A business process is the combination of a set of activities within an enterprise with a structure describing their logical order and dependence whose objective is to produce a desired result. Business process modelling enables a common understanding and analysis of a business process. A process model can provide a comprehensive understanding of a process. An enterprise can be analysed and integrated through its business processes. Hence the importance of correctly modelling its business processes. Using the right model involves taking into account the purpose of the analysis and, knowledge of the available process modelling techniques and tools. The number of references on business modelling is huge, thus making it very time consuming to get an overview and understand many of the concepts and vocabulary involved. The primary concern of this paper is to make that job easier, i.e. review business process modelling literature and describe the main process modelling techniques. Also a framework for classifying business process-modelling techniques according to their purpose is proposed and discussed. © 2003 Elsevier B.V. All rights reserved.

Determining the economic lot size has always represented one of the most important issues in production planning. This problem has long attracted the attention of researchers, and several models have been developed to meet requirements at minimum cost. In this paper we explore and discuss the evolution of these models during one hundred years of history, starting from the basic model developed by Harris in 1913, up to today. Following Harriss work, a number of researchers have devised extensions that incorporate additional considerations. The evolution of EOQ theory strongly reflects the development of industrial systems over the past century. Here we outline all the research areas faced in the past by conducting a holistic analysis of 219 selected journal papers and trying to give a comprehensive view of past work on the EOQ problem. Finally, a new research agenda is proposed and discussed.

This paper considers manufacturing flexibility and real options from an industrial engineering/production management perspective. Real options papers are related to different types of manufacturing flexibility in order to show which types that are considered and in what way they are considered. Flexibility types not valued with real options and real options without any corresponding manufacturing flexibility type are identified and discussed. © 2001 Elsevier Science B.V. All rights reserved.

Flexibility in manufacturing operations is becoming increasingly more important to industrial firms, due to e.g., increasing market demand volatility, internationalisation of markets and competition, and shorter product life cycles. Defining, measuring and evaluating manufacturing flexibility have not been straightforward - neither in theory nor in practice. The use of real options has shown to be an accessible approach for the valuation of certain types of flexibility. When using real options for capital budgeting purposes it is possible to take flexibility options into account in the valuation process. In this paper, we use real options to evaluate one specific type of manufacturing flexibility, i.e., product-mix flexibility. We provide both theoretical and practical perspectives, based on a real case. The main interest of the company under study is to evaluate product-mix flexibility with respect to capacity, set-ups, level of automation and multi-functionality of resources. The case involves multiple products and demand uncertainty, wherefore product demands are used as the underlying asset in the real options models. Thus, the contribution of this paper concerns the combination of real case, multiple products, capacity constraints, and set-up costs. The results of the analysis show that (i) the value of flexibility decreases when demand volatility increases, (ii) flexible resources add substantial value as compared to dedicated resources, and (iii) the flexibility value of marginal capacity decreases with increasing levels of capacity.

This study of scheduling work in practice addresses how the production-scheduling processes in four companies are influenced by human, technological, and organizational aspects. A conclusion is that the outcome of the scheduling process is influenced by the scheduler adding human capabilities that cannot be automated, by technical constraints in the scheduled production system and by the available scheduling software tools. Furthermore, the outcome is influenced not only by how the scheduling process is formally organized, but also by the scheduler's informal authority and the role taken to interconnect activities between different organizational groups. The findings from the study support a number of previous studies done on scheduling in practice whilst giving new insights into their interpretation.

MRP Theory has been developed during the last 25 years for capturing processes concerning multi-level, multi-stage production-inventory systems in a compact way. Input-output analysis has been used to describe structures, and Laplace transforms to describe the timing relations. This theory has mainly dealt with assembly systems, in which each item has only one successor. The lead times for the assembly of an item have usually been constants and equal for all items entering a given successor. For such systems, the equations describing the flows of components may be written to include the generalised input matrix as the product of an input matrix containing needed amounts, and a diagonal lead time matrix with lead time operators along its main diagonal. less thanbrgreater than less thanbrgreater thanOn occasion, there has been a need to deviate from this representation enabling lead times to vary depending on which input item that is considered. This paper deals with how to represent lead times and similar output delays (in diverging, arborescent systems), when the assumption of equal times is relaxed, in order to retain the basic structure of the fundamental balance equations involved. The intention of this paper is to create a general taxonomy for the representation of timing in algebraic form for a variety of systems covering both assembly systems and arborescent systems (such as extraction, distribution and remanufacturing), as well as systems with mixed properties. For instance, this method may be used directly for the evaluation of investments in capacity or in the location of activities in a production network, or even in a global supply chain.

