Development of an improvement framework for warehouse processes using lean six sigma (DMAIC) approach. A case of third party logistics (3PL) services

Abstract

This study aims at developing an improvement framework of warehouse processes by improving process cycle efficiency using lean six sigma (DMAIC) approach.

A case study method was used to illustrate the evaluation of the existing processes in the warehouse of a third party logistics company with a focus on productivity using warehouse lean tools. Data were collected based on warehouse operational areas of suppliers, customers and internally related with a focus on warehouse core processes. The optimization of the warehouse processes was based on established lean tools.

Based on the warehouse evaluation, high non-value added activities were observed leading to a low process cycle efficiency of 40%. After the implementation of the lean six sigma approach, the process cycle efficiency improved of up to 70%. An improvement framework was also established for productivity across the warehouse processes to minimise waste.

Lean warehousing has been used to illustrate solutions to a real-time problem of productivity which has negatively impacted on management and customer satisfaction.

The study has added to lean warehousing focusing on all the warehouse processes using six sigma DMAIC approach, unlike most literature which concentrates on a specific process. Also develop a framework for the improvement of the processes.

1. Introduction

The existing competitions in manufacturing industries is becoming obvious in the warehousing operation, either as third party logistics or total supply chain [1] whereby lean approach is used to improve productivity [[2], [3], [4], [5]]. Peculiar to the warehouse operations are receiving and shipping operations [[6], [7], [8]]. Most organizations into logistics and warehousing are leveraging on lean to boost their efficiency [[9], [10], [11], [12], [13]]. The accomplishment of lean in a system is revealed in waste reduction whereby non-value activities are eliminated or reduced to barest minimum [9]. Research in supply chain management focusing on the improvement of productivity has been receiving attention in the areas like logistics and supply chain [[14], [15], [16]] compared to warehousing [13,[17], [18], [19], [20]]. Evidences have shown dearth of literature and several plausible gaps in the research [21,22]. According to Ref. [23], lean maturity in production environment varies as found in the warehouse environment. From the literature, contributions to the improvement of warehousing operations are limited to some specific processes like inbound [24], storage strategy [[25], [26], [27]] and technologies advancement [25,28,29]. Few works that were reported on the application of lean six sigma to warehousing problems were the like of [1] which evaluated the warehouse practices and concluded that the productivity of warehouse can be improved with the deployment of the Lean approach. Phogat [26] also studied the applicability of lean to warehousing. The results showed that lean warehousing can improve the visibility, flow of materials, organization of work and standardization of process. Salah et al. [30], linked Lean Six Sigma (LSS) with supply chain management (SCM) in a study. The outcome showed satisfaction in terms of cost, quality and delivery. Warehouse processes are labour intensive, thus have tendency to accumulate waste [[31], [32], [33], [34]]. The only way to eradicate or at least minimise waste is to identify the unnecessary processes via implementation of Lean [18,19,35,36]. Literature is lacking on the evaluation waste across all the warehouse processes. The main reason is inability to apply lean principles directly to warehousing due to the complexity of warehousing processes which is dependent on factors size, layout, etc [36]. This makes it difficult for organizations to adopt lean warehousing due to little or no knowledge of optimization and implementation strategy [[37], [38], [39]]. The study proposes a novel structured improvement framework for warehouse, integrating different lean tools and six sigma DMAIC approach. The case study organization is into a third party logistics warehousing, faced with the challenge poor productivity (low process cycle efficiency) due to accumulation of waste across the warehouse operation. Lean warehousing is proposed with the aim of improving the process cycle efficiency across the warehouse processes by identifying and eliminating waste.

The paper is structured as follows: section 2 presents the literature review on lean warehousing. Section 3 presents the methodology of the work. In section 4, case-study background is presented. Section 5 presents the results of the evaluation of the case-study, implication of the study and establishes the improvement framework. Section 6 presents the conclusion of the study, limitation as well as proposes the future research directions.

