Use of DES to develop a decision support system for lot size decision-making in manufacturing companies

Figures & data

Table 1. Review of lot sizing approaches.

	Fixed order quantity (FOQ)	Lot for lot (discrete order quantity) (LTL)	Economic order quantity (EOQ)	Fixed order period (FOP)	Wagner–Within algorithm	Silver–Meal heuristics (SMH)	Part period balancing (PPB)	Least unit cost (LUC)	Groff (GMR)	Capacitated lot sizing problem (CLSP)	Multi-level capacitated lot sizing problem (MLCLSP)
	Jacobs and Chase (2014)	Jacobs and Chase (2014)	Harris (1990); Andriolo et al. (2014), Glock et al. (2014)	Jacobs and Chase (2014)	Wagner and Whitin (2004); Jans and Degraeve (2007)	Silver and Meal (1973); Schulz (2011)	Baciarello et al. (2013); Van den Heuvel and Wagelmans (2010)	Baciarello et al. (2013); Brahimi et al. (2017)	Groff (1979); Beck et al. (2015)	Dixon & Silver (1981); Bruno et al. (2014); Wu et al. (2021)	Billington (1983); Helber and Sahling (2010); Mickein et al., 2022; Zhao & Zhang, 2020	This paper
Problem type	SLULSP	SLULSP	SLULSP	SLULSP	SLULSP	SLULSP	SLULSP	SLULSP	SLULSP	CLSP	MLCLSP	MLCLSP
Considers inventory costs			x	x	x	x	x	x	x	x	x	x
Considers order or set-up costs			x	x	x	x	x	x	x	x	x	x
Considers service level												x
Considers capacity constraints										x	x	x
Considers multiple products										x	x	x
Considers multi-level production											x	x
Integrates different objectives into a single decision										x	x	x
Visualizes trade-offs and effects of decisions and between objectives												x
Analytical/statistical approaches			x	x	x	x	x	x	x	x	x	x
Dynamic simulations												x

SL, single-Level; ML, multi-level; U, uncapacitated; C, capacited; LSP, lot sizing problem.

Table 2. Overview of data case company.

Step	Purpose	Data item	Source
Understanding of problem and scope of researcher involvement	Overview of the current situation, identify the problem, define the objective (KPIs) and decisions to make	Three interviews with problem owner, discussion of current problems such as delay in delivery, low throughput rate	Supply Chain Manager, Head of Operations, CEO, COO
Design of the model	Data collection to understand the decision-making process, production processes and sequences for the different products	Receiving data and explanations from problem owner, continuous communication about model design	ERP system, Supply Chain Manager and Head of Operations
	To model the customer demand pattern	Sales data: during the last 3 years	ERP system
	To calculate on-time-delivery (OTD)	Targeted lead time	ERP system
	To model the material flow	Production process definition: operations sequence and the specific processing times, set-up times	ERP system and Head of Operations
	To calculate costs	Costs: resource hourly rate, product yearly inventory cost	ERP system
	To model system capacity	Resource capacity: machine amount, working schedule	ERP system
	To calculate the performance of current lot size strategy	Current production planning method and current lot-sizes	ERP system and Head of Operations
Application of the model	Comparison of previous state and improved state, discussion about solution and the performance (KPIs) of solution	Interview about solution feasibility and implementation	Supply Chain and Operations Manager
Evaluation and refinement	Receive feedback from problem owner about our solution approach. Evaluate the solution regarding decision-support and outcome.	Final interview with problem owner	Supply Chain and Operations Manager

Table 3. Overview of interviews outside the case company.

Company	Position	Industry	Size (employees)	Duration (min)
Company A	Head of Operational Excellence & Projects	Glass production	ca. 16,000	90
Company B	Head of Production	Spring production	ca. 300	45
Company B	Head of Operational Excellence & Projects	Spring production	ca. 300	60
Company C	Lean Manager	Measuring Equipment	ca. 500	120

Figure 1. Mapping of design requirements (DRs) and design principles (DPs).

Eight design requirements (DRs) have been mapped to five design principles (DPs) to show the interdependencies.