In this paper we extend and apply MRP theory towards reverse logistics including the considerations of transportation consequences. Our aim is to demonstrate the versatility obtained from using MRP theory when combining Input-Output Analysis and Laplace transforms. This enables an analysis of a supply chain including four sub-systems, namely manufacturing, distribution, consumption and reverse logistics, where the geographical distance between the activities play an important role. The main focus in this paper is on reverse logistics (recycling, remanufacturing). Especially we wish to model the evaluation of disposal and reverse activities far away from agglomerations, which often means an improved environment for nearby inhabitants. This is also illustrated in a numerical example. We use the Net Present Value as a measure of the economic performance. Our ambition is to show that supply chain sub-systems may accurately be described using input and output matrices collected together in corresponding matrices for the system as a whole. Activity levels in each sub-system govern the speed of the respective processes, and these activity levels, in general, will be considered as decision variables. We now analyse reverse logistics activities in which the flows of materials and goods are typically divergent (arborescent processes), similar to properties of the distribution sub-system, and recent results on the extensions of basic MRP theory introducing the concepts of output delays and the generalised output matrix are also introduced here, when modelling the reverse logistics sub-system.

Inefficient locations for production, distribution and reverse logistics plants will result in excess costs no matter how well material requirements planning (MRP), inventory control, distribution and information sharing decisions are optimized. In this paper we study ways in which aspect of activity cell location decisions can be analyzed within an extended MRP model. This model has previously been extended by including distribution and reverse logistics components in a compact form, presented in Grubbstrom et al. (2007). Our aim is to demonstrate the basic differences between an approach to location problems with MRP "under the same roof" as the global supply chain, in which transportation time delays and direct transportation costs have substantial influence. We discuss possibilities of how to present location aspects in the supply chain model obtained from combining input-output analysis and Laplace transforms in four sub-systems, namely manufacturing, distribution, consumption and reverse logistics, and show how the transportation costs and lead time influenced by the location of all these activities affect the resulting net present value (NPV). Our aim is to build a model supporting decisions concerning the structure of a supply chain as an alternative to a mixed integer programming formulation. The model developed is based on the use of continuous functions describing spatial distributions of cost and customer demand. Continuous functions are embedded in the MRP extension previously introduced in Grubbstrom et al. (2007). Location decisions influence (i) production costs, because timing influences the cost of activities involved in creating a product, cf. (Grubbstrom and Bogataj, submitted for publication), and (ii) logistics costs, which refer to the procurement and physical transmission of materials through the supply chain. In this current paper we wish to combine both of these aspects into a comprehensive model, where we show the interaction between the "space of flows" and the "space of places" as Giovanni Arrighi distinguishes one from the other in his book The Long Twentieth Century.

In the aftermath of large-scale natural disasters, supply shortage and inequitable distribution cause various losses, hindering humanitarian supply chains? performance. The optimal decisions are difficult due to the complexity arising from the multi-period post-disaster consideration, uncertainty of supplies, hierarchal decision levels and conflicting objectives in sustainable humanitarian supply chains (SHSCs). This paper formulates the problem as a fuzzy tri-objective bi-level integer programming model to minimize the unmet demand rate, potential environmental risks, emergency costs on the upper level of decision hierarchy and maximize survivors? perceived satisfaction on the lower level of decision hierarchy. A hybrid global criterion method is devised to incorporate a primal-dual algorithm, expected value and branch-and-bound approach in solving the model. A case study using data from the Wenchuan earthquake is presented to evaluate the proposed model. Study results indicate that the hybrid global criterion method guides an optimal strategy for such a complex problem within a reasonable computational time. More attention should be attached to the environmental and economic sustainability aspects in SHSCs after golden rescue stage. The proposed bi-level optimization model has the advantages of reducing the total unmet demand rate, total potential environmental risks and total emergency costs. If the decision-agents with higher authorities act as the leaders with dominant power in SHSCs, the optimal decisions, respectively taking hierarchical and horizontal relationships into account would result in equal performance.