2. Literature review

The extent of influence of Lean six sigma (LSS) is expanding across industries and sectors [40]. It customary model focussed on the improvement in manufacturing environment. However, LSS is gaining good success in health care [41], construction [42], education [43] and warehousing [36,44]. With focus on warehousing, most implementation of LSS focuses on the systematic application of DMAIC approach to characterize processes with effective use of some LSS tools such as design of experiment (DOE), supplier input producer output consumer (SIPOC) and 5S [45]. While the implementation of LSS in manufacturing and production environments is vast, its application in maintenance repair overhaul (MRO) function and warehousing operation is less progressing. A strong contribution is available on the application of lean in MRO function [[46], [47], [48]]. In order to improve on the performance in warehouse environment, design and physical operations need to be addressed [16]. There are limited contributions on warehouse design motivated by principle of lean [39]. Wastes such as handling cost and used spaces are solved by establishing a storage area for products via the approach of simulated annealing, developed by Ref. [48]. Shah and Khanzode [49] worked on warehouse layout and material handling flexibility, stating that increase in the number of store keeping units (SKUs) requires appropriate system. Conceptual framework that allows to assess the storage selection and material handling equipment via performance measure was presented by the authors in order to understand the inefficiencies that can be generated thereby refine the choice. Lean tool such as value stream mapping (VSM) was suggested in order to balance system performance and minimise waste. The work by Garcial [50] was among the early contributions which centre on the use of VSM to increase the efficiency of flow in the warehouse. He linked warehouse in VSMs as inventory triangles which are not representing the existing waste. Therefore, it’s more acceptable to analyse a single operation taking at a time such as receiving, palletizing, put away, picking and shipping of orders. Taking to the precedence laid by Ref. [51] focus on the waste as a result of transportation during receiving process. To this end, combination of VSM and Discrete event simulation is used to model the warehouse flows, uncovering inefficiencies and quantifying them. An algorithm to assign trucks to yard was developed in order to optimise the distance moved by goods from the receiving area to put away location. Statistical and mathematical analysis were integrated to VSM to quantitatively address waste due to uncertainty in supply, process parameters and customer’s order was carried out by Ref. [52]. Chen et al. [53], also used VSM to assess detail warehouse operation in a distribution centre. This led to process re-engineering and consequent changes in the warehouse layout were performed via Radio Frequency Identification (RFID) technology. The total operation time was improved from 79 to 87% by adopting only lean principle and integrating RFID technology respectively. Literature were further established on stocks by reducing waste and improving through put via development of a lean storage policy based on dynamic product allocation by Shah and Khanzode [54]. Demeter and Matyusz [55] worked on the improvement of inventory turnover using lean philosophy. To this end, emphasizing on the contribution of [33] is a plus. The authors applied a novel approach of integrating unified modelling language for mapping warehouse process. VSM was used to find value added activities. In the areas of evaluation of degree of lean implementation in warehousing [56], worked on the performance measurement of lean practices using a multi-attribute group decision making framework. This is adopted to evaluate lean philosophy in the distribution centre of a commercial tobacco company in China. Combination of Delphi method and analytical network process was used to develop a decision making model aimed at improving warehouse performance in a manufacturing company was carried out by Ref. [34]. Finally [21], contributed towards the work of [57] on the hierarchy of different metrics in contributing to the assessment of the level of leanness achieved by a warehouse using performance indicator score card. Additional lean waste was added to the previous seven defined by Ref. [57]. There is still dearth in literature despite the fact that lean warehousing is gaining more attention. Combination of lean tools enhances critical analysis of warehouse with multiple views [33]. However, application of the correct lean tools is still limited thus making warehouse performance evaluation not easily achieved [34]. Thus, there is a need for the development of a formalized lean warehousing approach, identification of right lean techniques and to combine them appropriately.

3. Methodology

A case study method is used whereby lean tools are used to evaluate the existing warehouse processes of a third party logistics (3PL) operated company in Nigeria focusing on productivity. The lessons and implications of lean warehousing arrived at in the study were discussed. The reason for using the method is because complexities in organizations can be investigated and resolved both quantitatively and qualitatively [[58], [59], [60], [61], [62], [63]], as well as collection of data in real-time via observation [62,64]. Yin [65] explains that the approach can be used to understand certain organization’s problems so that a suitable framework can be developed to address the identified problem. The approach is used to provide answers to questions, which relates to “why” and “how” [66]. The steps followed in this study is according to John and Kadadevaramath [67] are as follow: the definition of the problem, problem quantification, decision the approach for solving the identified problem, data collection and analysis, the implementation of the proposed solution, reporting of the findings. A real-time problem of poor productivity in warehouse processes was look into based on data gathered on suppliers, customers and warehouse relations using some established lean tools capable of diagnosing and resolving warehousing problems [36,44,64,68]. Six sigma (DMAIC) approach, Qi Macros embedded in Microsoft Excel software and statistical process control tools were used to make improvement in the warehouse.

The problem of accumulation of waste in the course of processing orders was explained. Primary data relating to lead time for processing orders and average daily processed order were collected across the warehouse over a period of six months to evaluate the actual process cycle efficiency and productivity of the warehouse. The improvement of the warehouse processes features the use of some warehouse basic lean tools such as those described by Ref. [44]. The statistical analysis was performed using the Statistical Package for Social Science (SPSS) version 2019 [69].

