The Impact of Complexity, Rate of Change and Information Availability on the Production Planning and Control Structure

The organizational theory literature argues that the more uncertain the environment, the more likely the firm’s operational decision structure is decentralized. However, it remains unclear which uncertainty dimensions (i.e. complexity, rate of change and lack of information) impacts the production planning and control structure the most given today’s turbulent manufacturing environments. Based on 206 responses from medium sized Dutch discrete parts manufacturing firms, this study retests the impact of these uncertainty dimensions. This study indicates that each dimension of uncertainty affects the production planning and control structure in a different way. In general, complexity, rate of change and lack of information result in a decentralization of the operational planning and control decision structure, but at the same time a centralization of the customer-order processing decision structure.empirical research method;production planning and control structure;structural equations model;uncertainty

The impact of innovation and organizational factors on APS adoption: Evidence from the Dutch discrete parts industry

Advanced Planning and Scheduling (APS) systems have gained renewed interest from academics and practitioners. However, literature on APS adoption is scant. This study explores the impact of organizational and innovation related factors on the adoption of APS systems from a factors approach. The results from our field survey of 136 Dutch discrete manufacturing firms, show that management support, cost of purchase, number of end-products, and the value that firms attach to other users’ opinions are key-factors that directly influence the adoption of APS systems. In addition, professionalism, external communications, and innovation experience indirectly influence APS adoption.innovation;impact;advanced planning and scheduling (APS) systems;causal model;factors research;organizational context

A simpler and faster algorithm for optimal total-work-content-power due date determination

Due date determination problems comprise scheduling problems in which not only the scheduling of n jobs is involved, but the assignment of due dates to jobs as well. This paper considers the case where a schedule is given and there is a single decision variable involved: the due date multiplier. The multiplier is used to set the due dates in order to minimize a composite objective function. Recently, an O(n2) algorithm was presented, the validity of which was proved on basis of the dual of the linear programming formulation of this problem. We give a simpler and faster algorithm based upon strictly primal arguments, requiring only O(n log n) time. In addition, we point out that these arguments can be employed for alternative proofs in case of a common due date

Minimizing total inventory cost on a single machine in just-in-time manufacturing

The just-in-time concept decrees not to accept ordered goods before their due dates in order to avoid inventory cost. This bounces the inventory cost back to the manufacturer: products that are completed before their due dates have to be stored. Reducing this type of storage cost by preclusion of early completion conflicts with the traditional policy of keeping work-in-process inventories down. This paper addresses a single-machine scheduling problem with the objective of minimizing total inventory cost, comprising cost associated with work-in-process inventories and storage cost as a result of early completion. The cost components are measured by the sum of the job completion times and the sum of the job earlinesses. This problem differs from more traditional scheduling problems, since the insertion of machine idle time may reduce total cost. The search for an optimal schedule, however, can be limited to the set of job sequences, since for any sequence there is a clear-cut way to insert machine idle time in order to minimize total inventory cost. We apply branch-and-bound to identify an optimal schedule. We present five approaches for lower bound calculation, based upon relaxation of the objective function, of the state space, and upon Lagrangian relaxation. Key Words and Phrases: just-in-time manufacturing, inventory cost, work-in-process inventory, earliness, tardiness, machine idle time, branch-and-bound algorithm, Lagrangian relaxation