Whether the corporate social performance affects the financial performance is still unclear in many manufacturing companies. We commonly expect, on one side, that profitable corporations have stronger incentives to reveal information on social performance in order to improve their publicity; on the other hand, companies may face the fear of rising costs due to Corporate Social Responsibility (CSR) activities. With increasing concerns of CSR, it is timely to investigate the relationship between the disclosure of corporate social performance and financial performance. In this paper with the above study objective, we use Global Reporting Initiative (GRI) reports of 75 sample companies, collect evidences by applying the method of structured content analysis of the cases and attempt to identify this relationship. The corporate social performance is measured by the indicators according to the GRI guidelines, i.e. within the categories of Labor practices and decent work, Human Rights, Society as well as Product responsibility. Financial performance is measured by return on equity, sales growth and cash flow/sales ratio. Using statistical evaluation methods, our results indicate that the categories of Human Rights, Society as well as Product responsibility display a significant and positive correlation with the return on equity. Same conclusion also holds for many CSR indicators. Nevertheless, when examining the CSR practices across different manufacturing sectors, we have not observed significant differences. The study results are important for understanding the development and implementation of CSR practices in the manufacturing industry.

The perspectives on the manufacturing footprint of global firms are widening from the economic aspects to also include the environmental and social aspects. Thus, sustainability is becoming an important issue for the location of manufacturing facilities. It is therefore timely to review the relevant aspects and dimensions in the extant literature to investigate the relationship between sustainability and facility location. In this paper, we aim to understand how sustainability aspects are included in decision-making concerning manufacturing facility locations and the role of location in evaluating manufacturing sustainability. We examine the literature streams on sustainability and facility location. A comprehensive search includes peer-reviewed literature from 1990 to 2011. We propose a literature classification scheme with respect to focal area and research methodology. The content analysis identifies the environmental, social and economic perspectives and factors affecting location decisions. We synthesize the findings into a framework for taking sustainability aspects into account in manufacturing facility location decision-making. We also propose a research agenda for further research on sustainable locations.

New technology is altering business strategies and innovation capabilities while increasing the possibilities of production and process innovation. Supply chain collaboration undertaken for the sake of sustainability is currently speeding up this process of change; a growing pool of research is exploring the links between sustainability collaboration and company performance on economic, environmental, and social metrics. It is a good time to review the literature to reveal what has been studied and what are the gaps in the current body of knowledge, and also to comment on what the future research agenda should include. For these purposes, the authors conducted a systematic literature review and a quantitative bibliometric analysis. Results indicate that research about supply chain collaboration for the purpose of sustainability is gaining growing attention in the business field; however, environmental and economic considerations still dominate the research, while there is a lack of consideration about social concerns such as child labor and personal development. In addition, the collaboration partners under investigation have mainly been the company and its customers and suppliers, whereas competitors and other horizontal collaboration partners have received little attention.

Negative impacts on environment such as carbon emissions and pollution resulting from the business activities of firms in a supply chain have attracted great attention worldwide. Hence, one of the crucial issues for supply chain management is the trade-off between economic objectives and environmental sustainability. This paper focuses on investigating the government policy incentive mechanism, with which supply chain members are motivated to work collaboratively to reduce carbon and pollutant emissions by investing in producing environmental-friendly products (EFP). Such a mechanism affects the transfer price through negotiation between the supply chain firms, and consequently it has an impact on the supply chains value transition and profit allocation. A collaborative supply chain decision-making framework is formulated with environmental constraints and carbon caps; its objective is to maximize the net present value of an integrated supply chain as well as satisfy the interests of its individual members. Our key contribution lies in exploring a decision-making mechanism for an environmentally sustainable supply chain that is jointly constrained by environmental carrying capacities and carbon caps, and also takes into account government policy incentives. The results show that collaboration between supply chain members plays a crucial role in improving their environmental performance, as the transfer price is determined through negotiations to share government subsidies to satisfy the individual interests of supply chain members while the level of pollutants and carbon emissions are in compliance with environmental standards.(C) 2016 Elsevier B.V. All rights reserved.