3.1. Value stream mapping (VSM)

This is a lean improvement tool for classifying activities into value added and non-value added activities [59], as well as resolving any form of organization waste [64,70]. The tool was utilized as it helps in achieving significant reduction of waste across warehouse processes [71]. Value stream mapping can be design using Qi Macros embedded in Excel Software (QiMacros User guide [72]).

3.2. Process cycle efficiency (PCE)

This is defined as the ratio of the value added time to total lead time [73]. The value added time is the useful time put into processing of orders across the warehouse while the total lead time is the combination of both the value added time and non-value added time. The data used for the estimation of PCE was obtained via past records and work study carried out in the warehouse by the help of the warehouse staffs. It was utilized due to its ability to improve processes within the warehouse. PCE is expressed as Equation (1).

4. Case-study background

The study is a case of D&S warehouse providing third party logistics services for manufacturing companies. The warehouse is located in Nigeria with a total of two hundred and fifty (250) members of staff engaging in different activities including administrative. The warehousing operation takes 70% of the entire labour force.

The warehouse engages in supply chain through inbound and outbound of orders on a daily basis. The value chain is presented in Fig. 1, which gives the idea of the process transformation [74,75].

Fig. 1.

Processes of the warehouse.

4.1. Six sigma case

As the warehouse tries to build competitive edge and improve the customer-client relationship, the need to improve on the productivity by minimizing non-value added activities which constitute wastes arises. Lean six sigma practitioners were engaged by the management to review the warehouse processes by going through the process stored data and face to face interaction with some selected staffs, as well as physical observation. The team comprises of a six sigma Black Belt certified practitioner and a quality management researcher in the academia.

5. Results

The lean six sigma approach used is presented in this section.

5.1. Define phase

This phase defines the objective and scope of the project, giving consideration to the process requirements. The scope was limited to the value added and non-value added activities (wastes) of the processes. Waste in the processes was categorised as follows [76–77; 16–17; 14):

• i.Excess stock/Inventory: This is an overproduction from the manufacturers in the supply chain leading to accumulation of stocks deposited to the warehouse. This results in a shortage of storage space and a decline in the productivity of the workers due to excess stock.
• ii.Transportation: This is termed as unnecessary movement of products, workers and materials handling equipment operators. It also occurs when SKUs are not logically stored thereby increasing the searching time of orders. Sometimes, when loading vans are packed far from the loading point.
• iii.Waiting time: This is the idle time of workers as a result of the unavailability of products, machines or system. It leads to the underutilisation of resources capacity. It also occurs as a result of queue up of truck drivers at the same time in the parking lot.
• iv.Motion: This is the unnecessary movement of the body by the operators when products are stored in ergonomically uncomfortable positions.
• v.Overproduction: This is termed as the picking or preparation of unrequested orders.
• vi.Space/Overprocessing: This occurs as a result of accumulation of unnecessary orders around the storage area as well as undue movement of products through the walk ways by more than one forklift.
• vii.Defect/Error: This is termed as selecting the wrong item or quantity leading to excess or shortage of supply of customers' orders. It leads to process error. Damaged products within the warehouse in the course of processing are also known as defects.

The objective is to reduce the lead time in carrying out the processes without compromising quality. The problem was defined thus:

Between January 2019 and June 2019, the current process cycle efficiency (PCE) of the warehouse was estimated at 40%, far below the organizational target of 60%. The low PCE was due to accumulated non-value added times in the processes which impacted negatively on the total lead time of processing orders. The estimation of process cycle efficiency in the process line is presented in Table 1.

Table 1.

Present Value Added and Non-Value-Added Time of the warehouse.

S/N	Processing Stage	Types of Stock Keeping Units	Average Value Added Time (Sec)	Average Non Value Added Time (Sec)
1	Receiving	Mono-Pallet Mixed-Pallet	7200 18,000	10,800 27,000
2	Put-away	Mono-Pallet Mixed-Pallet	1800 7200	2700 10,800
3	Storage	Mono-Pallet Mixed-Pallet	10,800 21,600	17,280 34,560
4	Picking	Mono-Pallet Mixed-Pallet	1200 2400	1920 3840
5	Packing	Mono-Pallet Mixed-Pallet	2700 5400	4320 5640
6	Shipping	Mono-Pallet Mixed-Pallet	3600 10,800	5400 16,200
	Total		92,700 (39.8%)	140,460 (60.24%)

5.1.1. Estimation of the present process cycle efficiency

VAT = 92,700 s

NVAT = 140,460

Present Lead Time = VAT + NVAT (1)

= 92,700 + 140,460 = 233,160 s (2)

$$\mathrm{P}\mathrm{C}\mathrm{E}=\frac{ValueAddedTime}{LeadTime}\times 100=39.8\%$$ (3)

The estimation shows that the process cycle efficiency (PCE) is below the organization target of 60%.

