Publications and Working Paper

• [ Article ]
• [ Book ]
• [ Inbook ]
• [ Inproceedings ]
• [ Unpublished ]

• Prescriptive Analytics for a Multi-Shift Staffing Problem. Notz, Pascal M.; Wolf, Peter K.; Pibernik, Richard. In European Journal of Operational Research. 2022.
  • [ Abstract ]
  • [ BibTeX ]
  Motivated by the work with an industry partner, this paper proposes and examines novel data-driven approaches to solve a certain type of capacity-sizing problem, which we term the multi-shift staffing problem (MSSP). In our MSSP, a company has to staff multiple shifts for each workday in the presence of uncertain arrival rates that vary throughout the day and patient ``customers'' that do not abandon the queue while waiting for a service, but who must be served by some pre-defined time. Drawing on established methods in both capacity management and prescriptive analytics, we propose to use fluid and stationary approximations of the demand arrival process to apply tailored prescriptive analytics approaches to determine staffing levels for multiple interrelated shifts. The prescriptive analytics approaches rely on machine learning techniques that incorporate a detailed representation of the non-stationary structure of arrivals and leverage extensive auxiliary data. In particular, we adapt established prescriptive analytics approaches\textemdash weighted sample average approximation and kernelized empirical risk minimization\textemdash and propose a new optimization prediction approach to solving the multi-shift staffing problem. Using a case study that is based on extensive data from our project partner, the maintenance service provider, we demonstrate the applicability of these approaches, highlight their benefits over traditional ``estimate then optimize'' approaches, and shed light on their structural properties and performance drivers.

  @article{NOTZ2022, abstract = {Motivated by the work with an industry partner, this paper proposes and examines novel data-driven approaches to solve a certain type of capacity-sizing problem, which we term the multi-shift staffing problem (MSSP). In our MSSP, a company has to staff multiple shifts for each workday in the presence of uncertain arrival rates that vary throughout the day and patient ``customers'' that do not abandon the queue while waiting for a service, but who must be served by some pre-defined time. Drawing on established methods in both capacity management and prescriptive analytics, we propose to use fluid and stationary approximations of the demand arrival process to apply tailored prescriptive analytics approaches to determine staffing levels for multiple interrelated shifts. The prescriptive analytics approaches rely on machine learning techniques that incorporate a detailed representation of the non-stationary structure of arrivals and leverage extensive auxiliary data. In particular, we adapt established prescriptive analytics approaches\textemdash weighted sample average approximation and kernelized empirical risk minimization\textemdash and propose a new optimization prediction approach to solving the multi-shift staffing problem. Using a case study that is based on extensive data from our project partner, the maintenance service provider, we demonstrate the applicability of these approaches, highlight their benefits over traditional ``estimate then optimize'' approaches, and shed light on their structural properties and performance drivers.}, author = {Notz, Pascal M. and Wolf, Peter K. and Pibernik, Richard}, journal = {European Journal of Operational Research}, keywords = {datadrivenoperations}, title = {Prescriptive Analytics for a Multi-Shift Staffing Problem}, year = 2022 }
• Prescriptive Analytics for Flexible Capacity Management. Notz, Pascal M.; Pibernik, Richard. In Management Science, 68(3), S. 1756–1775. 2022.
  • [ Abstract ]
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  Motivated by the real-world problem of a logistics company, this paper proposes a novel distribution-free prescriptive analytics approach\textemdash termed kernelized empirical risk minimization (kernelized ERM)\textemdash to solve a complex two-stage capacity planning problem with multivariate demand and vector-valued capacity decisions and compares this approach both theoretically and numerically with an extension of the well-known sample average approximation (SAA) approach termed weighted SAA. Both approaches use integrated machine learning algorithms to prescribe capacities directly from historical demand and numerous features (covariates) without having to make assumptions about the underlying multivariate demand distribution. We provide extensive analytical insights into both approaches. Most important, we prove the universal approximation property for the kernelized ERM approach when using a universal (data-independent) kernel and show how out-of-sample guarantees can be derived for various kernels. We demonstrate the applicability of both approaches to a real-world planning problem and evaluate their performance relative to traditional parametric approaches that first estimate a multivariate demand distribution and then solve a stochastic optimization problem and a nonparametric approach (SAA). Our results suggest that the two prescriptive analytics approaches can result in substantial performance improvements of up to 58\% compared with traditional approaches. Additional numerical analyses shed light on the behavior and performance drivers of the various approaches and demonstrate that in our case, the prescriptive approaches are much more robust to variations of exogenous cost parameters than traditional approaches. This paper was accepted by J. George Shanthikumar, Management Science Special Section on Data-Driven Prescriptive Analytics.

  @article{notzPrescriptiveAnalyticsFlexible2022, abstract = {Motivated by the real-world problem of a logistics company, this paper proposes a novel distribution-free prescriptive analytics approach\textemdash termed kernelized empirical risk minimization (kernelized ERM)\textemdash to solve a complex two-stage capacity planning problem with multivariate demand and vector-valued capacity decisions and compares this approach both theoretically and numerically with an extension of the well-known sample average approximation (SAA) approach termed weighted SAA. Both approaches use integrated machine learning algorithms to prescribe capacities directly from historical demand and numerous features (covariates) without having to make assumptions about the underlying multivariate demand distribution. We provide extensive analytical insights into both approaches. Most important, we prove the universal approximation property for the kernelized ERM approach when using a universal (data-independent) kernel and show how out-of-sample guarantees can be derived for various kernels. We demonstrate the applicability of both approaches to a real-world planning problem and evaluate their performance relative to traditional parametric approaches that first estimate a multivariate demand distribution and then solve a stochastic optimization problem and a nonparametric approach (SAA). Our results suggest that the two prescriptive analytics approaches can result in substantial performance improvements of up to 58\% compared with traditional approaches. Additional numerical analyses shed light on the behavior and performance drivers of the various approaches and demonstrate that in our case, the prescriptive approaches are much more robust to variations of exogenous cost parameters than traditional approaches. This paper was accepted by J. George Shanthikumar, Management Science Special Section on Data-Driven Prescriptive Analytics.}, author = {Notz, Pascal M. and Pibernik, Richard}, journal = {Management Science}, keywords = {published}, month = {03}, number = 3, pages = {1756–1775}, title = {Prescriptive Analytics for Flexible Capacity Management}, volume = 68, year = 2022 }
• ddop: A python package for data-driven operations management. Philippi, Andreas; Buttler, Simone; Stein, Nikolai. In Journal of Open Source Software, 6(66), S. 3429. 2021.
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  @article{philippi2021ddop, author = {Philippi, Andreas and Buttler, Simone and Stein, Nikolai}, journal = {Journal of Open Source Software}, keywords = {newsvendor}, number = 66, pages = 3429, title = {ddop: A python package for data-driven operations management}, volume = 6, year = 2021 }
• Single-Period Stochastic Demand Fulfillment in Customer Hierarchies. Fleischmann, Moritz; Kloos, Konstantin; Nouri-Roozbahani, Maryam; Pibernik, Richard. In accepted for publication in European Journal of Operational Research. 2020.
  • [ BibTeX ]
  @article{fleischmann2020singleperiod, author = {Fleischmann, Moritz and Kloos, Konstantin and Nouri-Roozbahani, Maryam and Pibernik, Richard}, journal = {accepted for publication in European Journal of Operational Research}, keywords = {demand-fulfillment}, title = {Single-Period Stochastic Demand Fulfillment in Customer Hierarchies}, year = 2020 }
• Prescriptive Analytics for a Queuing Model without Abandonment. Notz, Pascal M.; Wolf, Peter K. In Available at SSRN 3516708. 2020.
  • [ Abstract ]
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  This paper proposes and studies novel data-driven approaches to solve a specific class of capacity sizing problems with uncertain, time varying arrival rates and patient customers that do not abandon the queue while waiting for a service. Our approaches build on a fluid approximation of the underlying queuing system and use machine learning techniques that leverage extensive auxiliary data for prescribing interrelated capacities of multiple shifts. In particular, we employ the prescriptive analytics approaches weighted sample average approximation (weighted SAA), kernelized empirical risk minimization (kernelized ERM) and an ex-post optimization approach (ExPost approach) to solve the capacity sizing problem. We demonstrate the applicability of all three approaches to the real-world staff capacity sizing problem of an aviation maintenance service provider and evaluate their performance relative to traditional approaches. The results show that prescriptive analytics approaches can lead to significant performance improvements, and that at least some are robust across variations of exogenous parameters. We observe that the prescriptive analytics approaches benefit from both their capability of exploiting predictive features, and their ability to model the time structure of arrivals with high accuracy.