Table 4. Design requirements from OM and DSS.

Domain	Derived design requirements (DR)	Supporting literature	Empirical validation
From Operations Management (OM) literature	DR1: Model the problem as real as possible	Brahimi et al.(2017), (Drexl & Kimms, 1997)	‘Information about demand patterns, trends, risks, capacities, products and process flows as well as order status need to be considered’ – Head of Production, Springs
	DR2: Consider multiple objectives and information sources	Florian et al. (1980), Aggarwal and Park (1993)	‘We need to see the effects on different KPIs to decide on the optimal lot size, not just the costs perspective’ – Head of Production, Springs
	DR3: Identify feasible and optimal solutions compute trade-offs between conflicting goals	Glock et al. (2014)	‘We need to consider real-time data for capacities, order status, processing times to derive optimal solutions’ – Head of Operational Excellence, Glass
From Decision Support Systems (DSS) literature	DR4: Consider a broader system perspective and show the value of respective lot sizes to make informed lot size decisions	Form of guidance (Morana et al., 2017)	‘We have to get away from lot size discussions only focused on inventory and set-up costs, we have to establish a broader, more holistic decision perspective’, ‘MRP systems are limited to single-products and single-stage considerations.’ – Head of Production, Springs
	DR5: Decrease the complexity of lot size determination for managers	Intention of guidance (Arnold et al., 2004)	‘We have to integrate more information, to facilitate better decision-making and have to make it understandable at the same time’ – Lean Manager, Measuring equipment
	DR6: Provide an understandable visualization of effects and interdependencies to all stakeholders	Format of guidance (Gregor & Benbasat, 1999)	‘The challenge is to show a complex problem in simple way, so that stakeholders can make use of it’ – Head of Production, Springs
	DR7: Foster a cross-functional decision-making discussion to decide on the final option	Participative guidance (Morana et al., 2017)	‘If we are able to visualize a broader system perspective, this will allow us to integrate other operational decisions such as make or buy, product allocations, etc.’ – Head of Production, Springs
	DR8: Enable a learning form decision	Dynamic guidance (Morana et al., 2017)	‘Transparency about effects of decisions, will bring learning and cross-functional collaboration on another level’ – Head of Operational Excellence & Projects, Glass

Figure 2. Solution approach.

The solution approach consists of four phases: building model, execute lot sizing techniques, visualize solutions and the selection of preferred solution.

Figure 3. Decision support system with discrete event simulation model of production process.

Overview of simulation model: inputs, processes map and outputs.

Table 5. Lot sizing strategies performance in the case company.

Sc.	Strategy-lot size	Hourly cost (CHF)		Utilization rate		Hourly throughput (product units)		Customer satisfaction
		Total	Inventory	Average	Bottleneck	Produced	Delivered	Fill rate	OTD rate
1.1	FOQ: Q	241.0	0.5	14%	72%	2,523.5	2,526.0	100%	80%
1.2	FOQ: Monthly average	241.7	0.7	15%	94%	2,499.9	2,496.9	99%	57%
1.3	FOQ: 0.5* Q	241.1	0.4	13%	65%	2,511.2	2,526.6	100%	97%
1.4	FOQ: 2* Q	243.2	0.7	15%	84%	2,521.1	2,487.4	98%	43%
1.5	FOQ: 3* Q	244.9	0.8	15%	90%	2,519.6	2,480.0	98%	35%
1.6	FOQ: 4* Q	245.4	1.0	15%	96%	2,573.4	2,501.2	99%	42%
2.1	LTL 0	242.6	0.4	15%	92%	2,496.9	2,496.9	99%	45%
2.2	LTL 1	238.9	0.9	15%	91%	2,471.7	2,442.8	97%	22%
2.3	LTL 2	238.8	1.0	15%	91%	2,424.9	2,368.9	94%	21%
3	EOQ	479.4	11.4	28%	100%	5,291.6	2,526.6	100%	81%
4	FOP	625.5	8.3	37%	100%	3,780.4	2,327.2	92%	60%
5	SMH	632.9	9.5	37%	100%	4,087.9	2,448.4	97%	59%
6	PPB	629.6	9.3	37%	100%	4,073.5	2,448.4	97%	62%
7	LUC	639.9	9.7	37%	100%	4,359.4	2,467.1	98%	66%
8	GMR	631.0	9.4	37%	100%	4,105.2	2,448.4	97%	60%

Table 6. Product basic data and lot sizes for strategies 1.3 and EOQ.

