A conceptual framework of the applicability of production scheduling from a contingency theory approach: addressing the theory-practice gap

Figures & data

Table 1. Summary of causes (issues) for the theory-practice gap in production scheduling academic literature.

Cause of theory-practice gap	Consequence for the practice of scheduling	Efforts needed to close the gap and overcome the issue	Current state of the gap
(a) Simplification of scheduling problems	Lack of suitable scheduling approaches	Accurate characterization of real-world problems: jobs, resources, constraints, objectives, stability, dynamism, stochasticity	Smaller since 2000 (realistic scenarios have been addressed and more approaches have been proposed) but still exists because more complex realities have appeared and most of scheduling research continues to be focused on the static-deterministic approach (Abedinnia et al. 2017)
(b) Simplification of the practical scheduling task	Inadequate decision support	Acknowledge the iterative and continuous nature of the task: schedule generation, monitoring, control, repeat. Design decision support tools acknowledging this reality	Smaller since 2000 (several authors have addressed this issue) but continues to exist because not enough effort has been put into studying scheduling as a control activity, as most studies still regard scheduling only as a combinatorial problem
(c) Lack of relevance of the traditional scheduling approach to all manufacturing environments	Under-performing scheduling efforts by using unsuitable scheduling approaches	Identify the fits between scheduling approaches and manufacturing environments, based on information from (a) and (b)	Some studies have tackled this issue tangentially, e.g. capacity planning (Tenhiälä 2011), but none has explicitly aligned scheduling approaches with manufacturing environments

Figure 1. Outline of the research steps taken in this study to formulate the conceptual framework.

Table 2. Manufacturing environment types and characteristics.

Type	SCpx	Dyn	Representative characteristics
Social	Low	Low	High volume, low product variety (very low demand uncertainty); flow-shop with no specific constraints
Smooth	High	Low	Medium to high volume, low product variety; low demand uncertainty; (flexible) flow-shop with few additional constraints, e.g. permutation
Socio-technical	Low	High	Low volume, high product variety (customized products); make-to-order or engineer-to-order with loose due dates, highly dynamic job arrivals; job-shop or flexible job-shop with some additional constraints, e.g. in-tree precedence, sub-resources (additional tools or specialized workers), setup-times; variable processing times and possibility of machine breakdowns; somewhat unpredictable job routings
Stress	High	High	Medium volume, low to medium product variety; make-to-stock and make-to-order with tight due dates; somewhat dynamic job arrivals; flow-shop or flexible flow-shop with some constraints, such as lot-sizing and sequence-dependent setup times; variable processing and setup times

Figure 2. Fit between manufacturing environment types and scheduling approaches.

Figure 3. Monitoring needs for different manufacturing environment types.

Figure A1. Classification and PPSTCpx values of 50 manufacturing firms according to their Dyn and SCpx values.

Abedinnia, Hamid, Christoph H. Glock, Eric H. Grosse, and Michael. Schneider. 2017. “Machine Scheduling Problems in Production: A Tertiary Study.” Computers & Industrial Engineering 111 (Supplement C): 403–416. doi:10.1016/j.cie.2017.06.026.

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Tenhiälä, Antti. 2011. “Contingency Theory of Capacity Planning: The Link between Process Types and Planning Methods.” Journal of Operations Management 29 (1–2): 65–77. doi:10.1016/j.jom.2010.05.003.

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