5.2. Measurement phase

Measurement phase is aimed at determine the actual positive response of the system so as to define its actual state [78]. More so, it is necessary to establish a measurement system that permits monitoring of the processes evolution under analysis in line with the established objectives of the define phase [78]. The assumption is that measurable data for the control stage needs to be gathered to point out differences and assess progress.

Considering the study case, it can be seen that the average daily processed order per process per month in the year 2019 from January to June (10,224) is very low with reference to the company’s target as well as to both new stock and replacement of shipped stock. Also, from the process performance capability index (Cpk) of the data, Cpk is less than 1. The obtained data clearly shows the need to improve the processes. Fig. 2, Fig. 3 show the average daily processed orders per process per month for 2019 between January to June and Process Capability chat respectively.

Fig. 2.

Average daily processed orders per process per month for 2019 (Jan–June).

Fig. 3.

Process performance capability index from jan–jun 2019.

Fig. 4 shows the current VSM of the warehouse comprises of the value added time and non-value added time of each process with consideration given to both mixed and mono pallet stocks. Processing of mixed pallet stocks consumes more time than mono pallet stocks, adding to non-value added time. Adequate analysis on the value stream mapping help to identify the wastes in the processes.

Fig. 4.

Current value stream mapping of the warehouse.

Fig. 5 shows the current VSM with areas that need to be improved. The areas of improvement differ by process. For instant, unnecessary waiting was peculiar at the receiving and storage areas, errors or rework was common to packing and shipping process, long reach or transportation is common in the storage and picking areas and sometimes accumulation of inventory was reported at the put away process. All these accumulated to the prolonged lead time.

Fig. 5.

Current Value Stream Mapping with areas of Improvement.

5.3. Analysis phase

The review of the warehouse processes was done by the Lean Six Sigma team and presented by the Ishikawa Diagram in Fig. 6 and waste analysis in Table 2, in order to design improvement measure and subsequent control [[79], [80], [81]]. The root causes were grouped into seven work process elements, namely: management, machine, method, environment, materials, measuring equipment and man. The factors that affected productivity grouped under materials are missing stock, poor or lack of stock label, overloading of stock and sometimes obsolete stock which is overdue in the storage areas. The effects of these factors were not limited to inefficiencies of the workers but also the financial implications to the organization. In terms of method, there were no process instruction manuals for employees which meant that work processes were carried out irrationally. Ergonomic limitation was also seen as one of the factors that hinder productivity. Unnecessary movement of stock or workers was observed from the put-away and storage processes due to SKUs not positioned in the right location or level, and employees needing to reach or bend over in awkward positions to pick the items. Sometimes, unnecessary movement is also referred to when machine operators and pickers used longer searching times to pick SKUs due to the unreasonable storage of goods. Prolonged waiting time was observed from the receiving, picking, packing and shipping processes due to the unavailability of products, right machine or systems when the workers were ready to work but the process did not permit them to work and this resulted in an underutilisation of resources.

Fig. 6.

Root Cause and Effect Diagram of waste.

Table 2.

Waste analysis chart of the warehouse.

Waste-analysis
Date of Evaluation:	15/07/2020		Observer:	Dr. Adefemi Adeodu
No.	Process/Activity/Location		Observation	Overproduction	Excessive stock	Space	Unnecessary movements	Transportation	Waiting times	Errors/Rework
1	(Receiving Process)		It is observed that too much time was used on the verification of SKUs in the inbound area.						Yes
			Lack of access road for delivery trucks close to inbound area					yes
			Too much time on the offloading from the truck to the inbound area	yes		yes		yes	yes
			Delay in the inventory taking of the arrived SKUs by the inventory officer		yes				yes
			Space constraint in the inbound areas			yes
			Delayed in the inventory report writing							yes
2	Put-Away		Accumulation of buffer stock in the storage area		yes	yes
			Excess movement of products, workers and machines to and from receiving area					yes
			unlogical and unsequential stored of SKUs leading to longer searching time						yes	yes
			storing of products at the wrong location level				yes
3	Storage		Movement of goods through in more than one machine	yes
			Unergonomical storage of goods in an uncomfortable height				yes
			Space constraint in the storage areas			yes
			Damage of Goods due to excess storage or congestion in the dispatched area							yes
4	Picking		Customers waiting at shopfloor for order not yet generated on POE. This leads to wasting of Customer's time						yes
			Customer mandate are not presented by the customers when coming for stock pick up, this leads to delay in attending to them and cause dissatisfaction						yes
			Conduction of quality checks severally at different stages leading to unnessary inspectiont of picked order							yes
5	Packing		Conduction of quality checks severally at different stages leading to unnessary inspectiont of packed order							yes
			Packing of wrong orders due to wrong labelling
			Incessant breakdown of material handling equipment for movement of Goods					yes	yes
6	Shipping		No provision for technical evaluation procedure for returned stocks to ascertain the sellability of the stock						Yes	Yes
			It was observed that when despatching goods, items are not arranged in any sequential order or logic when made ready to be picked by transporter. Hence, when Transporter is picking goods, he has to move in a lot of directions in a zigzag way just to identify items on the sheet in order to confirm the item for receipt on the Delivery Note/Waybill				Yes		Yes
			it was observed that after despatch of goods labels have to be printed again to reflect new stock levels. Shopfloor attendant has to go to office to print these labels in the office and bring them to shopfloor for pasting.				Yes			Yes
			Training tools/POS materials are not captured on Enterprise Resources Management							Yes

