 2) contribute to the understanding of which factors should be used as the base for differentiating the service level 3) develop an easy to use method for grouping the products and determining the appropriate service level for each group. In the paper we suggest a simple method for differentiating the service level using a combined ABC/XYZ analysis based on the sales volume in SEK (as the traditional method) and the demand in units. The economical benefits of the suggested method have been evaluated using numerical examples based on real life demand data. This study indicates that substantial cost benefits can be achieved compared to a system with no differentiation of the service levels.

  • 19.
    Berling, Peter
    et al.
    Linnaeus University, School of Business and Economics, Department of Management Accounting and Logistics. Lund University.
    Xie, Zhixue
    Tsinghua University, China.
    Approximation algorithms for optimal purchase/inventory policy when purchase price and demand are stochastic2014In: Or-Spektrum, ISSN 0171-6468, E-ISSN 1436-6304, Vol. 36, no 4, p. 1077-1095Article in journal (Refereed)
    Abstract [en]

    We consider a purchase/inventory control problem in which the purchase price and demand are stochastic, a common situation encountered by firms that replenish in a foreign currency or from commodity markets. More specifically, we assume that the demand follows a Poisson arrival process and that the log-price evolves according to a general Wiener process. Under these circumstances, the optimal policy is a state dependent base-stock policy that can be described as a series of threshold prices. An iterative procedure for determining the optimal thresholds has been derived earlier but, even for the simplest price process, the solution quickly becomes numerically intractable. To deal with this, we propose an approximation that allows us to derive simple heuristics for finding thresholds that are close to optimal. For certain price processes the heuristics are just a series of closed-form expressions. The computational complexity is reduced significantly, and the numerical study shows that the new heuristics perform considerably better than earlier suggested heuristics.

  • 20.
    Farvid, Mojtaba
    et al.
    Linnaeus University, Faculty of Science and Engineering, School of Engineering.
    Berling, Peter
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics.
    Lead-time investigation and estimation2012In: Book of Abstracts: 17th International Symposium on Inventories, August 20-24, 2012, Budapest, Hungary / [ed] Attila Chikán, Budapest: OOK-Press Ltd. , 2012, p. 79-79Conference paper (Refereed)
    Abstract [en]

    It is well known that longer and more uncertain lead-times in a distribution system increase the need for safety stock and/or reduce the service level at lower levels. It is also well known that ordering decisions at higher level of the supply chain have an impact on the lead-time. Yet not much research has been devoted to increasing the knowledge of how exactly these decisions do influence the lead-time and there is a lack of simple methods to estimate the expected value and variance of the lead-time. Recent research that strongly indicates that it is optimal to provide a low service level at the earlier stages only emphasizes the importance of shedding a light on this issue. This as lower service levels leads to prolonged and more frequent stock outs and hence longer and more uncertain lead-time

     

    In this paper we provide a large simulation study of a two-level distribution system where different decision parameters as well as external parameters influence the lead-time are investigated. Not surprisingly, the study shows that the service level at a higher, not surprisingly, has a positive effect on both the length and variability of the lead time, i.e. they decrease with an increasing service-level. More interestingly though, is that of the other parameters it is only the order quantities that has a significant impact on the lead-time. Both the expected value and variance of the lead-time increase with the retailer’s as well as the central warehouse’s order quantity. This can be describe as an inverted bullwhip effect as the lead-time variability increase as one moves down the supply chain towards the end customer.

     

    In the paper we also develop simple methods to estimate the mean value and variance of the lead-time. The methods are based on replacing a stochastic lead-time demand at the central warehouse with a stochastic demand rate. For a given inventory position and demand rate it is shown how to compute the delay at the central warehouse due to stock-outs in a straight forward manner. These delays are then weighted together based on the probability for each combination of inventory position and demand rate to attain the estimates. The difference between the developed methods lay in how the stochastic demand rate is estimated. The above mentioned simulation study shows that the estimates exhibit a good accuracy and constitutes an improvement compared to existing methods with similar computational complexity.

  • 21.
    Jahre, Marianne
    et al.
    BI Norwegian Business School, Norway.
    Kembro, Joakim
    Lund University.
    Rezvanian, Tina
    Northeastern University, USA.
    Ergun, Ozlem
    Northeastern University, USA.
    Håpnes, Svein J.
    UNHCR, Hungary.
    Berling, Peter
    Linnaeus University, School of Business and Economics, Department of Management Accounting and Logistics. Lund University.
    Integrating supply chains for emergencies and ongoing operations in UNHCR2016In: Journal of Operations Management, ISSN 0272-6963, E-ISSN 1873-1317, Vol. 45, no Special Issue, p. 57-72Article in journal (Refereed)
    Abstract [en]

    Humanitarian organizations (HOs) often base their warehouse locations on individuals' experience and knowledge rather than on decision-support tools. Many HOs run separate supply chains for emergency response and ongoing operations. Based on reviews of humanitarian network design literature combined with an in-depth case study of United Nations High Commissioner for Refugees (UNHCR), this paper presents a warehouse location model for joint prepositioning that incorporates political and security situation factors. Although accessibility, co-location, security, and human resources are crucial to the practice of humanitarian operations management, such contextual factors have not been included in existing network optimization models before. We found that when quantified, and modeled, such factors are important determinants of network configuration. In addition, our results suggest that joint prepositioning for emergency response and ongoing operations allows for expansion of the global warehouse network, and reducing cost and response time.

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