Product number	Basic data				Lot sizes
	Monthly demand mean	Monthly demand STD	Yearly inventory costs (CHF/Pcs)	Set-up costs (CHF)	FOQ: Q	FOQ: Q/2	EOQ
1	334,124	163,156	0.007219	363.0	120,000	60,000	635,012
2	328,426	124,883	0.007219	341.0	120,000	60,000	610,199
3	133,276	124,551	0.004125	605.0	180,000	90,000	684,932
4	206,572	111,435	0.007219	363.0	120,000	60,000	499,303
5	205,092	90,024	0.007219	341.0	120,000	60,000	482,200
6	201,789	71,641	0.014438	341.0	100,000	50,000	338,210
7	34,960	41,460	0.009625	284.9	72,000	36,000	157,593
8	108,001	75,968	0.002406	581.9	200,000	100,000	791,724
9	100,968	58,652	0.001444	581.9	200,000	100,000	1,397,117
10	70,571	43,162	0.007219	363.0	60,000	30,000	291,838
11	71,089	40,875	0.007219	363.0	60,000	30,000	292,907
12	51,737	36,456	0.009625	308.0	36,000	18,000	199,334

Figure 4. Example of KPIs displayed in the simulation model.

KPIs such as OTD, costs and utilization are exemplary visualized for the 12 different products.

Figure 5. Theoretical implications.

Six implications in OM driven by digital technologies.

Jacobs, F. R., & Chase, R. B. (2014). Operations and supply chain management (Fourteenth ed.). McGraw-Hill/Irwin.

 Google Scholar

Harris, F. W. (1990). How many parts to make at once. Operations Research, 38(6), 947–950. https://doi.org/10.1287/opre.38.6.947

 Google Scholar

Andriolo, A., Battini, D., Grubbström, R. W., Persona, A., & Sgarbossa, F. (2014). A century of evolution from Harris׳s basic lot size model: Survey and research agenda. International Journal of Production Economics, 155(C), 16–38. https://doi.org/10.1016/j.ijpe.2014.01.013

 Google Scholar

Glock, C. H., Grosse, E. H., & Ries, J. M. (2014). The lot sizing problem: A tertiary study. International Journal of Production Economics, 155(September 2014), 39–51. https://doi.org/10.1016/j.ijpe.2013.12.009

 Google Scholar

Wagner, H. M., & Whitin, T. M. (2004). Dynamic version of the economic lot size model. Management Science, 50(12_Suppl.), 1770–1774. https://doi.org/10.1287/mnsc.1040.0262

 Google Scholar

Jans, R., & Degraeve, Z. (2007). Meta-heuristics for dynamic lot sizing: A review and comparison of solution approaches. European Journal of Operational Research, 177(3), 1855–1875. https://doi.org/10.1016/j.ejor.2005.12.008

 Web of Science ®Google Scholar

Silver, E. A., & Meal, H. C. (1973). A heuristic for selecting lot size quantities for the case of a deterministic time-varying demand rate and discrete opportunities for replenishment. Product Management Investopedia, 14 (2), 64–74. Scopus

 Google Scholar

Schulz, T. (2011). A new Silver–Meal based heuristic for the single-item dynamic lot sizing problem with returns and remanufacturing. International Journal of Production Research, 49(9), 2519–2533. https://doi.org/10.1080/00207543.2010.532916

 Web of Science ®Google Scholar

Baciarello, L., D’Avino, M., Onori, R., & Schiraldi, M. M. (2013). Lot sizing heuristics performance. International Journal of Engineering Business Management, 5(January), 5. https://doi.org/10.5772/56004