In the case of management, the system control put in place does not have effect on the work organization. Poor work standardization was also among the factors that caused poor productivity in this group as standard operating procedure (SOP) was lacking. Productivity measurement tools such as 6S, and Plan-Do-Check-Act (PDCA) were poorly implemented. In the case of machine, the poor operational state due to aging of the material handling equipment led to unexpected breakdowns thereby decreased the effectiveness of carrying out the processes. There is bureaucracy on the part of management in terms of maintenance policy which was time-consuming unlike when total preventive maintenance is adopted. However, the cost of replacement is on the high side. Low productivity in the process also resulted from the inexperience poor predisposition and motivation for work by the employee. Specialised training on process implementation, equipment/machine operation and maintenance were not organised by the management except compulsory health and safety training. As there were no incentives for extra work hour, lack of motivation for work was clearly visible. Another cause of poor productivity in the warehouse is linked to environmental factors such as poor ventilation. The storage areas are air tight which causes the temperature to be unnecessarily high and unbearable for operators. Visibility is also a challenge in the storage area. This added to the increase chance for errors in picking, packing and storage. Space constraints due to excess delivered SKUs and work-in-progress was observed from the receiving, put-away and storage processes. Damaged/error products or reworks were noticed from all the processes due to mismanagement of goods. The return orders need to be reprocessed [9,14,16,17,76,77].

Fig. 7a, Fig. 7ba and 7b shows the control charts (X Bar and R Chart) that were constructed with the performance data. From the figures, it is clear that majority of the points in X Bar are outside the control limits, however, the process is in statistical control.

Fig. 7a.

X Bar for the process performance.

Fig. 7b.

R Chart for the process performance.

5.4. Improvement phase

This phase is aimed at proposing solutions to the identified root causes and evaluating the proposed solutions in order to select the optimum solution from the alternatives [82,83]. Once the optimum solution is chosen, re-evaluation of the system should be carried out to observe the efficiency and effectiveness [84]. It should be noted that the proposed solution should not involve large cost implications such that the long-term benefits are put at risk [78].

In the situation of the case study, all the processes of warehousing were benchmarked with the developed lean warehousing tools [36,44] by the six sigma team in order to establish where the warehouse practices are faulty. Improvement actions were implemented in line with the cause and effect analyzed, which are presented in Table 3.

Table 3.

Improvement Actions against categories of the cause of poor productivity.

S/N	Causes of poor productivity by Category	Improvement Actions
1	Materials	i.Stocks are properly stored and sectionalized according to SKUs with standard storage procedures like Last-in-First-Out (LIFO), First-in-First-Out (FIFO) ii.Stocks are properly labeled as well as making sure that the labels are firmly secure on the stock to avoid drop-off iii.Overcrowding of stocks is eliminated
2	Machine	i.Wrong and mismanagement of material handling equipment were stopped ii.Materials handling equipment are service at regular intervals to avoid unexpected or unnecessary down time iii.Provision are made for spare parts in case of unforeseen damage of tools and equipment
3	Method	i.Adequate work inspection and reporting at regular interval are implemented ii.Work ergonomics are put in place with strict actions for compliance iii.Orientations on job description and work breakdown structure are regularly given to staffs iv.Proper implementation of Plan-Do-Check-Act.
4	Man	i.Adequate training and orientation are given to fresh staffs both in admin and main warehouse ii.Standard staff to job ratio is implemented iii.Orientation on job key performance indices (KPI) are given to staffs, as well as staff are remunerated based on performance iv.Introduction of more work incentives are put in place to motivate the staffs v.Proper and adequate work health and safety measures related to warehousing are put in place
5	Environment	i.Warehouse environment are made more ventilated to reduce excessive heat ii.Sound absorbers are put in place within the warehouse iii.Alternatives sources of power are put in place. iv.Standard rules for health safety and environment are set for staffs
6	Measuring Equipment	i.Measuring instrument are properly serviced before usage to prevent misleading data generation ii.Calibrations of measuring equipment are double checked before usage and recording iii.Work Management systems are restructured iv.Capturing of orders are double checked on Enterprise Resource Management before posting to the warehouse for execution
7	Management	i.Organizational policies are restructured ii.Staff KPI are put in place at management level iii.Job class restructuring are executed at management level to curb bureaucracy