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Pay-per-use services are an interesting phenomenon, both empirically and theoretically. Despite the alleged benefits, companies still struggle to succeed with pay-per-use services. Theoretically, it is common sense that existing service capabilities cannot easily be converted into organizational capabilities for pay-per-use services. Based on multiple case studies, the present article narrows down the existing empirical and theoretical gaps through an inductive research approach. Our findings make the following contributions to the field of services in product-oriented companies and to the servitization literature. We describe the organizational capabilities necessary for pay-per-use services (e.g., financing such services, aligning costs with equipment usage, and collaborating with customers). We reveal that companies should convert these capabilities into three core competencies (strategizing pay-per-use services, utilizing technologies for pay-per-use services, and de-risking pay-per-use services), in order to achieve competitive advantages.

The dynamic lotsizing problem concerns the determination of optimal batch quantities, when given required amounts appear at discrete points in time. The standard formulation assumes that no shortages are allowed and that replenishments are made instantaneously.

For the case when no shortage is allowed, previously it has been demonstrated that the inner-corner condition for an optimal production plan in continuous time reduces the number of possible replenishment times to a finite set of given points at which either a replenishment is made, or not. The problem is thus turned into choosing from a set of zero/one decisions with 2ⁿ⁻¹ alternatives, of which at least one solution must be optimal, where n is the number of requirement events. Recently, the instantaneous replenishment assumption has been replaced by allowing for a finite production rate, which turned the inner-corner condition into a condition of tangency between the cumulative demand staircase and cumulative production.

In this paper we investigate relationships between optimal cumulative production and cumulative demand, when backlogging is permitted. The production rate is assumed constant and cumulative production will then be a set of consecutive ramps. Cumulative demand is a given staircase function. The net present value (NPV) principle is applied, assuming a fixed setup cost for each ramp, a unit production cost for each item produced and a unit revenue for each item sold at the time it is delivered.

Among other results, it is shown that optimal cumulative production necessarily intersects the demand staircase. Instead of having 2ⁿ⁻¹ production staircases as candidates for optimality, there are 2ⁿ⁻¹ production structures as candidates. These are made up of sequences of batches, of which the set of batches may be optimised individually. Also is shown that the NPV of each batch has a unique timing maximum and behaves initially in a concave way and ends as convex.

Results for the average cost approach are obtained from a zeroth/first order approximation of the objective function (NPV).

The dynamic lotsizing problem concerns the determination of optimally produced/delivered batch quantities, when demand, which is to be satisfied, is distributed over time in different amounts at different times. The standard formulation assumes that these batches are provided instantaneously, i.e. that the production rate is infinite.

Using a cumulative geometrical representation for demand and production, it has previously been demonstrated that the inner-corner condition for an optimal production plan reduces the number of possible optimal replenishment times to a finite set of given points, at which replenishments can be made. The problem is thereby turned into choosing from a set of zero/one decisions, whether or not to replenish each time there is a demand. If n is the number of demand events, this provides 2ⁿ⁻¹ alternatives, of which at least one solution must be optimal. This condition applies, whether an Average Cost approach or the Net Present Value principle is applied, and the condition is valid in continuous time, and therefore in discrete time.

In the current paper, the assumption of an infinite production rate is relaxed, and consequences for the inner-corner condition are investigated. It is then shown that the inner-corner condition needs to be modified to a tangency condition between cumulative requirements and cumulative production.

Also, we have confirmed the additional restriction for feasibility in the finite production case (provided by Hill, 1997), namely the production rate restriction. Furthermore, in the NPV case, one further necessary condition for optimality, the distance restriction concerning the proximity between adjacent production intervals, has been derived. In an example this condition has shown to reduce the number of candidate solutions for optimality still further. An algorithm leading to the optimal solution is presented.