  @article{Notz2020, abstract = {This paper proposes and studies novel data-driven approaches to solve a specific class of capacity sizing problems with uncertain, time varying arrival rates and patient customers that do not abandon the queue while waiting for a service. Our approaches build on a fluid approximation of the underlying queuing system and use machine learning techniques that leverage extensive auxiliary data for prescribing interrelated capacities of multiple shifts. In particular, we employ the prescriptive analytics approaches weighted sample average approximation (weighted SAA), kernelized empirical risk minimization (kernelized ERM) and an ex-post optimization approach (ExPost approach) to solve the capacity sizing problem. We demonstrate the applicability of all three approaches to the real-world staff capacity sizing problem of an aviation maintenance service provider and evaluate their performance relative to traditional approaches. The results show that prescriptive analytics approaches can lead to significant performance improvements, and that at least some are robust across variations of exogenous parameters. We observe that the prescriptive analytics approaches benefit from both their capability of exploiting predictive features, and their ability to model the time structure of arrivals with high accuracy.}, author = {Notz, Pascal M. and Wolf, Peter K.}, journal = {Available at SSRN 3516708}, keywords = {capacity-management}, title = {Prescriptive Analytics for a Queuing Model without Abandonment}, year = 2020 }
• Allocation planning under service-level contracts. Kloos, Konstantin; Pibernik, Richard. In European Journal of Operational Research, 280(1), S. 203–218. Elsevier, 2020.
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  Motivated by the practical limitations of current demand fulfillment systems, this paper addresses the problem of allocation planning under service-level contracts in a multi-period setting. We provide a formal definition of the allocation planning problem under a type of service-level contract that is particularly relevant to manufacturing industries and formulate a corresponding stochastic dynamic program. Based on a rigorous formal analysis of the dynamic program, we derive the requirements a “good” allocation policy should meet and use them to evaluate the heuristic policies proposed in the literature and to derive new allocation policies that may enhance the performance of allocation planning under service-level contracts. After detailed characterization and discussion of these new policies, we present the results of an extensive numerical study that allow us to quantify and compare allocation policies’ performance and to derive recommendations for decision makers in practice.

  @article{kloos2020allocation, abstract = {Motivated by the practical limitations of current demand fulfillment systems, this paper addresses the problem of allocation planning under service-level contracts in a multi-period setting. We provide a formal definition of the allocation planning problem under a type of service-level contract that is particularly relevant to manufacturing industries and formulate a corresponding stochastic dynamic program. Based on a rigorous formal analysis of the dynamic program, we derive the requirements a “good” allocation policy should meet and use them to evaluate the heuristic policies proposed in the literature and to derive new allocation policies that may enhance the performance of allocation planning under service-level contracts. After detailed characterization and discussion of these new policies, we present the results of an extensive numerical study that allow us to quantify and compare allocation policies’ performance and to derive recommendations for decision makers in practice.}, author = {Kloos, Konstantin and Pibernik, Richard}, journal = {European Journal of Operational Research}, keywords = {demand-fulfillment}, number = 1, pages = {203-218}, publisher = {Elsevier}, title = {Allocation planning under service-level contracts}, volume = 280, year = 2020 }
• Allocation planning in sales hierarchies with stochastic demand and service-level targets. Kloos, Konstantin; Pibernik, Richard; Schulte, Benedikt. In OR Spectrum, 41(4), S. 981–1024. 2019.
  • [ Abstract ]
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  Matching supply with demand remains a challenging task for many companies, especially when purchasing and production must be planned with sufficient lead time, demand is uncertain, overall supply may not suffice to fulfill all of the projected demands, and customers differ in their level of importance. The particular structure of sales organizations often adds another layer of complexity: These organizations often have multi-level hierarchical structures that include multiple geographic sales regions, distribution channels, customer groups, and individual customers (e.g., key accounts). In this paper, we address the problem of ``allocation planning'' in such sales hierarchies when customer demand is stochastic, supply is scarce, and the company's objective is to meet individual customer groups' service-level targets. Our first objective is to determine when conventional allocation rules lead to optimal (or at least acceptable) results and to characterize their optimality gap relative to the theoretical optimum. We find that these popular rules lead to optimal results only under very restrictive conditions and that the loss in optimality is often substantial. This result leads us to pursue our second objective: to find alternative (decentral) allocation approaches that generate acceptable performance under conditions in which the conventional allocation rules lead to poor results. We develop two alternative (decentral) allocation approaches and derive conditions under which they lead to optimal allocations. Based on numerical analyses, we show that these alternative approaches outperform the conventional allocation rules, independent of the conditions under which they are used. Our results suggest that they lead to near-optimal solutions under most conditions.

  @article{Kloos2019, abstract = {Matching supply with demand remains a challenging task for many companies, especially when purchasing and production must be planned with sufficient lead time, demand is uncertain, overall supply may not suffice to fulfill all of the projected demands, and customers differ in their level of importance. The particular structure of sales organizations often adds another layer of complexity: These organizations often have multi-level hierarchical structures that include multiple geographic sales regions, distribution channels, customer groups, and individual customers (e.g., key accounts). In this paper, we address the problem of ``allocation planning'' in such sales hierarchies when customer demand is stochastic, supply is scarce, and the company's objective is to meet individual customer groups' service-level targets. Our first objective is to determine when conventional allocation rules lead to optimal (or at least acceptable) results and to characterize their optimality gap relative to the theoretical optimum. We find that these popular rules lead to optimal results only under very restrictive conditions and that the loss in optimality is often substantial. This result leads us to pursue our second objective: to find alternative (decentral) allocation approaches that generate acceptable performance under conditions in which the conventional allocation rules lead to poor results. We develop two alternative (decentral) allocation approaches and derive conditions under which they lead to optimal allocations. Based on numerical analyses, we show that these alternative approaches outperform the conventional allocation rules, independent of the conditions under which they are used. Our results suggest that they lead to near-optimal solutions under most conditions.}, author = {Kloos, Konstantin and Pibernik, Richard and Schulte, Benedikt}, journal = {OR Spectrum}, keywords = {imported}, month = 12, number = 4, pages = {981–1024}, title = {Allocation planning in sales hierarchies with stochastic demand and service-level targets}, volume = 41, year = 2019 }
• Machine Learning for Inventory Management: Analyzing Two Concepts to Get From Data to Decisions. Meller, Jan; Taigel, Fabian. In Available at SSRN 3256643. 2019.
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  We analyze two fundamentally different concepts to considering data for planning decisions using the example of a newsvendor problem in which observable features drive variations in demand. Our work contributes to the extant literature in two ways. First, we develop a novel joint estimation-optimization (JEO) method that adapts the random forest machine learning algorithm to integrate the two steps of traditional separated estimation and optimization (SEO) methods: estimating a model to forecast demand and, given the uncertainty of the forecasting model, determining a safety buffer. Second, we provide an analysis of the factors that drive difference in the performance of the corresponding SEO and JEO implementations. We provide the analytical and empirical results of two studies, one in a controlled simulation setting and one on a real-world data set, for our performance evaluations. We find that JEO approaches can lead to significantly better results than their SEO counterparts can when feature-dependent uncertainty is present and when the cost structure of overage and underage costs is asymmetric. However, in the examined practical settings the magnitude of these performance differences is limited because of the overlay of opposing effects that entail the properties of the remaining uncertainty and the cost structure.