Other improvement propositions are as follow; development of a complete and standardized warehouse process set-up checklist that defines all resources and locations must be executed in written form. Problems or unproductive issues should be documented as quickly as possible in the open point list (OPL) and addressed to the right personnel for immediate action. This will impact on the accumulation of non-value added activities. There should be regular documentation of actions or activities in a plan-do-check-act (PDCA) format and a regular review of it. There should be the establishment and usage of mandatory key performance indices (KPIs) within the warehouse. There should be a clear communication line/route with regards to the execution of processes. There should be a classification of activities into internal or external groups. Implementation of warehouse lean tools like 6S, process management, waste analysis and work instruction are carried out at every process stage. Also, routine maintenance of materials handling equipment must be decentralized [36].

The non-value-added time of the processes has decreased to approximately 30% as presented in Table 4. The improved Value Stream Mapping of the warehouse is presented in Fig. 8.

Table 4.

Future value added and non-value-added time of the warehouse.

S/N	Processing Stage	Type of Stock Keeping Unt	Average Value Added Time (Sec)	Average Non Value Added Time (Sec)	Lean Tools Implemented
1	Receiving	Mono-Pallet Mixed-Pallet	7200 18,000	3240 8100	SUP, PM, WA, 6S, PC, SC
2	Put-away	Mono-Pallet Mixed-Pallet	1800 7200	810 3240	WA, PM, WI, 6S, PC, PDCA
3	Storage	Mono-Pallet Mixed-Pallet	10,800 21,600	4212 8424	WA, PM, PS, WI, 6S, PC, PDCA
4	Picking	Mono-Pallet Mixed-Pallet	1200 2400	468 936	WI, PM, WA, 6S, VI, PC, PS, PDCA
5	Packing	Mono-Pallet Mixed-Pallet	2700 5400	1053 2106	PM, WA, WI, 6S, VI, PC, PDCA
6	Shipping	Mono-Pallet Mixed-Pallet	3600 10,800	1620 4860	PM, WA, CUS, 6S, PC, SC
	Total		92,700 (70.4%)	39,069 (29.6%)

Fig. 8.

Improved value stream mapping of the warehouse.

5.4.1. Improved process cycle efficiency (PCE) of the warehouse

The improved process cycle efficiency of the warehouse is estimated to be approximately 70%. This also caused the lead time to reduce to 131,769 s.

VAT = 92,700 s

NVAT = 39,069 s

Improved Lead Time = VAT + NVAT (4)

= 92,700 + 39,069 = 131,769 s (5)

$$\mathrm{P}\mathrm{r}\mathrm{o}\mathrm{c}\mathrm{e}\mathrm{s}\mathrm{s}\mathrm{C}\mathrm{y}\mathrm{c}\mathrm{l}\mathrm{e}\mathrm{E}\mathrm{f}\mathrm{f}\mathrm{i}\mathrm{c}\mathrm{i}\mathrm{e}\mathrm{n}\mathrm{c}\mathrm{y}=\frac{ValueAddedTime}{LeadTime}\times 100\%=70.4\%$$ (6)

Fig. 9, Fig. 10 present the analysis of the process performance graph and process capability index of the warehouse after all improvement measures have been implemented between July–December 2020. From the performance analysis, average daily processed order per process per month increased by 18% compared to 2019 performance analysis. Also, process capability index (CP_k) is now greater 1, showing that the process is now more capable.

Fig. 9.

Average daily processed orders per process per month for 2020 (July–Dec).

Fig. 10.

Process performance capability index from July–Dec 2020.

5.5. Control phase

The control phase is to integrate, standardise, monitor the implemented changes [85], and such that the system continues to run well in the long term [86] as well as ensures that the key variables are maintained within specified limits. Fig. 11a, Fig. 11ba and 11b, show that the warehouse processes are more statistically controlled compared when improvement measures have not been implemented. In the situation of the case study, a warehouse productivity improvement framework was developed and integrated into the entire warehouse processes coupled with the established lean warehousing tools to monitor any deviation. Table 5, summarises the improvement framework.