In this paper we attempt to derive properties of the distribution of the net present value NPV, when the un-derlying cash flow is affected by stochastic disturbances in the form of a random walk process, the disturbances thus jumping continuously between each of two values. A leverage is introduced, affecting the size of each jump in relation to time.A given time period is split into a number of sub-periods of equal length. During each sub-period, a fraction of altogether one monetary unit is transferred. This cash flow is subject to the random walk, and is discounted by a given constant discount rate. We study consequences on the distribution of the net present value as the number of sub-periods increases beyond all bounds.Two different processes are studied, which turn out to have rather different properties. In the first, Case A, the two levels between each jump, are chosen as each others negative, and in the second, Case B, except for a timing factor, as each others inverse. Among other consequences, it is shown that the probability distribution of the NPV in Case A is close to, but different from, a lognormal distribution, and that in Case B, the NPV indeed has a lognormal distribution, but lies stable at a constant value. Also is shown that in Case A, the leverage must be at least 1/2 for a limiting distribution to exist, and in Case B at least unity. For leverage values above these values, respectively, the random walk will not affect NPV, and the net present value becomes deterministic.

In this paper we attempt to provide a partial answer to the question of why energy is a scarce resource. Scarcity is a fundamental concept in the science of economics. If resources, goods or services were not in scarce supply, we need not economise when utilising them. Indeed, free commodities we need not pay for, their prices are zero, we attach no economic value to them, and their supply is in abundance - at least beyond the point at which our needs and wants are satisfied. However, energy is regarded as a scarce resource, although energy - as such - is not scarce. To describe energy as a useful and therefore a valuable quantity, to which a price may be attached, energy will thus have to be characterised in further dimensions than energy content alone. Apart from quantity, there is a need for a uniform qualitative measure of energy. The obvious field to revert to for such considerations is thermodynamics, which offers a method for defining a uniform measure for the qualitative content of energy, namely exergy. Although exergy is defined from purely physical properties, it is shown to have an important role to play when comparing the economic value of energy in different forms. In particular, this paper will focus on the economic value of heat, especially heat delivered through a district heating system. The concept of exergy is defined from maximising a work output reversibly taking an infinite time. However, for processes to run within finite horizons, entropy must be generated. This leads us to add finite time considerations from examining consequences from the assumed availability of so-called endo-reversible processes. In a small case example we show that heat appears to be overpriced compared to electricity from an exergetic point of view and that this is even more pronounced adopting finite time considerations. (C) 2015 Elsevier B.V. All rights reserved.

In this paper we compare the two basic risk preference measures suggested independently by John W. Pratt (1964) and Kenneth J. Arrow (1965) as to their impact on simple production decisions with uncertain economic outcomes. We develop a further related concept by introducing a risk aversion leverage measuring the dependence of absolute risk aversion on wealth. We also discover a remarkable approximate relation between risk aversion measures of different leverages making the corresponding risk preference functions similar and leading to a method for estimating different Certainty Monetary Equivalents using zero-leverage expressions. We concentrate on three simple cases of production decisions of the Newsboy type.

In this paper we base our study on the application of the Laplace transform to risk preference theory. With a constant measure of absolute risk aversion (Pratt, 1964; Arrow, 1965), the Certainty Monetary Equivalent (CME) of a risky project previously has been developed into an expression involving the logarithm of the bilateral Laplace transform of the probability density of its stochastic economic outcome. The internal risk aversion (IRA) is the break-even level of the absolute risk aversion, between making the project favourable or unfavourable. Below, we apply this methodology to determining an expression for the incremental risk rate of interest in the case of standard investments with payments having probability distributions. A simple approximate formula is derived explaining how the incremental risk rate of interest depends on the absolute risk aversion and the discount rate, i.e. on measures of risk preference and of time preference.

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A substantial amount of research has been carried out on capacity-constrained production-inventory systems. This has mostly dealt with models assuming deterministic demand and single-item systems. In previous work, one of the authors together with a co-researcher designed a basic theoretical model for systems with multiple items and stochastic external demand. These developments were presented within a discrete time framework. Lead times were assumed to be given constants and the net present value principle was applied. Although the theory developed concerned situations in which the lead times could be any non-zero constants, in order to design an analytical solution procedure, the assumption was made that lead times were zero, in order to be able to apply dynamic programming. Cumulative production and cumulative demand were taken as state variables. In this paper, we remain in the discrete time framework and develop a methodology for the case that lead times are non-zero, whereas demand is deterministic. Our emphasis is on the design of the state space, the properties of which depend on the product structures (the input matrix), the distribution of lead times among the production processes (the lead time matrix), and on the historical sequences of the production vectors. Once an efficient state space is designed, dynamic programming may be applied as a solution method. The net present value principle is again applied. © 2005 Elsevier B.V. All rights reserved.

