Keywords

1 Introduction

Manufacturing organisations are tasked with the responsibility of developing suitable production strategies capable of improving their productivity in order to increase their competitiveness. In order to achieve this, innovative manufacturing paradigms such as lean manufacturing (Gupta and Jain 2013; Bhamu and Sangwan 2014), agile manufacturing (Kumar et al. 2019), reconfigurable manufacturing (Goyal et al. 2013; Koren et al. 2017), and wisdom manufacturing (Barman 2022) have evolved over the years. Hence, on the one hand, manufacturing organisations have tailored their efforts towards producing high quality products that are required by the customers on or before the due date as well as putting at the centre “the voice of the customers” as a major driver towards developing sustainable products required by the customers (Yang et al. 2019). On the other hand, manufacturing organisations have also identified the need to continuously improve the production methods deployed in their organisations, with strategic objectives of exponentially improving value-added activities carried out within their production processes as well as eliminating or reducing to the bare minimum the non-value added activities (i.e. process wastes) present within their manufacturing systems.

The study of Hill (2018) viewed process wastes as activities that add more time and cost to the production activities carried out in an organisation without concrete value generated from these activities towards meeting customers’ products demand. Therefore, systematic elimination/minimisation of process wastes by means of innovative lean and JIT production strategies are critical towards achieving high productivity gains required to foster sustainable manufacturing in various organisations. According to Wahab et al. (2013), the seven categories of process wastes that needs to be eliminated in a manufacturing organisation are over-processing, overproduction, motion, waiting, defect and transportation. Identification of the sources of the process wastes generated in an organisation is critical towards eliminating them. In light of this, the study of Mostafa et al. (2013) identified the use of traditional value stream mapping and dynamic value stream mapping as the techniques that could be used to pinpoint various process wastes present in a manufacturing organisation. In their study, they also revealed that exercise such as establishment of the root causes of process wastes, prioritisation of the various categories of process wastes and selection of appropriate process waste elimination tools, need to be executed after the process waste identification activity, with a view to systematically eradicate the process wastes present in a manufacturing organisation. A lot of literature in the process of prioritising process waste have focused on carrying out this exercise based on frequency of process wastes generated, cost associated with each process waste, ease of detection of each process waste, and ease of removal of the process wastes, and impact of removal of process wastes (Sutrisno et al. 2018, Manninen et al. 2014). To the best of the authors’ knowledge, no literature has focused on prioritising process wastes based on the influence of each process waste on one another in a manufacturing organisation, tailored towards ascertaining the intensity rating of each process waste from a systems thinking perspective.

The study of Ayomoh and Oke (2006) proposed a HSIM methodology that could be used to prioritise factors by means of carrying out factors subordination pairwise analysis, which could thus unveil the hierarchy level of each factor, thereby unveiling the intensity rating of each factor. In light of the lesson learnt from this study, this study intend to deploy the HSIM prioritisation methodology, with a view to ascertain the vital few process wastes experienced in an electronic product manufacturing organisation. The structure of this paper is highlighted as follows. Section 2 present the HSIM methodology deployed in this study for process waste prioritisation. Section 3 present and discuss the process wastes prioritisation results for an electronic product manufacturing organisation. The last section concludes based on the result obtained in this study and also highlight the future research work.

2 Methodology

The process waste prioritisation solution developed in this study commenced by indicating all the process wastes that could be generated in a manufacturing organisation. Thereafter, the HSIM methodology premised on the theory of subordination was deployed in order to interrogate the interactions and dependencies that exist amongst the various process wastes that affect the productivity of an organisation. The HSIM methodology deployed a contextual question that was used to conduct a pairwise assessment of two consecutive process wastes, with a view to develop a Binary Interaction Matrix (BIM) via subordination of a process waste relative to another, during the course of daily manufacturing operation. The contextual question utilised in this research, denoted as \({Q}_{ij}\) is presented as follows:

$${Q}_{ij}=\left\{\begin{array}{c}1, if\, the\, influence\, of\, process\, waste\, i\, on\, an\, organisation\, productivity\, can\, be\, directly\, influenced\, by\, process\, waste\, j \\ 0, if\, the\, influence\, of\, process\, waste\, i\, on\, an\, organisation\, productivity\, cannot\, be\, directly\, influenced\, by process\, waste\, j\end{array}\right.$$

Mathematically, the generalised form of the contextual relationship can be expressed as:

If \(i\to j=1\) then \(j\to i=0\) however,

If \(i\to j=0\) then \(j\to i=0\) or \(j\to i=1\).

On the other hand, the next step of the HSIM methodology is to develop a Hierarchical Tree Structure Diagram (HTSD) for process wastes ranking using the steps highlighted as follows:

  1. a)

    Locate the elemental spaces containing “1” in each row of the BIM matrix constructed.

  2. b)

    Form subordination by means of arrows to link row elements to their corresponding column elements where \({Q}_{ij}\) = 1.