 Google Scholar

Van den Heuvel, W., & Wagelmans, A. P. M. (2010). Worst-case analysis for a general class of online lot-sizing heuristics. Operations Research, 58(1), 59–67. https://doi.org/10.1287/opre.1080.0662

 Web of Science ®Google Scholar

Brahimi, N., Absi, N., Dauzère-Pérès, S., & Nordli, A. (2017). Single-item dynamic lot-sizing problems: An updated survey. European Journal of Operational Research, 263(3), 838–863. https://doi.org/10.1016/j.ejor.2017.05.008

 Web of Science ®Google Scholar

Groff, G. K. (1979). LOT SIZING RULE FOR TIME-PHASED COMPONENT DEMAND. Production and Inventory Management Journal, 20 (1), 47–53. Scopus

 Google Scholar

Beck, F. G., Grosse, E. H., & Teßmann, R. (2015). An extension for dynamic lot-sizing heuristics. Production & Manufacturing Research, 3(1), 20–35. https://doi.org/10.1080/21693277.2014.985390

 Web of Science ®Google Scholar

Dixon P S and Silver E A. (1981). A heuristic solution procedure for the multi-item, single-level, limited capacity, lot-sizing problem. Journal of Operations Management, 2(1), 23–39. 10.1016/0272-6963(81)90033-4

 Google Scholar

Bruno, G., Genovese, A., & Piccolo, C. (2014). The capacitated lot sizing model: A powerful tool for logistics decision making. International Journal of Production Economics, 155(September 2014), 380–390. https://doi.org/10.1016/j.ijpe.2014.03.008

 Google Scholar

Wu, T., Wang, X., & Xia, Y. (2021). Predictive search for capacitated multi-item lot sizing problems. INFORMS Journal on Computing, 33(2), 419–835. https://doi.org/10.1287/ijoc.2021.1073

 PubMedGoogle Scholar

Helber, S., & Sahling, F. (2010). A fix-and-optimize approach for the multi-level capacitated lot sizing problem. International Journal of Production Economics, 123(2), 247–256. https://doi.org/10.1016/j.ijpe.2009.08.022

 Web of Science ®Google Scholar

Mickein, M., Koch, M., & Haase, K. (2022). A decision support system for brewery production planning at feldschlösschen. INFORMS Journal on Applied Analytics, 52(2), 158–172. https://doi.org/10.1287/inte.2021.1101

 Google Scholar

Zhao, M., & Zhang, M. (2020). Multiechelon lot sizing: new complexities and inequalities. Operations Research, 68(2), 534–551. https://doi.org/10.1287/opre.2019.1867

 Web of Science ®Google Scholar

Drexl, A., & Kimms, A. (1997). Lot sizing and scheduling—Survey and extensions. European Journal of Operational Research, 99(2), 221–235. http://hdl.handle.net/10419/175395

 Web of Science ®Google Scholar

Florian, M., Lenstra, J. K., & Kan, A. H. G. R. (1980). Deterministic production planning: algorithms and complexity. Management Science, 26(7), 669–679. https://doi.org/10.1287/mnsc.26.7.669

 Web of Science ®Google Scholar

Aggarwal, A., & Park, J. K. (1993). Improved algorithms for economic lot size problems. Operations Research, 41(3), 549–571. https://doi.org/10.1287/opre.41.3.549

 Web of Science ®Google Scholar

Morana, S., Schacht, S., Scherp, A., & Maedche, A. (2017). A review of the nature and effects of guidance design features. Decision Support Systems, 97(May 2017), 31–42. https://doi.org/10.1016/j.dss.2017.03.003

 Google Scholar

Arnold, V., Collier, P. A., Leech, S. A., & Sutton, S. G. (2004). Impact of intelligent decision aids on expert and novice decision-makers’ judgments. Accounting and Finance, 44(1), 1–26. https://doi.org/10.1111/j.1467-629x.2004.00099.x.

 Google Scholar

Gregor, S., & Benbasat, I. (1999). Explanations from intelligent systems: Theoretical foundations and implications for practice. MIS Quarterly, 23(4), 497–530. https://doi.org/10.2307/249487

 Web of Science ®Google Scholar

Data availability statement

Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data is not available.