In a sequence of previous papers, the Laplace transform and Input–Output Analysis have been applied to formulate a theoretical basis for material requirements planning (MRP) under different levels of generalisation. Searching for the optimal safety stock is one of the extensions of this theory. This paper investigates the safety stock properties in the case that the time interval of demand is Gamma-distributed. The Generalised Leontief inverse plays an effective role in describing the multi-level production–inventory system when the subordinate products are produced according to a lot-for-lot (L4L) policy.

Decisions for planning production activities for multi-period production–inventory systems have been studied in a number of papers applying input–output analysis and the Laplace transform. The decisions have concerned activities spread out over time without having the opportunity to adjust future decisions when the external and/or internal circumstances change. In this paper, we extend the analysis to situations when rescheduling is possible. Firstly, different classes of causes justifying rescheduling activities are presented including periodic rescheduling and “net change”. Secondly, in terms of previously developed theory, we model the behaviour of a simple single-level production–inventory system for which its production plan may be modified at one point in the future.

MRP Theory combines the use of Input-Output Analysis and Laplace transforms, enabling the development of a theoretical background for multi-level, multi-stage production-inventory systems together with their economic evaluation, in particular applying the Net Present Value principle (NPV). less thanbrgreater than less thanbrgreater thanIn a recent paper (Grubbstrom et al., 2010), a general method for solving the dynamic lotsizing problem for a general assembly system was presented. It was shown there that the optimal production (completion) times had to be chosen from the set of times generated by the Lot-For-Lot (L4L) solution. Thereby, the problem could be stated in binary form by which the values of the binary decision variables represented either to make a production batch, or not, at each such time. Based on these potential times for production, the problem of maximising the Net Present Value or minimising the average cost could be solved, applying a single-item optimal dynamic lotsizing method, such as the Wagner-Whitin algorithm or the Triple Algorithm, combined with dynamic programming. less thanbrgreater than less thanbrgreater thanThis current paper follows up the former paper by investigating the complexity defined as the number of possible feasible solutions (production plans) to compare. We therefore investigate how properties of external demand timing and properties of requirements (Bill-of-Materials) have consequences on the size of this solution space. Explicit expressions are developed for how the total number of feasible production plans depends on numbers of external demand events on different levels for, in particular, the two extreme cases of a serial system and a full system (the latter, in which items have requirements of all existing types of subordinate items). A formula is also suggested for general systems falling in between these two extremes. For the most complex full system, it is shown that the number of feasible plans will be the product of elements taken from Sylvesters sequence (an instance of doubly exponential sequences) raised to powers depending on numbers of external demand events.

A great deal of research has been done on capacity-constrained production–inventory systems, most of which concerns deterministic demand situations and single-product systems. In this paper we present a model of a multi-level capacity-constrained system when external demand is stochastic. Unlike the traditional total cost objective, adopted in the vast majority of capacity-constrained production–inventory models, the (expected) Net Present Value is here used as the objective function. Dynamic programming is chosen as the solution procedure. Numerical examples are given to explain the model and to illustrate features when changing available capacity.

The Laplace transform together with input–output analysis are employed as tools to construct the model. This approach has been used in previous research to formulate a theoretical base for Material Requirements Planning (MRP) systems.

The paper provides a further argument for the use of transforms in combination with matrix representations of product structures and capacity requirements, and it extends previous theory in the direction of capacity considerations combined with uncertainty in external demand. Dynamic programming is also shown to be a practical method for the multi-stage optimisation involved.

The numerical examples further illustrate, for instance, the natural propensity for subordinate items to be lot sized in a more lumpy way than their parents, and also how the marginal benefit of capacity increments follows the law of diminishing returns. Also comparisons are made with solutions from the deterministic equivalence model, using average demand as a proxy.