  @article{meller2019machine, abstract = {We analyze two fundamentally different concepts to considering data for planning decisions using the example of a newsvendor problem in which observable features drive variations in demand. Our work contributes to the extant literature in two ways. First, we develop a novel joint estimation-optimization (JEO) method that adapts the random forest machine learning algorithm to integrate the two steps of traditional separated estimation and optimization (SEO) methods: estimating a model to forecast demand and, given the uncertainty of the forecasting model, determining a safety buffer. Second, we provide an analysis of the factors that drive difference in the performance of the corresponding SEO and JEO implementations. We provide the analytical and empirical results of two studies, one in a controlled simulation setting and one on a real-world data set, for our performance evaluations. We find that JEO approaches can lead to significantly better results than their SEO counterparts can when feature-dependent uncertainty is present and when the cost structure of overage and underage costs is asymmetric. However, in the examined practical settings the magnitude of these performance differences is limited because of the overlay of opposing effects that entail the properties of the remaining uncertainty and the cost structure.}, author = {Meller, Jan and Taigel, Fabian}, journal = {Available at SSRN 3256643}, keywords = {Inventory-Management}, month = 11, title = {Machine Learning for Inventory Management: Analyzing Two Concepts to Get From Data to Decisions}, year = 2019 }
• Purchasing managers’ willingness to pay for attributes that constitute sustainability. Goebel, Philipp; Reuter, Carsten; Pibernik, Richard; Sichtmann, Christina; Bals, Lydia. In Journal of Operations Management, 62, S. 44–58. 2018.
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  Considering the increasing international division of labor, as well as stakeholders' growing awareness of sustainability, assuring that business practices are sustainable is a major challenge. Companies have to account for the fact that any misconduct at a supplier's premises may have spillover effects that reach the manufacturer or retailer. Therefore, purchasing managers have to assure that their suppliers are compliant with sustainability standards. This, however, may induce higher purchasing costs and, as a consequence, force a trade-off between (short term) economic (i.e., purchasing cost reduction) and social/environmental sustainability criteria. How purchasing managers evaluate this trade-off is particularly interesting because they often receive performance-based salaries that incentivize the reduction of purchasing costs. Our paper sheds light on this trade-off by examining how much purchasing managers are willing to pay to assure compliance along different sustainability dimensions when selecting new suppliers in a mature market setting, namely Germany. Additionally, we identify potential (individual, professional, and organization-related) factors that may impact the purchasing managers' willingness to pay (WTP), and examine their effects. Among the most surprising findings, purchasing managers on average are willing to pay a price premium for manuals that demonstrate compliance with the United Nationals Global Compact (UNGC). Furthermore, the results show that this WTP is mostly influenced (negatively) by self-enhancement (on the individual level) and/or obedience to authority (on the organizational level), but the effects of company, affiliation with the UNGC, gender, or years of experience have no influence. Moreover, the WTP is higher for the social than for the environmental dimension, and the marginal effect of accreditation on WTP depends on which combinations of dimensions are accredited.

  @article{GOEBEL2018, abstract = {Considering the increasing international division of labor, as well as stakeholders' growing awareness of sustainability, assuring that business practices are sustainable is a major challenge. Companies have to account for the fact that any misconduct at a supplier's premises may have spillover effects that reach the manufacturer or retailer. Therefore, purchasing managers have to assure that their suppliers are compliant with sustainability standards. This, however, may induce higher purchasing costs and, as a consequence, force a trade-off between (short term) economic (i.e., purchasing cost reduction) and social/environmental sustainability criteria. How purchasing managers evaluate this trade-off is particularly interesting because they often receive performance-based salaries that incentivize the reduction of purchasing costs. Our paper sheds light on this trade-off by examining how much purchasing managers are willing to pay to assure compliance along different sustainability dimensions when selecting new suppliers in a mature market setting, namely Germany. Additionally, we identify potential (individual, professional, and organization-related) factors that may impact the purchasing managers' willingness to pay (WTP), and examine their effects. Among the most surprising findings, purchasing managers on average are willing to pay a price premium for manuals that demonstrate compliance with the United Nationals Global Compact (UNGC). Furthermore, the results show that this WTP is mostly influenced (negatively) by self-enhancement (on the individual level) and/or obedience to authority (on the organizational level), but the effects of company, affiliation with the UNGC, gender, or years of experience have no influence. Moreover, the WTP is higher for the social than for the environmental dimension, and the marginal effect of accreditation on WTP depends on which combinations of dimensions are accredited.}, author = {Goebel, Philipp and Reuter, Carsten and Pibernik, Richard and Sichtmann, Christina and Bals, Lydia}, journal = {Journal of Operations Management}, keywords = {Industrial-purchasing}, pages = {44-58}, title = {Purchasing managers' willingness to pay for attributes that constitute sustainability}, volume = 62, year = 2018 }
• The Value of Entrant Manufacturers: A Study of Competition and Risk for Donor-Funded Procurement of Essential Medicines. Lauton, Felix; Rothkopf, Alexander; Pibernik, Richard. In European Journal of Operational Research, 272(1), S. 292–312. 2018.
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  Abstract Global-health purchasing organizations (POs) want to increase access to essential medicines in low-income countries. One way to purchase more medicines with limited funds is to contract with generics manufacturers, thereby increasing competition and lowering prices. However, many \{POs\} fear that these entrants are less reliable than others and increase supply risks: failure to adhere to lead times and supplier defaults may cause disruptions that result in unsuccessful medical treatments. The problem can be remedied or at least reduced if \{POs\} have a sound basis for assessing manufacturers. To this end, we develop a mathematical framework that supports decision-makers in an integrated evaluation of an entrant’s effect on purchasing costs and supply risks. Our approach accounts for the characteristics of donor-funded global-health markets and the particular tasks and specific challenges of \{POs\} in these markets. More specifically, our approach enables a \{PO\} to quantify a potential entrant’s value depending on important characteristics of the incumbent and the entrant manufacturer. We use data from a project for donor-funded procurement of Depot Medroxyprogesterone Acetate (DMPA) of two large POs. Our results show the feasibility of our approach for POs, manufacturers, and philanthropic investors in the global-health domain, and we explore the trade-off between competition and supply risks and provide insights into how the entrant’s value is affected by parameters like production costs, capacity, lead time and default risk, and in-country registration.

  @article{Lauton2018, abstract = {Abstract Global-health purchasing organizations (POs) want to increase access to essential medicines in low-income countries. One way to purchase more medicines with limited funds is to contract with generics manufacturers, thereby increasing competition and lowering prices. However, many \{POs\} fear that these entrants are less reliable than others and increase supply risks: failure to adhere to lead times and supplier defaults may cause disruptions that result in unsuccessful medical treatments. The problem can be remedied or at least reduced if \{POs\} have a sound basis for assessing manufacturers. To this end, we develop a mathematical framework that supports decision-makers in an integrated evaluation of an entrant’s effect on purchasing costs and supply risks. Our approach accounts for the characteristics of donor-funded global-health markets and the particular tasks and specific challenges of \{POs\} in these markets. More specifically, our approach enables a \{PO\} to quantify a potential entrant’s value depending on important characteristics of the incumbent and the entrant manufacturer. We use data from a project for donor-funded procurement of Depot Medroxyprogesterone Acetate (DMPA) of two large POs. Our results show the feasibility of our approach for POs, manufacturers, and philanthropic investors in the global-health domain, and we explore the trade-off between competition and supply risks and provide insights into how the entrant’s value is affected by parameters like production costs, capacity, lead time and default risk, and in-country registration.}, author = {Lauton, Felix and Rothkopf, Alexander and Pibernik, Richard}, journal = {European Journal of Operational Research}, keywords = {published}, number = 1, pages = {292-312}, title = {The Value of Entrant Manufacturers: A Study of Competition and Risk for Donor-Funded Procurement of Essential Medicines}, volume = 272, year = 2018 }
• Privacy-preserving condition-based forecasting using machine learning. Taigel, F; Tueno, A K; Pibernik, R. In Journal of Business Economics, 88(5), S. 563–592. 2018.
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  As machines get smarter, massive amounts of condition-based data from distributed sources become available. This data can be used to enhance maintenance management in several ways, such as by improving maintenance demand forecasting and spare parts and capacity planning. Regarding the former, machine learning techniques promise substantial benefits for forecasting the demand for spare parts over conventional techniques that are commonly used. While development and implementation of these techniques is difficult, practical applications pose another important challenge to providers of maintenance, repair, and overhaul services. Their customers are reluctant to provide access to sensitive real-time data because of privacy concerns, and even more so when their data is stored and processed in the cloud. In this paper we describe an application for privacy-preserving forecasting of demand for spare parts based on distributed condition data. It combines machine learning techniques---more specifically, decision-tree classification---with order-preserving encryption. The application is appropriate whenever planning for spare parts for the maintenance of condition-monitored machinery is needed, and it is particularly suitable for cloud-based implementation.