  3. c)

    Repeat steps (a) and (b) for the entire BIM matrix.

  4. d)

    If an element e is subordinate to more than two elements, say; f, g, h e.t.c., and element f is subordinate to elements g and h, reduce the number of arrows by drawing only one arrow from e to f, f to g etc.

  5. e)

    Repeat step (d) for all identified subordinates, until all subordinates are connected by one arrow line, thus forming a hierarchy.

Furthermore, Eqs. (1) to (3) was used to compute the intensity rating for each process waste.

$${IRPW}_{i}=\frac{{N}_{spw(i)}}{{T}_{pw}} \,\times\, {M}_{sr}+ \frac{a}{{T}_{pw}}({M}_{sr}-\mu )$$
(1)
$$ a = N_{spw} + 1, $$
(2)
$$ \mu = \frac{{M_{spw} }}{{T_{pw} }}\,\times\, M_{sr} ; $$
(3)

\({N}_{spw}\) is the number of subordinate process wastes possessed by a given process waste \(i\), \({T}_{pw}\) is the total number of process wastes considered in the study, \({M}_{sr}\) is maximum scale rating, \({M}_{spw}\) is the maximum subordinating process wastes and IRPW is the Intensity Rating score for each Process Waste. The intensity rating solution is premised on the: (i) the results of BIM exercise, which establish the number of subordinate process wastes (\({N}_{spw})\) that a particular process waste \(i\) possess and (ii) the rating of each process waste on a scale which ranges between a value of 0 and 9 as indicated in the study of Ayomoh and Oke (2006). Therefore, the value of the maximum scale rating (i.e. \({M}_{sr}\)) is 9.

Lastly, Pareto Analysis was conducted based on the intensity rating of each process waste and the frequency of occurrence of each process waste generated in an organisation, with a view to ascertain the vital few process wastes limiting the productivity of an organisation.

The steps deployed to conduct the Pareto exercise are as follows:

  1. a)

    Generate a table that would be used to capture the numeric result before proceeding to build the Pareto Chart.

  2. b)

    Populate the process wastes and the intensity rating score for each process waste (PWR) in the Table developed in step (a).

  3. c)

    Populate the frequency of the occurrence of each process waste (F), obtained at an Electronic Product Manufacturing Organisation considered in this study, in the Table developed in step (a).

  4. d)

    Compute PWR.F for each process waste.

  5. e)

    Rearrange the process waste name in the descending order of PWR.F.

  6. f)

    Rearrange the process waste value in the descending order of PWR.F.

  7. g)

    Determine the cumulative PWR.F based on the result of step (f).

  8. h)

    Determine the percentage cumulative PWR.F based on the result of step (g).

  9. i)

    Develop the Pareto Chart using the results obtained in steps (g) and (h).

3 Results and Discussion

3.1 Identified Process Wastes

The process wastes presented in this study, obtained from the literature include: Overproduction (O-Po), Excess Inventory (EI), Defect (D), Motion (M), Transportation (T), Waiting Time (WT) and Over-processing (O-Pc).

3.2 BIM Results and Discussion

The result of the BIM matrix is depicted in Table 1.

From Table 1, it could be seen that Overproduction (i.e. O-Po) has its effect on productivity directly influenced by process wastes {EI, D, M, T, WT}. In the same manner, excess inventory and defect (i.e. EI and D) are influenced by process wastes {M, T, WT}. Motion (i.e. M) is directly influenced by process waste {WT}. Other process wastes include process Transportation (i.e. T) influenced by process wastes {M, WT}, Waiting Time (i.e. WT), which is not directly influenced by any process waste, and Over-processing (i.e. O-Pc) influenced by process wastes {O-Po, EI, D, M, T, WT}. In light of this, it could be deduced that process wastes O-Po, EI and D respectively have 5, 3 and 4 subordinate process wastes, while process wastes M, T, WT and O-Pc have 1, 2, 0 and 6 subordinate process wastes.

Table 1. Pair-wise comparison mapping (i.e. BIM) result for the identified process wastes
Full size table

3.3 HSTD Result for Process Wastes

The HSTD result presented in Fig. 1 has seven levels.

Fig. 1.
figure 1

HTSD for the prioritised process wastes

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As depicted in Fig. 1, Over-processing (i.e. O-Pc) is the most prioritised process waste while waiting time (i.e. WT) is the least prioritised process waste. Overproduction (i.e. O-Po) is a direct subordinate of O-Pc. Machines used in manufacturing organisations in some instances are pushed to operate (i.e. continue processing product) on a full-time shift, with a view to reduce the operations cost incurred by an organisation, thereby resulting into overproduction. As a result of exceeding customers’ expectations, excess inventories and rejects (to be classified as defect items) could be experienced in an organisation. In addition to this, over-processing of products produced create unnecessary movement of workers to source and transport more unrequired raw materials in producing a product required by the customer. Closely followed, is O-Po. This process waste is on priority level 2. Production of more inventory items than what is needed by the immediate processing workstation could create the holding of excess inventories within these workstations. Therefore, overproduction could promote excessive lead-time, waiting time and storage in an organisation. With overproduction, which promote push system, there is a high chance that defects would not be detected early, hence, promoting high production of defects. In addition, as a result of overproduction, excessive motion and transportation is expected to be experienced in manufacturing organisations.