In this paper, the integrated planning of production and distribution for a pulp company is considered. The tactical decisions included regard transportation of raw materials from harvest areas to pulp mills; production mix and contents at pulp mills; inventory; distribution of pulp products from mills to customers and the selection of potential orders and their levels at customers. The planning period is one year and several time periods are included. As a solution approach we make use of two different heuristic approaches. The main reason to use heuristics is the need for quick solution times. The first heuristic is based on a rolling planning horizon where iteratively a fixed number of time periods is taken into consideration. The second heuristic is based on Lagrangian decomposition and subgradient optimization. This provides optimistic bounds of the optimal objective function value that are better than the LP relaxation value, which can be used as a measure of the heuristic (pessimistic) solution quality. In addition, we apply the proposed rolling horizon heuristic in each iteration of the subgradient optimization. A number of cases based on real data is analysed which shows that the proposed solution approach is simple and provides high quality solutions.

In this paper we describe a production planning and scheduling problem in an oil refinery company. The problem concerns the planning and the utilization of a production process consisting of one distillation unit and two hydro-treatment units. In the process crude oil is transformed to bitumen and naphthenic special oils. The aim of the scheduling is to decide which mode of operation to use in each processing unit at each point in time, in order to satisfy the demand while minimizing the production cost and taking storage capacities into account. The production cost includes costs for changing mode and for holding inventory. We formulate a mixed integer linear programming model for the scheduling problem. The model can be regarded as a generalized lot-sizing problem, where inventory capacities are considered and more than one product is obtained for some modes of operation. A number of modifications and extensions of the model are also discussed. It is shown how the optimization model can be used as a viable tool for supporting production planning and scheduling at the refinery, and that it is possible to analyze scheduling scenarios of realistic sizes. It is also shown that the model can support shipment planning and strategic decisions concerning new products and investments in storage capacity. © 2002 Elsevier Science B.V. All rights reserved.

Increasingly, digital platforms connect suppliers, customers, and other ecosystem parties, facilitating the introduction of various Platform-as-a-Service (PaaS) offerings. This study examines the co-evolution of actor engagement (AE), reflecting actors' dispositions to invest resources in interactions, and value co-creation (VCC), demonstrated through reciprocal resource integration. The focus is on PaaS initiatives in the asset management industry. Using a comparative case study approach, we analyze two fintech-generated PaaS offerings: a freemium model and a subscription-based model. Our findings reveal distinct pathways for platform development and scaling, driven by the interplay between AE and VCC. The freemium model fosters a "viral" community-building path, where early VCC serves as the engine for AE-VCC co-evolution. Conversely, the subscription model follows a "controlled" ecosystem-building path, with AE driving the process. We observe a gradual involvement of additional ecosystem partners, leading to upgraded solutions, expanded PaaS offerings, and platform upscaling. We identify key factors influencing these dynamics, including business model characteristics, actor roles, and feedback loops. Our study contributes to the literature on digital servitization and platform ecosystems by highlighting the importance of integrating AE and VCC practices to enhance platform utilization and scalability. We propose a framework and research propositions to guide future studies on PaaS development in complex, institutionalized industries. These insights also provide implications for managers aiming to implement effective PaaS strategies and foster innovation in the financial services sector.

Firms using industrial goods as a resource in their own operations need support and services to maintain an efficient use of these resources. Education, spare parts and maintenance are just some examples of services required by many industrial customers. These services make up a large part of many industrial companies purchase budget, but, even more importantly, for the supplier these services often make up a substantial proportion of the company's profit. There is also a trend towards the integration of goods and services. However, there is little help available on strategies for the efficient supply or manufacture of such services. An operations strategy should not be limited to supporting just new sales if the after-sales market of industrial services has a large impact on the company's competitive advantage. A complete operations strategy should therefore be linked not only to the marketing strategy, but also to a service strategy of the company. In this paper we take the supplier's view on the task of providing industrial services, i.e. the supply of after-sales services, including tangibles such as spare parts and consumables, related to the maintenance of industrial goods. We focus on the positioning of industrial services relative manufacturing, aiming at an integrated approach for manufacturing and service operations management. We extend the product-profiling concept of Hill to service operations, developing the concept of industrial service profiling, providing a detailed analysis of market and service offering characteristics relative production characteristics. The resulting profile reveals possible mismatches in the existing operations, and can also be used to identify areas in need of corrective actions.

