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Sigma Designs Maxima Pro Driver
The supply-chain process is a complex, composite business process comprising a hierarchy of different levels of value-delivering business processes.
Design of six sigma supply chains.
Achieving superior delivery performance is the primary objective of any industry supply chain. Quick and timely deliveries entail high levels of synchronization among all business processes from sourcing to delivery. This in turn calls for variability reduction all along the supply chain. Variability reduction and business-process synchronization are therefore acknowledged as key to achieving superior levels of delivery performance in supply-chain networks.
Lead times of individual business processes and the variabil- ities Sigma Designs Maxima Pro the lead times are key determinants of end-to-end de- Sigma Designs Maxima Pro performance in supply-chain networks. When the number of resources, operations, and organizations in a supply-chain increases, variability destroys synchronization among the indi- vidual processes, leading to poor delivery performance. On the other hand, by reducing variability all along the supply chain in an intelligent way, proper synchronization can be achieved among the constituent processes.
This motivates us to explore variability reduction Sigma Designs Maxima Pro a means to achieving outstanding de- livery performance. We approach this problem in an innovative way by looking at a striking analogy from mechanical design tolerancing.
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Variability reduction is a key idea in the Sigma Designs Maxima Pro tolerancing approaches that are widely used in mechanical-design toler- ancing . A complex supply-chain network is much like a complex electromechanical assembly. Each individual business process in a given supply-chain process is analogous to an indi- vidual subassembly.
Minimizing defective or out-of-date deliv- eries in supply chains can therefore be viewed as minimizing tol- erancing defects in electromechanical assemblies. This analogy provides the motivation and foundation for this paper.
In statistical-design tolerancing, process capability indexes PCIs such as and ,  provide an elegant framework for describing the effects of variability. Best prac- Sigma Designs Maxima Pro such as the Motorola six sigma MSS program  and Taguchi methods  have been extensively used in design toler- ancing problem solving.
In this paper, we use these popular ap- proaches in a unifying way to address variability reduction, Sigma Designs Maxima Pro chronization, and delivery performance improvement in supply- chain networks. The contribution of this paper is two fold.
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Using this analogy, we introduce the notion of six sigma supply chains. We show that the design of six sigma supply chains can be expressed in a natural way as a mathematical programming problem. This provides an appealing framework for studying a rich variety of design optimization and tactical decision making problems in the supply-chain context. The second part of the paper proves the potential of the proposed methodology by focusing on a specific design op- timization problem which we call the inventory optimization IOPT problem.
We investigate this problem with the specific objective of tying up design of six sigma supply Sigma Designs Maxima Pro with supply-chain IOPT. Given a multistage supply-chain network, the IOPT problem seeks to find optimal allocation of lead time variabilities and inventories to individual stages, so as to achieve required levels of delivery performance in a Sigma Designs Maxima Pro fective way.
The study uses a representative liquid petroleum gas LPG supply-chain network, with four stages: The results obtained are extremely Sigma Designs Maxima Pro for a supply-chain asset manager to quantitatively assess inventory-service level trade offs.
For example, a supply-chain manager for the LPG supply chain will be able to determine the optimal number of LPG trucks to keep at the regional depot Sigma Designs Maxima Pro and the optimal way of choosing logistics providers, so as to ensure six sigma delivery of LPG trucks to destinations. All supply chains, regardless of whether they are dealing with discrete parts or continuous pro- cesses can be modeled as a discrete event dynamical system.
The continuous processes that may Sigma Designs Maxima Pro individual subsystems are modeled at an aggregate level, by representing their state only at discrete epochs of time and through a stochastic repre- sentation of the lead time of the process. We only need to model the starting epoch and completion epoch of the activities.
In our view, the concepts and approach developed in this paper provide a framework in which a rich variety of supply- chain design and tactical decision problems can be addressed. Relevant Work The subject matter of this paper falls in the intersection of several topical areas of research. Cycle-time compression in business processes using vari- ability reduction is the subject matter of a large number of Sigma Designs Maxima Pro in the last decade.
See, for example, the paper by Narahari et al. Hopp and Spearman, in their book , have brought out different ways in which variability reduction can be used in compressing lead times of machining and other Sigma Designs Maxima Pro processes. Lead time compression in supply chains using variability reduction techniques is the subject of several recent papers, see, for example, Narahari et al.
Statistical-design tolerancing is Sigma Designs Maxima Pro mature subject in the de- sign community. The key ideas in statistical-design tolerancing which provide the core inputs to this paper are: IOPT in supply chains is the topic of numerous papers in the past decade. Important ones of relevance here are those on multi- echelon supply chains —. Variability reduction is a cen- tral theme in many of these papers.
Recent work by Schwartz and Weng  is particularly relevant here.
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