  @article{TaigelTuenoPibernik2016, abstract = {As machines get smarter, massive amounts of condition-based data from distributed sources become available. This data can be used to enhance maintenance management in several ways, such as by improving maintenance demand forecasting and spare parts and capacity planning. Regarding the former, machine learning techniques promise substantial benefits for forecasting the demand for spare parts over conventional techniques that are commonly used. While development and implementation of these techniques is difficult, practical applications pose another important challenge to providers of maintenance, repair, and overhaul services. Their customers are reluctant to provide access to sensitive real-time data because of privacy concerns, and even more so when their data is stored and processed in the cloud. In this paper we describe an application for privacy-preserving forecasting of demand for spare parts based on distributed condition data. It combines machine learning techniques—more specifically, decision-tree classification—with order-preserving encryption. The application is appropriate whenever planning for spare parts for the maintenance of condition-monitored machinery is needed, and it is particularly suitable for cloud-based implementation.}, author = {Taigel, F and Tueno, A K and Pibernik, R}, journal = {Journal of Business Economics}, keywords = {datadrivenoperations}, month = {07}, number = 5, pages = {563-592}, title = {Privacy-preserving condition-based forecasting using machine learning}, volume = 88, year = 2018 }
• Big data on the shop-floor: sensor-based decision-support for manual processes. Stein, Nikolai; Meller, Jan; Flath, Christoph M. In Journal of Business Economics, 88(5), S. 593–616. 2018.
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  Analytics applications are becoming indispensable in today's business landscape. Greater data availability from self-monitoring production equipment allows firms to empower individual workers on the shop-floor with powerful decision support solutions. To explore the potential of such solutions, we replicate an important manual leak detection process from high-tech composite manufacturing and augment the system with highly sensitive sensors. Based on this setup we illustrate the main steps and major challenges in developing and instantiating a predictive decision support system. By establishing a scalable and generic feature generation approach as well as leveraging techniques from statistical learning, we are able to improve the forecasts of the leak position by almost 90{\%}. Recognizing that mere forecast information cannot be evaluated with respect to business value, we subsequently embed the problem in an analysis of the underlying searcher path problem. We compare predictive and prescriptive search policies against simple benchmark rules. The data-supported policies dramatically reduce the median as well as the variability of the search time. Based on these findings we posit that prescriptive analytics can and should play a greater role in assisting manual labor in manufacturing environments.

  @article{Stein2018, abstract = {Analytics applications are becoming indispensable in today's business landscape. Greater data availability from self-monitoring production equipment allows firms to empower individual workers on the shop-floor with powerful decision support solutions. To explore the potential of such solutions, we replicate an important manual leak detection process from high-tech composite manufacturing and augment the system with highly sensitive sensors. Based on this setup we illustrate the main steps and major challenges in developing and instantiating a predictive decision support system. By establishing a scalable and generic feature generation approach as well as leveraging techniques from statistical learning, we are able to improve the forecasts of the leak position by almost 90{\%}. Recognizing that mere forecast information cannot be evaluated with respect to business value, we subsequently embed the problem in an analysis of the underlying searcher path problem. We compare predictive and prescriptive search policies against simple benchmark rules. The data-supported policies dramatically reduce the median as well as the variability of the search time. Based on these findings we posit that prescriptive analytics can and should play a greater role in assisting manual labor in manufacturing environments.}, author = {Stein, Nikolai and Meller, Jan and Flath, Christoph M.}, journal = {Journal of Business Economics}, keywords = {datadrivenoperations}, month = {07}, number = 5, pages = {593-616}, title = {Big data on the shop-floor: sensor-based decision-support for manual processes}, volume = 88, year = 2018 }
• Profitability of Service-Level-Based Price Differentiation with Inventory Rationing. Schulte, B; Pibernik, R. In Production and Operations Management, 26(5), S. 903–923. 2017.
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  @article{SchultePibernik2016b, author = {Schulte, B and Pibernik, R}, journal = {Production and Operations Management}, keywords = {Published}, number = 5, pages = {903-923}, title = {Profitability of Service-Level-Based Price Differentiation with Inventory Rationing.}, volume = 26, year = 2017 }
• Hit-Or-Miss: What Leads Experts to Take Advice for Long Term Judgments. Ecken, P; Pibernik, R. In Management Science, 62(7), S. 2002–2021. 2016.
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  When decision makers face crucial strategic decisions they frequently have to rely on judgments about events in the far future. These judgments are typically characterized by very high uncertainty and the absence of experience from previous good or bad judgments. Judgments of other experts are oftentimes an important—sometimes the only—source of additional information to reduce uncertainty and improve judgment accuracy. However, decision makers have very limited means to evaluate the quality of such “advice” from other experts and could tend to ignore this valid source of information. In this paper we study what leads decision makers to take advice from an expert panel when judging the probability of far future events with high economic impact. Our analysis is based on a unique dataset that comprises more than 15,000 advice taking decisions made by almost 1,000 experts from different industries. We find that decision makers’ general tendency to ignore advice is particularly strong in the domain of long term judgments and pronounces even further when conflicts in terms of beliefs, past experiences, or desires arise.

  @article{EckenPibernik2016a, abstract = {When decision makers face crucial strategic decisions they frequently have to rely on judgments about events in the far future. These judgments are typically characterized by very high uncertainty and the absence of experience from previous good or bad judgments. Judgments of other experts are oftentimes an important—sometimes the only—source of additional information to reduce uncertainty and improve judgment accuracy. However, decision makers have very limited means to evaluate the quality of such “advice” from other experts and could tend to ignore this valid source of information. In this paper we study what leads decision makers to take advice from an expert panel when judging the probability of far future events with high economic impact. Our analysis is based on a unique dataset that comprises more than 15,000 advice taking decisions made by almost 1,000 experts from different industries. We find that decision makers’ general tendency to ignore advice is particularly strong in the domain of long term judgments and pronounces even further when conflicts in terms of beliefs, past experiences, or desires arise.}, author = {Ecken, P and Pibernik, R}, journal = {Management Science}, keywords = {imported}, number = 7, pages = {2002-2021}, title = {Hit-Or-Miss: What Leads Experts to Take Advice for Long Term Judgments}, volume = 62, year = 2016 }
• Maverick Buying: Eliminate, Participate, Leverage. Rothkopf, A; Pibernik, R. In International Journal of Production Economics, 179, S. 77–89. 2016.
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  In recent years many companies have made an eort to consolidate purchasing volumes that were previously scattered across different organizational units with the objective to achieve lower purchasing prices, lower transaction costs, etc. To realize these benefits companies frequently opt for a hybrid organizational structure. A central purchasing department negotiates frame contracts based on consolidated purchasing volumes from which local (de-central) business units are required to order. This organizational setup is conducive to a specific form of non-compliant behavior, oftentimes referred to as maverick buying: local business units bypass the official purchasing processes and source from a supplier other than the designated one. This non-compliant behavior obviously counteracts the companies' efforts to achieve benefits of volume consolidation. In this paper we consider maverick buying as a specific form of hidden action in a principal agent framework and provide a formal analysis of different strategies that companies can employ to remedy its negative consequences. Our results suggest that the conventional strategy of monitoring agents and penalizing them in case of non-compliance has clear limitations and may, in fact, be ineffective. We argue that rather than trying to eliminate maverick buying through monitoring, it can, under certain conditions, be more effective to participate in the agents' superior market knowledge. To this end we propose a self-selection model in which the principal offers distinct contract menus - termed participation menus - which are tailored toward agents with an attractive outside option and agents without an incentive to buy maverick. We demonstrate that participation menus perform particularly well in situations in which conventional monitoring fails. Our results indicate that there may even be situations in which they can be employed to leverage the agents' purchasing capabilities so that profits exceed those of a traditional first-best solution.