After-sales service in firms manufacturing and selling durable goods has a strategic relevance in its potential contribution to company profitability, customer retention and product development. This paper addresses the configuration of the after-sales supply chain. Three configuration choices are analysed: The degree of vertical integration, the degree of centralisation, and the decoupling of activities (i.e. how activities are decomposed and separated organisationally). Exploratory case study research was performed over seven companies belonging to durable consumer goods industries. The empirical findings show that configuration choices vary, suggesting that no “one best way” exists. Moreover, many firms develop multiple configurations. Choices are influenced by drivers, including the attractiveness of the after-sales business, the strategic priorities, the characteristics of the physical product and the services offered, and the configuration of the manufacturing and distribution supply chain. The paper discusses how these drivers may lead to consistent configuration choices, and how choices are related.

We analyze consumers choice and overflow behavior between two potential market segments with different fares, capacity allocated, and then develop the optimal capacity and pricing polices assuming that such consumers strategic behavior can be observed. Every consumer prefers to choose a fare to obtain their utmost value surplus, and select the second if the first-best choice cannot be satisfied. Our study indicates that the effort of fencing the segments should be considered to cope properly with pricing and capacity decisions in order to direct the overflows. Disregarding overflows could create differences in decisions as well as economic consequences. The study results can be implemented, but not limited, to understand the flight seat allocation problem with strategic consumers.

This paper focuses on linkages between strategic service sourcing decisions and the competitiveness of business firms. It responds to calls for more research focusing on the outcomes of services and the nature and durability of service benefits and contributes with relatively comprehensive, detailed and managerially oriented knowledge regarding the linkages between service sourcing and competitive advantage. The paper reviews the literature and develops propositions regarding how different make-or-buy decisions, supply-base structures, and types of buyer-seller relationships influence the possibility of simultaneously maintaining positive bonds with customers, keeping costs low, and developing valued and differentiated offerings. The practical applications of the propositions are illustrated with four case studies. © 2008 Elsevier B.V. All rights reserved.

This paper provides a systematic literature review of Sales and Operations Planning (S&OP) in process industries. The aim is to investigate the present state of S&OP in process industries in comparison to discrete manufacturing industries and to identify the desired future state of the S&OP process based on the specific characteristics of process industries. The findings of this paper show that this issue has not received much attention in the academic world. Hence there is a need for conceptual models with focus on process industries’ specific characteristics. Process industries are actually hybrids of continuous production and discrete production (respectively, upstream and downstream of the discretization point) and the specific characteristics of process industries are related to the continuous production part. Thus, in this paper, the characteristics upstream of the discretization point are investigated and the integration of them in the S&OP process of the process industries is stressed.

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The order penetration point (OPP) defines the stage in the manufacturing value chain, where a particular product is linked to a specific customer order. Different manufacturing environments such as make-to-stock (MTS), assemble-to-order (ATO), make-to-order (MTO) and engineer-to-order all relate to different positions of the OPP. These may be considered as product delivery strategies, having different implications for manufacturing objectives such as customer service, manufacturing efficiency and inventory investment. Furthermore, the OPP may differ between products and over time for a particular manufacturing firm. In this paper, the positioning of the OPP is treated from a strategic perspective. Market, product, and production factors are identified that affect the OPP positioning and the shifting of the OPP upstream or downstream in the manufacturing value chain. The major factors are demand volume and volatility, and the relationship between delivery and production lead times. These factors are included in a model that allows the manufacturing firm to choose the right product delivery strategy. Different manufacturing strategies must be developed for pre-OPP operations (i.e. upstream, forecast-driven) vs. post-OPP operations (i.e. downstream, customer-order-driven), since these two stages are fundamentally different. As a consequence, a manufacturing firm that has an ATO product delivery strategy must differentiate between MTS operations (upstream the OPP) and MTO operations (downstream the OPP). For example, the competitive priorities differ: price for pre-OPP operations but delivery speed and flexibility for post-OPP operations. Therefore, decision categories, such as production planning and control, and performance measurement must be designed accordingly. Guidelines are provided for this strategic choice. ⌐ 2003 Elsevier Science B.V. All rights reserved.