Abstract:In literature, most papers examine several stochastic demand processes where order lead times are constant. In reality, manufacturing firms use inventory management software, especially MRP, which ignored lead time uncertainty. It is true that in certain special cases, lead time uncertainty has essentially no effect and can be ignored. Nevertheless more often, lead time fluctuations strongly degrade tools performance and cause high production costs, just as demand uncertainty does. Seemingly, uncertainty has been neglected for a long time in favour of studying demand uncertainties. Industry agrees that it is overdue and there is a need to rectify this oversight. Nowadays, this gap in research activity begins to be filled in order to respond to companies having non-deterministic lead-times constraints. This paper reviews some of existing literature of supply planning tools under uncertainty of lead times. The extensive literature review that we compiled consists of citations from 1970 to 2012. A classification scheme for supply planning under uncertainty is defined.

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Purpose This paper aims to identify the differentiated paths followed by firms to innovate in business models, among four different strategic postures and also to determine the innovation interactions between business model components, among strategic postures. The authors intend to highlight the differentiated patterns of business model innovation (BMI) in each strategic posture and provide guidance to small and medium enterprises (SMEs) managers regarding the suitable alignments of business model components when they innovate in their business model. Design/methodology/approach The research model developed and tested in this work uses a composite model that borrows from the logic of Miles and Snow’s cycle of adaptive strategic choices as well as Demil and Lecocq’s perspective of permanent change within and between components of a business model. The authors’ model is designed first to encompass the differentiated patterns characterizing the relationships between the strategic posture of defender, prospector and analyzer profiles and the related innovation attributes of their business model components. The study was conducted with independent French manufacturing SMEs ranging from 10 to 250 employees in size and having revenues below €50m (European Commission, 2007). The analysed sample includes 169 firms from 14 sectors representative of French manufacturing SMEs. Findings Results confirm the differentiated propensity to adopt specific BMI behaviours among strategic postures. The authors also highlight the differentiated interactions between and within BMI components. These results suggest that SMEs tend to leverage specific BMI components related to their entrepreneurial, engineering and administrative choices. Thus, firms tend to evolve in a posture-specific, path-dependent dynamic consistency in which BMI attributes interact towards a limited set of alternatives, thus anchoring the new business model into strategic choices. It has been shown that the predictability of strategy–BMI alignment is contingent on the level of fit between empirically derived strategic profile attributes and Miles and Snow’s ideal profile attributes. Research limitations/implications This paper investigates strategy–BMI alignments without addressing such alignments from the standpoint of firm performance. Still, performance from a BMI perspective lies in the ability of the firm to sustain the dynamic consistency of its business model components by identifying the effects of change in interactions between and within components on overall BM performance. Further studies should explore dynamic consistency as a means for firms to generate and maintain performance by innovating in their business model when facing specific contingencies. The conceptual framework designed for the present research seems appropriate for conducting such an investigation on the performance implications of strategy–BMI fit. Practical implications This research offers insights regarding manufacturing SMEs seeking guidance when changing business strategy. Indeed, by combining Miles and Snow’s configurational framework of strategic postures with Demil and Lecocq’s RCOV BM framework, the authors provide insights that can bridge the gap between intended strategy and realized strategy. The authors suggest that when realizing new strategic choices, SMEs should favour behaviours of BMI that are likely to fit the new intended strategic posture. Accordingly, the authors introduce a set of field-based BMI alignments specific to firms’ strategic posture to support the strategic management of innovation in SMEs. Originality/value By unravelling the alignments between strategic posture and business model innovation, this work contributes to enlightening the dynamics of Miles and Snow’s adaptive cycle. Indeed, viewing Miles and Snow’s typology from the configurational perspective of BMI provides a clearer picture of the adaptive cycle through which BMI reflects the path-dependent process of the formation of the firm’s strategic posture through the transformation of its business model.

Planned lead times are crucial parameters in management of supply networks that continue to be more and more extended with multiple levels of inventory of components and uncertainties. The object of this study is the problem of determining planned lead times in multi-level assembly systems with stochastic lead times of different partners of supply chains. A general probabilistic model with a recursive procedure to calculate all the necessary distributions of probability is proposed. A Branch and Bound algorithm is developed for this model to determine planned order release dates for components at the last level of a BOM which minimize the sum of inventory holding and backlogging costs. Experimental results show the behaviour of the proposed model and optimisation algorithm for different numbers of components at the last level of the BOM and for different numbers of levels and values of holding and backlogging costs. The model and algorithm can be used for assembly contracting in an assembly to order environment under lead time uncertainty.

Purpose Many barriers prevent firms from changing their business models. Inertia, as it accumulates over time, transforms into organizational routines that doom change; however, it can also be a source of organizational flexibility. How does a business model evolve in interaction with organizational routines? This paper aims to study the interactions between forms of participative innovation (PI) and existing business models. Design/methodology/approach The exploratory approach includes interviews, participant and non-participant observations and archive analysis. It adapts an existing framework, based on the notion of scripts, to the evolutionary dynamic of organizational routines at the French railway company SNCF. The analysis of a set of contextual elements clarifies events over time and interactions between PI and the company’s business model. Findings The empirical insights indicate how existing routines can help reinvent business models. Business model components evolve along the transformation phases of PI. The case reveals co-evolutionary dynamics: evolution of the organizational routine from bureaucratic suggestion, to structured innovation, to PI leads to the transformation of the business model from functionalist, to customer-centric, to open business model. Practical implications Firm managers can think more proactively about how to reinvent established business models by innovating their existing routines, according to the position and role of routines, shifting from sources of rigidity and inertia to levers for innovation and change. Originality/value The business model concept serves as a prism of analysis for organizational routines. Organizational routines are sources of flexibility, strategic renewal and business model reinvention.

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In the last few years, there has been a growing interest in the disassembly scheduling problem to fulfill the demands of individual disassembled parts over a given planning horizon. Planners have to determine the timing and the optimal quantity of ordering the end-of-life products. This paper focuses on the case of a multi-period planning problem, a single product type and multi-level structure. In this paper, we suggest a new mixed integer linear programming model for solving the capacitated disassembly scheduling problem in order to maximize total profit while minimizing of the sum of setup, inventory holding, external procurement, disposal, backlogging and overload costs in each time period. In other words, the objective function aims to (i) maximize the profit obtained by the resale of the recovered components after disassembly, from the valuation of the end-of-life products, and (ii) to minimize the costs related to the operation of disassembly process. Our computational experiments show that the proposed model gives the optimal solutions for all the test instances within acceptable time. Sensitivity studies on capacity, procurement and sale costs are also conducted, which provide some useful insights for industrial makers.

Production planning and inventory control in supply chain are of prime importance for companies which aim to produce high quality Finished products at lowest costs and right on time. For this reason, planners must reduce average stock levels and determine optimal safety lead times. This study deals with a multi-period production planning problem with a known dynamic demand. The lead times of demands are independent, discrete random variables with known and bounded probability distributions. A general probabilistic model, including a recursive procedure to calculate the expected total cost, is derived. A Genetic Algorithm is developed for this model to determine planned lead times and safety stock level which minimize the total expected cost. The latter is equal to the sum of the backlogging and inventory holding costs. This approach is compared to three other ones to illustrate its performance. The results prove that, under certain assumptions, it could be advantageous to optimizing planned lead times rather than implementing safety stocks. To understand the effect of dispersion on the robustness of the solution, different levels of variance and different shapes of lead time distributions are studied. Different analysis proves that the variability of the lead time affects slightly the expected total cost when the unit inventory holding cost is close to the unit backlogging cost.