  @article{RothkopfPibernik2016a, abstract = {In recent years many companies have made an eort to consolidate purchasing volumes that were previously scattered across different organizational units with the objective to achieve lower purchasing prices, lower transaction costs, etc. To realize these benefits companies frequently opt for a hybrid organizational structure. A central purchasing department negotiates frame contracts based on consolidated purchasing volumes from which local (de-central) business units are required to order. This organizational setup is conducive to a specific form of non-compliant behavior, oftentimes referred to as maverick buying: local business units bypass the official purchasing processes and source from a supplier other than the designated one. This non-compliant behavior obviously counteracts the companies' efforts to achieve benefits of volume consolidation. In this paper we consider maverick buying as a specific form of hidden action in a principal agent framework and provide a formal analysis of different strategies that companies can employ to remedy its negative consequences. Our results suggest that the conventional strategy of monitoring agents and penalizing them in case of non-compliance has clear limitations and may, in fact, be ineffective. We argue that rather than trying to eliminate maverick buying through monitoring, it can, under certain conditions, be more effective to participate in the agents' superior market knowledge. To this end we propose a self-selection model in which the principal offers distinct contract menus - termed participation menus - which are tailored toward agents with an attractive outside option and agents without an incentive to buy maverick. We demonstrate that participation menus perform particularly well in situations in which conventional monitoring fails. Our results indicate that there may even be situations in which they can be employed to leverage the agents' purchasing capabilities so that profits exceed those of a traditional first-best solution.}, author = {Rothkopf, A and Pibernik, R}, journal = {International Journal of Production Economics}, keywords = {globalhealth}, pages = {77–89}, title = {Maverick Buying: Eliminate, Participate, Leverage}, volume = 179, year = 2016 }
• Capacity planning for a maintenance service provider with advanced information. Kurz, Julian. In European Journal of Operational Research, 251(2). 2016.
  • [ Abstract ]
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  Analytical and iterative optimization techniques are employed to solve a job shop-like capacity planning problem for a maintenance service provider with contractually defined lead time requirements. The problem is motivated by a real-life case example, namely the overhaul of airline aircraft engines through an external service provider. The production network is modeled as a network of GI/G/1 queues, where the service rates are the decision variables and capacity costs and penalty costs for not meeting contractually defined lead times are minimized. Additionally, we analytically investigate the effects of collaborative maintenance management as a source of advanced information regarding future maintenance demand. More specifically, we consider the benefits of improved service rates and service and demand variabilities on production capacities and total costs. Numerical examples are provided to verify the proposed optimization procedure and illustrate the effects of collaborative maintenance management.

  @article{Julian2016, abstract = {Analytical and iterative optimization techniques are employed to solve a job shop-like capacity planning problem for a maintenance service provider with contractually defined lead time requirements. The problem is motivated by a real-life case example, namely the overhaul of airline aircraft engines through an external service provider. The production network is modeled as a network of GI/G/1 queues, where the service rates are the decision variables and capacity costs and penalty costs for not meeting contractually defined lead times are minimized. Additionally, we analytically investigate the effects of collaborative maintenance management as a source of advanced information regarding future maintenance demand. More specifically, we consider the benefits of improved service rates and service and demand variabilities on production capacities and total costs. Numerical examples are provided to verify the proposed optimization procedure and illustrate the effects of collaborative maintenance management.}, author = {Kurz, Julian}, journal = {European Journal of Operational Research}, keywords = {datadrivenoperations}, number = 2, title = {Capacity planning for a maintenance service provider with advanced information}, volume = 251, year = 2016 }
• Service differentiation in a single-period inventory model with numerous customer classes. Schulte, B; Pibernik, R. In OR Spectrum, 38(4), S. 921–948. 2016.
  • [ Abstract ]
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  We study critical-level inventory-management policies as means to provide differentiated ( αα and ββ ) service levels to more than two classes of customers. First, we derive closed-form expressions for the service levels of a single-period critical-level policy with an arbitrary number of customer classes (with Poisson demand). Based on the service-level expressions, we derive additional structural insights and provide an efficient algorithm with which to compute the essential system parameters, that is, the minimum required starting inventory and the associated critical levels. Based on these results, we conduct numerical experiments and develop structural insights into the system’s behavior.