Fig. 11a.

X chart for the process performance after improvement.

Fig. 11b.

R chart for the process performance after improvement.

Table 5.

Developed improvement framework for warehouse productivity.

S/N	Process	Standard Task	Description
1	Receiving	Pre-Receiving	Ensuring the establishment of receiving document from suppliers. This enables stock to be easily processed. The package requirements such as label position and information, acceptable package size and weight, number of packages per pallet should be managed by the warehouse manager. Detailed information of the arriving stock and notification should be sent ahead. Arriving stock should be palletised.
		Labour and Booking	Proper documentation and account of the incoming stock volume, time, date and type should be made to avoid over or under allocation of human resources which can be done with scheduling software integrated to the warehouse management system (WMS).
		Unloading	Unloading process must include checking the seal, stocks temperature measurement (in case of perishables) and validation of the booking reference. Efficient unloading requires allocation of right labour and equipment.
		Verification	This is the final stage of the receiving process which includes cross checking of quantity of stocks received, confirmation of stocks codes and condition. The use of barcode scanners or RFID, and digital cameras integrated to WMS helps to speed up counting and minimise errors. A parcel or pallet dimensioning system integrated to WMS is also useful during weight and dimensioning.
2.	Put-Away	Product analysis and collection of data	This is the movement of goods from the receiving dock to the optimal position ready for final storage position with the objectives of: fast stored efficiently, minimal travel time, safety of employee and maximise space. Application of put-away mobile device can be used. Slotting software integrated to WMS is applicable to assign optimal position. SKUs size, weight, height, frequency receiving type, and volume are collected and analyzed using pallet dimensioning system.
		Monitor storage capacity and space availability	The use of barcode scanners and location bins integrated to WMS are used to track used or unused spaces across the warehouse RFID are also used to deliver real time tracking of capacity and space.
		Reduction of travel time	ABC analysis is used to know high volume and frequency ordered goods. Defining route for the shortest path to the storage location by considering congestion route, and potential conflict with other processes.
		Direct put-away	This is the movement of goods directly from the receiving area to the final storage area without staging phase. This speeds up the process as well as reduces handling. Adoption of advanced shipment notice (ASN) is applicable.
		Use of fixed and dynamic location	This is associated with some categories of product depending on the frequency of order.
3	Storage	Determination of storage utilisation via focus on key performance indices (KPIs)	To achieve storage utilisation, determine the total storage by calculating the maximum storage capacity based on current set-up and find the potential storage area. The standard is between 22 and 27%. Finally, find the space utilisation by integration with WMS. Most of the warehouse storage KPIs are: - Carrying cost of inventory: cost of storing inventory over a period of time and some other costs. - Storage productivity: this informs the volume of inventory stored per sq. ft. - Space utilisation: this is the percentage of the inventory space per total storage capacity. - Inventory turn-over: this informs the number of times an inventory was sold or replenished within a period of time. - Inventory to scale-ratio: informs carrying inventory per sales.
		Reduction of unnecessary aisle space	Warehouse aisle systems are: -Wide aisle: conventional aisle wider than 10.5 ft suitable for high volume orders and does not require specialised equipment. - Narrow aisle measures between 8.5 and 10.5 ft. It gives room up to 20% more product than normal. - Very narrow aisle measures under 6 ft. It can accommodate 40–50% more product than normal. However, it comes with additional costs.
		Installation of right storage system	Storage system depends on facility size and product mix. Widely used storage methods are: - floor/block stacking for products in unit loads. - pallet flow rack for fast moving product using First-in-First-out (FIFO). - Push back racks are best used for last-in-first-out. - Mezzanine flooring and use of containers.
4	Picking	Conduct ABC analysis or order profiling	Data is to be gathered to gain knowledge of the level of the inventory efficiently by conducting ABC analysis or order profiling. ABC analysis involves organising the inventory in such a way that highest ordered/volume goods (A) are stored in the front while the lowest ordered/volume goods (C) are moved back. This lessen the walking time and requires less man-hours.
		Using correct picking methodology	Depending on the operation, movement between pick locations accounts for about 50% of the picker time. An efficient method of picking is Picker-to-good method unlike pick-to-order method. The following are the common picker-to-good methods used nowadays. - Piece picking: the approach is most efficient for orders with low store keeping units (SKUs). The negative side of the approach is that, its inefficient and inaccurate as the number of orders increases - Cluster picking: this is more efficient when picking multiple SKUs as it can be integrated to WMS which provides the most efficient route for simultaneous picking. The approach can also prioritize picking if need be. This reduces/eliminates order error. - Zone picking: the approach zones SKUs in multiple zones and dedicated pickers are assigned to a particular zone. Orders are moved in trolleys, cages, pallets and conveyors. The specialisation of pickers and SKU-location knowledge, combined with the ability to meet up with multiple orders significantly increases the efficiency of the process. - Wave picking: orders are grouped and released for picking per time across zones depending on duration and alignment with event like shipping, schedules, replenishment cycles, shift change and available work force. It’s more efficient if managed with WMS as it reduces the work load variation of pickers.
		Introducing technology	Introduction of barcode scanners is the simplest and most efficient approach. It identifies products, locations, containers and more. Using both technology and pick list allow faster and free-error collection of data and product validation.
		Wearable computers	This provides a small screen and keyboard that allows pickers to interact with WMS from any location in real-time. In this approach, both hands are free and in turn significantly reduces accident and product damage.
		Voice picking	This adds more high-level efficiencies. Operators are equipped with a head set or microphone connected to the WMS over a local wireless network. It increases accuracy and productivity, reduces data entry errors, and increases picker time and safety.
5	Packing	Introducing technology	This complements the picking process by using technology such as wearable computers integrated to WMS. This ensures damages and errors are minimised.
6	Shipping	Ordering	This is the process of making an online shipment before the physical shipment. It’s also the function of the WMS capability. The approach easily provides loading guides with the items selected. Right and improved picking and storage will also improve shipping.
		Packing and labelling	When it comes to pulling one order at a time, labelling is very important. Fragile products require proper packaging using foam pallets, polystyrene, shredded paper, etc. Quality control personnel can also be available.
		Use of RFID and barcodes	In the case of batch picking and multiple orders, automation and barcodes are most efficient to reduce shipping time and eliminate errors.
		Weighing and dimensioning	Incorporation of inline scale and dimension solution integrated to WMS performs the approach efficiently without having to stop the flow.
		Documentation	Some documentation is necessary for the progress of shipping which can be generated manually or via WMS.
		Loading	Application of loading guide integrated to WMS makes the process more efficient. The loading guide puts into consideration weight, dimension and fragility. Space utilisation by automation also helps shipping. Ready-to-ship loading is also helpful, but puts the goods in a predetermined area.