Next to this process waste is the defect (i.e. D). Defect result into unplanned rework of products, thereby creating an increase in WIP inventories that bring about unnecessary movement of workers during the course of moving defective parts to the rework station. The time spent to rework defective products tends to increase the customer lead time and waiting time as well as increase the waiting time of the WIP items that needs to be processed at the rework station, if the rework station is a multipurpose workstation. Next on the process waste prioritised hierarchy is the excess inventory (i.e. EI). Availability of high amount of WIP inventories in a manufacturing organisation would promote excessive motion and transportation of WIP inventories amidst various workstations, thereby increasing the customer lead time and waiting time. The process waste on hierarchy 5 is transportation (i.e. T). Transportation of WIP items and raw materials amidst various workstations in a manufacturing organisation stimulate a high number of motions exhibited by workers during manufacturing operations. This act result in high waiting time experienced by the WIP inventories, if insufficient material handling system is deployed in a manufacturing organisation.

The process waste on the hierarchy level 6 is motion (i.e. M). Excessive movement of workers amidst various workstations during the production process will increase customer lead time and waiting time. Lastly, waiting time (i.e. WT) is on priority level 7. The influence of all other process wastes high up the hierarchy could influence the customer lead time and waiting time, which could therefore result into low productivity and customer dissatisfaction. In a nutshell, the production of any of these process wastes could on the one hand, result into lower productivity in the case of excess motion, transportation, waiting time, and defects. On the other hand, it could result into production of numerous unwanted inventories that emanates from over-processing (in terms of over-operation) and overproduction.

3.4 Intensity Rating Results for Each of the Process Waste

The results of the intensity rating of each process waste, which indicates the degree of contribution of these process wastes towards productivity loss experienced in a manufacturing organisation, calculated using the results obtained in Table 2 and using Eq. (1) is depicted in Table 2.

Table 2. Intensity Rating for each Process Waste
Full size table

Based on Table 2, it could be inferred that the intensity rating of the process wastes; O-Po, EI, D, M, T, W and O-Pc are 7.53, 4.59, 6.06, 1.65, 3.12, 0.18, and 9 respectively.

3.5 Pareto Analysis of the Process Wastes Generated at an Electronic Product Manufacturing Organisation

The Pareto Chart result, obtained using: (i) the Pareto computation procedure highlighted in the methodology section, (ii) the results presented in Table 2 and (iii) the frequency of process wastes generated at an electronic product manufacturing organisation for a period of one (1) month, which are 3 for D, 4 for EI, 2 for M, 10 for T, 0 for O-Pc, 1 for O-Po and 10 for O-Pc, obtained from the study of Makinde et al. (2022), is graphically illustrated in Fig. 2.

Fig. 2.
figure 2

Pareto Chart of the Process Wastes generated at an Electronic Product Manufacturing Organisation

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Based on Fig. 2, it could be inferred that 84.29% of the process wastes (i.e. the vital few process wastes) generated in an Electronic Product Manufacturing Organisation considered in this study, are transportation, excess inventory and defects while the remaining 15.71% of the process wastes (i.e. the non-vital process wastes) generated in this organisation are overproduction, motion, waiting time and over-processing.

From this Pareto result, it could be inferred that production managers and decision makers at this Electronic Product Manufacturing Organisation need to focus and concentrate their management efforts more on establishing suitable strategies tailored towards eliminating the aforementioned three (3) critical process wastes, with a view to exponentially improve the productivity of this organisation.

4 Conclusion

In view of the need to identify and eliminate process wastes present within a manufacturing organisation, many production managers are forced to explore novel solutions tailored towards improving the leanness of their production processes. In light of this, this study proposed a Pareto-enhanced HSIM technique, which on the one hand, prioritises myriads of process wastes that result into production loss using the principle of subordination, ingrained with Hierarchical Process Waste Tree Structure diagram. On the other hand, it unveiled the vital few process wastes based on the HSIM prioritisation results. The Pareto-enhanced HSIM result deduced that transportation, inventory and defects are the vital few process wastes contributing to the productivity loss experienced in an electronic product manufacturing organisation, used as a case study for this research work. The solution obtained from this study should attract lean manufacturing community since it open up a relatively new area of process waste prioritisation investigated towards promoting the leanness of processes used in manufacturing organisations. Development of a process waste elimination funding model tailored towards improving the productivity of a manufacturing organisation based on the Pareto-enhanced HSIM results should be explored in future studies. Further to this, comparative analysis of the process wastes prioritisation solution of this approach with the process wastes prioritisation solution of the approaches available in the literature need to be explored.