  @article{SchultePibernik2016c, abstract = {We study critical-level inventory-management policies as means to provide differentiated ( αα and ββ ) service levels to more than two classes of customers. First, we derive closed-form expressions for the service levels of a single-period critical-level policy with an arbitrary number of customer classes (with Poisson demand). Based on the service-level expressions, we derive additional structural insights and provide an efficient algorithm with which to compute the essential system parameters, that is, the minimum required starting inventory and the associated critical levels. Based on these results, we conduct numerical experiments and develop structural insights into the system’s behavior.}, author = {Schulte, B and Pibernik, R}, journal = {OR Spectrum}, keywords = {Published}, number = 4, pages = {921–948}, title = {Service differentiation in a single-period inventory model with numerous customer classes}, volume = 38, year = 2016 }
• Customer-facing supply chain practices – the impact of demand and distribution management on supply chain success. Rexhausen, D; Pibernik, R; Kaiser, G. In Journal of Operations Management, 30(4), S. 269–281. 2012.
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  @article{RexhausenPibernikKaiser2012, author = {Rexhausen, D and Pibernik, R and Kaiser, G}, journal = {Journal of Operations Management}, keywords = {Published}, number = 4, pages = {269–281}, title = {Customer-facing supply chain practices – the impact of demand and distribution management on supply chain success}, volume = 30, year = 2012 }
• Reservation and Allocation Policies for Influenza Vaccines. Samii, A B; Pibernik, R; Yadav, P; Vereecke, A. In European Journal of Operations Research, 222(3), S. 495–507. 2012.
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  @article{SamiiPibernikYadavEtAl2012, author = {Samii, A B and Pibernik, R and Yadav, P and Vereecke, A}, journal = {European Journal of Operations Research}, keywords = {globalhealth}, number = 3, pages = {495–507}, title = {Reservation and Allocation Policies for Influenza Vaccines}, volume = 222, year = 2012 }
• The Influence of Ethical Culture on Supplier Selection in the Context of Sustainable Sourcing. Goebel, P; Pibernik, R; Sichtmann, C; Reuter, C. In International Journal of Production Economics, 140(1), S. 7–17. 2012.
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  @article{GoebelPibernikSichtmannEtAl2012, author = {Goebel, P and Pibernik, R and Sichtmann, C and Reuter, C}, journal = {International Journal of Production Economics}, keywords = {imported}, number = 1, pages = {7–17}, title = {The Influence of Ethical Culture on Supplier Selection in the Context of Sustainable Sourcing}, volume = 140, year = 2012 }
• Paying for Convenience: Attractiveness and Market Potential of Time-Based Delivery Services. Goebel, P; Möller, S; Pibernik, R. In International Journal of Physical Distribution and Logistics Management, 42(6), S. 584–606. 2012.
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  @article{GoebelMoellerPibernik2012, author = {Goebel, P and Möller, S and Pibernik, R}, journal = {International Journal of Physical Distribution and Logistics Management}, keywords = {imported}, number = 6, pages = {584–606}, title = {Paying for Convenience: Attractiveness and Market Potential of Time-Based Delivery Services}, volume = 42, year = 2012 }
• Desirability bias in foresight: Consequences for decision quality based on Delphi results. Ecken, P; Gnatzy, T; von_der_Gracht, H. In Technological Forecasting & Social Change, 78(9), S. 1654–1670. 2011.
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  @article{EckenGnatzy2011, author = {Ecken, P and Gnatzy, T and von_der_Gracht, H}, journal = {Technological Forecasting & Social Change}, keywords = {imported}, number = 9, pages = {1654–1670}, title = {Desirability bias in foresight: Consequences for decision quality based on Delphi results}, volume = 78, year = 2011 }
• Secure collaborative Supply-Chain management. Zilli, A; Pibernik, R; Catrina, O; De_hoogh, S; Schoenmakers, B; Cimato, S; Damiani, E. In Computer, 44(9), S. 38–43. 2011.
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  @article{ZilliPibernikCatrinaEtAl2011, author = {Zilli, A and Pibernik, R and Catrina, O and De_hoogh, S and Schoenmakers, B and Cimato, S and Damiani, E}, journal = {Computer}, keywords = {Published}, number = 9, pages = {38–43}, title = {Secure collaborative Supply-Chain management}, volume = 44, year = 2011 }
• Secure collaborative supply chain planning and inverse optimization - The JELS model. Pibernik, R; Zhang, Y; Kerschbaum, F; Schröpfer, A. In European Journal of Operational Research, 208(1), S. 75–85. 2011.
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  @article{PibernikZhangKerschbaumEtAl2011, author = {Pibernik, R and Zhang, Y and Kerschbaum, F and Schröpfer, A}, journal = {European Journal of Operational Research}, keywords = {practice}, number = 1, pages = {75–85}, title = {Secure collaborative supply chain planning and inverse optimization - The JELS model}, volume = 208, year = 2011 }
• The manufacturer’s incentive to reduce lead times. Kraiselburd, S; Pibernik, R; Raman, A. In Production and Operations Management, 20(5), S. 639–653. 2011.
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  @article{KraiselburdPibernikRaman2011, author = {Kraiselburd, S and Pibernik, R and Raman, A}, journal = {Production and Operations Management}, keywords = {imported}, number = 5, pages = {639–653}, title = {The manufacturer’s incentive to reduce lead times}, volume = 20, year = 2011 }
• An inventory reservation problem with nesting and fill rate based performance measures. Samii, A-B; Pibernik, R; Yadav, P. In International Journal of Production Economics, 133(1), S. 393–402. 2011.
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  @article{SamiiPibernikYadav2011, author = {Samii, A-B and Pibernik, R and Yadav, P}, journal = {International Journal of Production Economics}, keywords = {Published}, number = 1, pages = {393–402}, title = {An inventory reservation problem with nesting and fill rate based performance measures}, volume = 133, year = 2011 }
• Inventory reservation and real-time order promising in a make-to-stock system. Pibernik, R; Yadav, P. In OR Spectrum, 31(1), S. 281–307. 2009.
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  @article{PibernikYadav2009a, author = {Pibernik, R and Yadav, P}, journal = {OR Spectrum}, keywords = {Published}, number = 1, pages = {281–307}, title = {Inventory reservation and real-time order promising in a make-to-stock system}, volume = 31, year = 2009 }
• The impact of performance measurement on the recognition of purchasing and supply management’s value contribution: A principal-agent based case study analysis. Groetsch, V; Henke, M; Pibernik, R. In International Journal of Production Economics - submitted. 2009.
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  @article{GroetschHenkePibernik2009, author = {Groetsch, V and Henke, M and Pibernik, R}, journal = {International Journal of Production Economics - submitted}, keywords = {imported}, note = {under revision - submitted}, title = {The impact of performance measurement on the recognition of purchasing and supply management’s value contribution: A principal-agent based case study analysis}, year = 2009 }
• Dynamic capacity reservation and due date quoting in a make-to-order system. Pibernik, R; Yadav, P. In Naval Research Logistics, 55(7), S. 593–611. 2009.
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  @article{PibernikYadav2009, author = {Pibernik, R and Yadav, P}, journal = {Naval Research Logistics}, keywords = {imported}, number = 7, pages = {593–611}, title = {Dynamic capacity reservation and due date quoting in a make-to-order system}, volume = 55, year = 2009 }
• Optimizations for risk-aware secure supply chain master planning. Schröpfer, A; Kerschbaum, F; Schütz, C; Pibernik, R. In Journal of Universal Computer Science, 15(15), S. 3019–3037. 2009.
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  @article{SchroepferKerschbaumSchuetzEtAl2009, author = {Schröpfer, A and Kerschbaum, F and Schütz, C and Pibernik, R}, journal = {Journal of Universal Computer Science}, keywords = {practice}, number = 15, pages = {3019–3037}, title = {Optimizations for risk-aware secure supply chain master planning}, volume = 15, year = 2009 }
• Verhandlungsbasiertes Master Planning in Supply Chains. Pibernik, R; Sucky, E. In DBW Die Betriebswirtschaft, 68(1), S. 85–111. 2008.
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  @article{PibernikSucky2008, author = {Pibernik, R and Sucky, E}, journal = {DBW Die Betriebswirtschaft}, keywords = {imported}, number = 1, pages = {85–111}, title = {Verhandlungsbasiertes Master Planning in Supply Chains}, volume = 68, year = 2008 }
• An approach to inter domain master planning in supply chains. Pibernik, R; Sucky, E. In International Journal of Production Economics, 108(1), S. 200–212. 2007.
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  @article{PibernikSucky2007, author = {Pibernik, R and Sucky, E}, journal = {International Journal of Production Economics}, keywords = {practice}, number = 1, pages = {200–212}, title = {An approach to inter domain master planning in supply chains}, volume = 108, year = 2007 }
• Strategic deployment of specialized testing and remanufacturing in a global high-tech supply chain. Goentzel, J; Manzione, L T; Pibernik, R; Pruett, J; Thiessen, B. In International Journal of Manufacturing Technology and Management, 11(1), S. 28–52. 2007.
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  @article{GoentzelManzionePibernikEtAl2007, author = {Goentzel, J and Manzione, L T and Pibernik, R and Pruett, J and Thiessen, B}, journal = {International Journal of Manufacturing Technology and Management}, keywords = {imported}, number = 1, pages = {28–52}, title = {Strategic deployment of specialized testing and remanufacturing in a global high-tech supply chain}, volume = 11, year = 2007 }
• Centralised and decentralised supply chain planning. Pibernik, R; Sucky, E. In International Journal of Integrated Supply Management, 2(1), S. 6–27. 2006.
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  @article{PibernikSucky2006, author = {Pibernik, R and Sucky, E}, journal = {International Journal of Integrated Supply Management}, keywords = {practice}, number = 1, pages = {6–27}, title = {Centralised and decentralised supply chain planning}, volume = 2, year = 2006 }
• Managing stock-outs effectively with order fulfillment systems. Pibernik, R. In Journal of Manufacturing Technology Management, 17(6), S. 721–736. 2006.
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  @article{Pibernik2006, author = {Pibernik, R}, journal = {Journal of Manufacturing Technology Management}, keywords = {imported}, number = 6, pages = {721–736}, title = {Managing stock-outs effectively with order fulfillment systems}, volume = 17, year = 2006 }
• Advanced available-to-promise: Classification, selected methods and requirements for operations and inventory management. Pibernik, R. In International Journal of Production Economics, 93(94), S. 239–252. 2005.
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  @article{Pibernik2005, author = {Pibernik, R}, journal = {International Journal of Production Economics}, keywords = {imported}, number = 94, pages = {239–252}, title = {Advanced available-to-promise: Classification, selected methods and requirements for operations and inventory management}, volume = 93, year = 2005 }
• Fallstudie: Zentrales und dezentrales Supply Chain Planning am Beispiel einer Supply Chain der Pharma-Industrie. Pibernik, R; Sucky, E. In Wirtschaftswissenschaftliches Studium, 1, S. 59–64. 2004.
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  @article{PibernikSucky2004a, author = {Pibernik, R and Sucky, E}, journal = {Wirtschaftswissenschaftliches Studium}, keywords = {imported}, pages = {59–64}, title = {Fallstudie: Zentrales und dezentrales Supply Chain Planning am Beispiel einer Supply Chain der Pharma-Industrie}, volume = 1, year = 2004 }
• Zentrales und dezentrales Supply Chain Planning. Pibernik, R; Sucky, E. In Wirtschaftswissenschaftliches Studium, 1, S. 25–31. 2004.
  • [ BibTeX ]
  @article{PibernikSucky2004, author = {Pibernik, R and Sucky, E}, journal = {Wirtschaftswissenschaftliches Studium}, keywords = {imported}, pages = {25–31}, title = {Zentrales und dezentrales Supply Chain Planning}, volume = 1, year = 2004 }
• Modellgestützte Flexibilitätsplanung in Supply Chains: Einsatz einer Software-Applikation in einem Biotechnologie-Unternehmen. Pibernik, R. In Zeitschrift für Betriebswirtschaft, 73(11), S. 1141–1165. 2003.
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  @article{Pibernik2003, author = {Pibernik, R}, journal = {Zeitschrift für Betriebswirtschaft}, keywords = {imported}, number = 11, pages = {1141–1165}, title = {Modellgestützte Flexibilitätsplanung in Supply Chains: Einsatz einer Software-Applikation in einem Biotechnologie-Unternehmen}, volume = 73, year = 2003 }
• Fallstudie: Supply Chain Configuration am Beispiel eines Biotechnologie-Unternehmens. Pibernik, R. In Wirtschaftswissenschaftliches Studium, 1, S. 54–60. 2003.
  • [ BibTeX ]
  @article{Pibernik2003c, author = {Pibernik, R}, journal = {Wirtschaftswissenschaftliches Studium}, keywords = {imported}, pages = {54–60}, title = {Fallstudie: Supply Chain Configuration am Beispiel eines Biotechnologie-Unternehmens}, volume = 1, year = 2003 }
• Einsatz dynamischer Planungsmodelle zur Unterstützung des strategischen Supply Chain Management. Pibernik, R. In Wirtschaftswissenschaftliches Studium, 1, S. 20–26. 2003.
  • [ BibTeX ]
  @article{Pibernik2003b, author = {Pibernik, R}, journal = {Wirtschaftswissenschaftliches Studium}, keywords = {imported}, pages = {20–26}, title = {Einsatz dynamischer Planungsmodelle zur Unterstützung des strategischen Supply Chain Management}, volume = 1, year = 2003 }
• Ausgewählte Methoden und Verfahren zur Unterstützung des Advanced Available to Promise. Pibernik, R. In Zeitschrift für Planung, 13, S. 345–372. 2002.
  • [ BibTeX ]
  @article{Pibernik2002, author = {Pibernik, R}, journal = {Zeitschrift für Planung}, keywords = {imported}, pages = {345–372}, title = {Ausgewählte Methoden und Verfahren zur Unterstützung des Advanced Available to Promise}, volume = 13, year = 2002 }
• Flexibilitätsplanung in Wertschöpfungsnetzwerken. Pibernik, R. In Zeitschrift für Betriebswirtschaft, 71(8), S. 893–913. 2001.
  • [ BibTeX ]
  @article{Pibernik2001, author = {Pibernik, R}, journal = {Zeitschrift für Betriebswirtschaft}, keywords = {imported}, number = 8, pages = {893–913}, title = {Flexibilitätsplanung in Wertschöpfungsnetzwerken}, volume = 71, year = 2001 }

• Quantitative Methoden in Logistik und Supply Chain Management. Jacquemin, M; Pibernik, R; Sucky, E. Hamburg, 2006.
  • [ BibTeX ]
  @book{JacqueminPibernikSucky2006, author = {Jacquemin, M and Pibernik, R and Sucky, E}, keywords = {imported}, title = {Quantitative Methoden in Logistik und Supply Chain Management}, year = 2006 }
• Flexibilitätsplanung in Wertschöpfungsnetzwerken. Pibernik, R. Gabler, Wiesbaden, 2001.
  • [ BibTeX ]
  @book{GablerWiesbaden2001, author = {Pibernik, R}, keywords = {imported}, publisher = {Gabler}, title = {Flexibilitätsplanung in Wertschöpfungsnetzwerken}, year = 2001 }

• Leistungsprogrammplanung in Wertschöpfungsnetzwerken – Fundierung, Implikationen, Grenzen. Isermann, H; Pibernik, R; Sucky, E. In Wertschöpfungsnetzwerke, S. 305–324. Berlin, 2006.
  • [ BibTeX ]
  @inbook{IsermannPibernikSucky2006, author = {Isermann, H and Pibernik, R and Sucky, E}, booktitle = {Wertschöpfungsnetzwerke}, keywords = {imported}, pages = {305–324}, title = {Leistungsprogrammplanung in Wertschöpfungsnetzwerken – Fundierung, Implikationen, Grenzen}, year = 2006 }
• Master planning in supply chains. Pibernik, R; Sucky, E. In Entscheidungsunterstützende Systeme in SCM und Logistik. Heidelberg, 2005.
  • [ BibTeX ]
  @inbook{PibernikSucky2005, author = {Pibernik, R and Sucky, E}, booktitle = {Entscheidungsunterstützende Systeme in SCM und Logistik}, keywords = {imported}, title = {Master planning in supply chains}, year = 2005 }
• Dynamische Supply Chain Configuration mit Advanced Planning Systemen. Pibernik, R. In Quantitative Methoden in ERP und APS. Paderborn. 2004.
  • [ BibTeX ]
  @inbook{Pibernik2004a, author = {Pibernik, R}, booktitle = {Quantitative Methoden in ERP und APS. Paderborn}, keywords = {imported}, title = {Dynamische Supply Chain Configuration mit Advanced Planning Systemen}, year = 2004 }
• A multi-product batch-available-to-promise model for make-to-stock manufacturing. Pibernik, R. In Operations Research Proceedings. Springer, Berlin, 2003.
  • [ BibTeX ]
  • [ URL ]
  @inbook{Pibernik2003a, author = {Pibernik, R}, booktitle = {Operations Research Proceedings}, keywords = {imported}, publisher = {Springer}, title = {A multi-product batch-available-to-promise model for make-to-stock manufacturing}, year = 2003 }

• Data-Driven Capacity Management with Machine Learning: A Novel Approach and a Case-Study for a Public Service Office. Taigel, Fabian; Meller, Jan; Rothkopf, Alexander. In INFORMS International Conference on Service Science, S. 105–115. Springer, 2018.
  • [ BibTeX ]
  @inproceedings{taigel2018data, author = {Taigel, Fabian and Meller, Jan and Rothkopf, Alexander}, booktitle = {INFORMS International Conference on Service Science}, keywords = {datadrivenoperations}, organization = {Springer}, pages = {105–115}, title = {Data-Driven Capacity Management with Machine Learning: A Novel Approach and a Case-Study for a Public Service Office}, year = 2018 }
• Strategic deployment of specialized testing and remanufacturing in a global network of contract manufacturers. Goentzel, J; Manzione, L T; Pibernik, R; Pruett, J; Thiessen, B. In Proceedings of EurOMA, International Conference on Global Operations and Competitiveness. Budapest, 2005.
  • [ BibTeX ]
  @inproceedings{GoentzelManzionePibernikEtAl2005, author = {Goentzel, J and Manzione, L T and Pibernik, R and Pruett, J and Thiessen, B}, booktitle = {Proceedings of EurOMA, International Conference on Global Operations and Competitiveness}, keywords = {imported}, title = {Strategic deployment of specialized testing and remanufacturing in a global network of contract manufacturers}, year = 2005 }
• Managing stock-outs effectively with order fulfillment systems. Pibernik, R. In Proceedings of SCMIS 2005 – 3rd International Workshop on Supply Chain Management and Information Systems. 2005.
  • [ BibTeX ]
  @inproceedings{ThessalonikiGreece2005, author = {Pibernik, R}, booktitle = {Proceedings of SCMIS 2005 – 3rd International Workshop on Supply Chain Management and Information Systems}, keywords = {Published}, note = {Best Paper Award}, title = {Managing stock-outs effectively with order fulfillment systems}, year = 2005 }
• Process Management in Supply Chains – A New Petri-Net Approach. von_Mevius, M; Pibernik, R. In Proceedings of the 37th Hawaii International Conference on System Sciences (HICSS 37), Bd. 37. 2004.
  • [ BibTeX ]
  @inproceedings{Pibernik2004, author = {von_Mevius, M and Pibernik, R}, booktitle = {Proceedings of the 37th Hawaii International Conference on System Sciences (HICSS 37)}, keywords = {imported}, title = {Process Management in Supply Chains – A New Petri-Net Approach}, volume = 37, year = 2004 }
• Advanced available to promise: Herausforderungen für das Produktions- und Logistikmanagement. Pibernik, R. In Wissenschaftssymposium Logistik. Magdeburg, 2002.
  • [ BibTeX ]
  @inproceedings{Pibernik2002a, author = {Pibernik, R}, booktitle = {Wissenschaftssymposium Logistik}, keywords = {imported}, title = {Advanced available to promise: Herausforderungen für das Produktions- und Logistikmanagement}, year = 2002 }
• Fundamentals of flexibility planning in supply chain networks. Pibernik, R. In European Logistics Association. Brussels, 1999.
  • [ BibTeX ]
  @inproceedings{Pibernik1999, author = {Pibernik, R}, booktitle = {European Logistics Association}, journal = {European Logistics Association}, keywords = {imported}, title = {Fundamentals of flexibility planning in supply chain networks}, year = 1999 }
• Simulationsgestützte Besatzungseinsatzplanung einer Luftverkehrsgesellschaft. Kaupp, M; Pauly, M; Pibernik, R. In Simulation im Luftverkehr, M. Kaupp (Hrsg.), S. 111–211. Gabler Verlag, Wiesbaden, 1996.
  • [ BibTeX ]
  @inproceedings{KauppPaulyPibernik1996, author = {Kaupp, M and Pauly, M and Pibernik, R}, booktitle = {Simulation im Luftverkehr}, editor = {Kaupp, M.}, keywords = {imported}, pages = {111–211}, publisher = {Gabler Verlag}, title = {Simulationsgestützte Besatzungseinsatzplanung einer Luftverkehrsgesellschaft}, year = 1996 }

• A Data-Driven Inventory Policy for Multi-Period, Multi-Product Inventory Planning Problems. Lauton, Felix. 2019.
  • [ BibTeX ]
  @unpublished{lauton2019datadriven, author = {Lauton, Felix}, keywords = {Inventory-management}, title = {A Data-Driven Inventory Policy for Multi-Period, Multi-Product Inventory Planning Problems}, year = 2019 }
• Drug Procurement with Long Term Considerations: Volume Splits and Volume Guarantees. Rothkopf, A; Spiliotopoulou, E. 2016.
  • [ Abstract ]
  • [ BibTeX ]
  Our analysis is motivated by recent decisions of global health procurement organizations to grant volume guarantees to existing suppliers (e.g. pentavalent vaccine, contraceptive implants, rotavirus vaccine). The basic structure of a volume guarantee is as follows: in addition to awarding the volume of the next procurement cycle to incumbent suppliers a procurer also guarantees a specific volume of future procurement cycles to these suppliers today. With this mechanism organizations aim to reduce prices through competition and higher economies of scale at the suppliers. The residual share of the future volumes remains open for competition and is awarded at a later point in time to suppliers when the upcoming cycles are up for allocation. Global health procurement organizations, however, are not only concerned with today’s price reduction but also bear in mind that future competition and new supplier entries are necessary for a healthy future market. An interesting decision problem ensues because the procurement organization has to balance committing high future volumes today to reduce prices with the potential negative impact on competition and supplier entry in the future. We use a two period model to study a global health purchaser’s decision on how to set today the volume split and the volume guarantee for future periods to (i) induce competition and reduce procurement prices among incumbent suppliers, and (ii) still motivate a new entrant supplier to enter the market. We explore the trade-off between scale economies from volume consolidation and future guarantees to reduce current prices, versus leaving the market open and splitting volumes to ensure a healthy supplier base for low prices in the long run. Our work provides structural and managerial insights into designing a long-term socially optimal procurement mechanism.

  @unpublished{RothkopfSpiliotopoulou, abstract = {Our analysis is motivated by recent decisions of global health procurement organizations to grant volume guarantees to existing suppliers (e.g. pentavalent vaccine, contraceptive implants, rotavirus vaccine). The basic structure of a volume guarantee is as follows: in addition to awarding the volume of the next procurement cycle to incumbent suppliers a procurer also guarantees a specific volume of future procurement cycles to these suppliers today. With this mechanism organizations aim to reduce prices through competition and higher economies of scale at the suppliers. The residual share of the future volumes remains open for competition and is awarded at a later point in time to suppliers when the upcoming cycles are up for allocation. Global health procurement organizations, however, are not only concerned with today’s price reduction but also bear in mind that future competition and new supplier entries are necessary for a healthy future market. An interesting decision problem ensues because the procurement organization has to balance committing high future volumes today to reduce prices with the potential negative impact on competition and supplier entry in the future. We use a two period model to study a global health purchaser’s decision on how to set today the volume split and the volume guarantee for future periods to (i) induce competition and reduce procurement prices among incumbent suppliers, and (ii) still motivate a new entrant supplier to enter the market. We explore the trade-off between scale economies from volume consolidation and future guarantees to reduce current prices, versus leaving the market open and splitting volumes to ensure a healthy supplier base for low prices in the long run. Our work provides structural and managerial insights into designing a long-term socially optimal procurement mechanism.}, author = {Rothkopf, A and Spiliotopoulou, E}, keywords = {globalhealth}, title = {Drug Procurement with Long Term Considerations: Volume Splits and Volume Guarantees}, year = 2016 }
• The Value of Entrant Manufacturers: A Study of Competition and Risk for Donor-Funded Procurement of Essential Medicines. Lauton, F; Rothkopf, A; Pibernik, R. 2016.
  • [ Abstract ]
  • [ BibTeX ]
  Global-health purchasing organizations (POs) try to increase the number of people who are treated with essential medicines in low- and low-middle-income countries. One way to purchase more medicines with limited funds is to contract generics manufacturers in order to increase competition and lower prices. However, many POs fear that these entrants are less reliable than others and that they increase supply risks: uncertain lead times and the potential for default may cause disruptions that result in unsuccessful medical treatments. Our study answers the question when does an entrant, specifically, a new generics manufacturer, provide value to the PO? Inspired by a project for donor-funded procurement of Depot Medroxyprogesterone Acetate (DMPA) of two large POs, we develop a mathematical model to capture an entrant's value depending on the volume split among manufacturers. We use data from the DMPA case to show the feasibility of our approach for POs, manufacturers, and philanthropic investors in the global-health domain. We present numerous interesting results - some counter intuitive. For example, our analyses show that the trade-off between competition benefits and risk is not as trivial as one may think and can sometimes be inverted: a risky entrant can still provide risk-diversification benefits, and procurement costs do not always decrease if the entrant has lower costs. We explore the trade-off between competition and risk and provide insights into how the entrant's value is affected by parameters like production costs, capacity, lead time, default risk and in-country

  @unpublished{LautonRothkopfPibernik2016, abstract = {Global-health purchasing organizations (POs) try to increase the number of people who are treated with essential medicines in low- and low-middle-income countries. One way to purchase more medicines with limited funds is to contract generics manufacturers in order to increase competition and lower prices. However, many POs fear that these entrants are less reliable than others and that they increase supply risks: uncertain lead times and the potential for default may cause disruptions that result in unsuccessful medical treatments. Our study answers the question when does an entrant, specifically, a new generics manufacturer, provide value to the PO? Inspired by a project for donor-funded procurement of Depot Medroxyprogesterone Acetate (DMPA) of two large POs, we develop a mathematical model to capture an entrant's value depending on the volume split among manufacturers. We use data from the DMPA case to show the feasibility of our approach for POs, manufacturers, and philanthropic investors in the global-health domain. We present numerous interesting results - some counter intuitive. For example, our analyses show that the trade-off between competition benefits and risk is not as trivial as one may think and can sometimes be inverted: a risky entrant can still provide risk-diversification benefits, and procurement costs do not always decrease if the entrant has lower costs. We explore the trade-off between competition and risk and provide insights into how the entrant's value is affected by parameters like production costs, capacity, lead time, default risk and in-country}, author = {Lauton, F and Rothkopf, A and Pibernik, R}, keywords = {globalhealth}, note = {submitted to EJOR) (Opens internal link in current windowlink}, title = {The Value of Entrant Manufacturers: A Study of Competition and Risk for Donor-Funded Procurement of Essential Medicines}, year = 2016 }
• Sourcing uncertain product quality: Competition, learning, and volume postponement. Lauton, F; Rothkopf, A. 2016.
  • [ BibTeX ]
  @unpublished{LautonRothkopf2016, author = {Lauton, F and Rothkopf, A}, keywords = {globalhealth}, title = {Sourcing uncertain product quality: Competition, learning, and volume postponement}, year = 2016 }
• Wishful Updating: The Role of Optimism When Experts Update Their Beliefs about the Future. Ecken, P; Pibernik, R. 2016.
  • [ BibTeX ]
  @unpublished{EckenPibernik2016, author = {Ecken, P and Pibernik, R}, keywords = {imported}, title = {Wishful Updating: The Role of Optimism When Experts Update Their Beliefs about the Future}, year = 2016 }
• Flexible Capacity Management with Future Information. Kurz, J; Pibernik, R. 2016.
  • [ BibTeX ]
  @unpublished{KurzPibernik2016, author = {Kurz, J and Pibernik, R}, keywords = {imported}, note = {Submitted to Operations Research}, title = {Flexible Capacity Management with Future Information}, year = 2016 }
• Queueing with Limited Future Information. Kurz, J; Pibernik, R. 2016.
  • [ BibTeX ]
  @unpublished{KurzPibernik2016a, author = {Kurz, J and Pibernik, R}, keywords = {imported}, title = {Queueing with Limited Future Information}, year = 2016 }