5.6. Discussion of results

The use of lean has proved its usefulness in warehouse and logistics environments by this research’s contributions to knowledge on productivity improvement across warehouse processes. It lean tools and six sigma approach can easily be applied to any situation. Based on this study, three improvement perspectives can be suggested. Quantitative and qualitative waste impact assessment are recommended at every stage of the process. As a matter of fact, identification and classification of critical tasks should be carried out so as to prioritize the task to come first per process [39]. Secondly, a methodology that permits assigning economic loss to waste should be established [39]. As said, quantitative assessment is useful for understanding the points to focus improvements. More so, if the waste assessment is based on real time economic losses, accuracy is higher [39]. Finally, a more detailed classification of wastes is suggested. The initial classified seven wastes can be further classified [39]. For instant, transport waste can be classified as re-storage, clear path and search location. The detailed waste classification enhances assessment, detection of the root causes and it impact. This article presents lean warehousing with direct implications on customer satisfaction. Lean warehousing focus more on the warehouse processes and customers. Customers and employee centred culture; taking decision at the bottom; low level continuous improvement and lean awareness at all levels of the organization are key to lean warehousing [64,87]. These principles are not put into practice in the warehouse studied thus, compounded by the initial low productivity. To gain more from lean, the principles should be adopted by the warehouse as part of its cultural practices [88].

The involvement of academia certified in six sigma (black belt) alongside a quality management student was helpful in the improvement of the warehouse productivity by application of lean tools and also in the establishment of the improvement framework. Therefore, academic-industry collaboration is recommended. Customer centre in warehousing operations blended with lean offers a unique combination to solve complex real-time problems of customer dissatisfaction.

6. Lesson learnt and limitation of study

This study contributed to improvement of warehousing using LSS methodology by encouraging industry and academia collaboration [61,89] for building academic-industry synergy. From the study, successful improvement in the warehouse processes were achieved through waste analysis and elimination using LSS as a continuous process achievable over a period of time [[90], [91], [92], [93]]. Also, high productivity in organization can be made possible as a continuous improvement process [94,95].

The study has the following limitations:

• i.The conclusions were based on data from single warehouse which may not be sufficient to draw general conclusion for other systems of warehousing.
• ii.Some lean software may be difficult to be adopted for the validation of the process due to the data that were considered
• iii.The case study considered was limited to third party logistics, which made the improvement process to be flexible unlike when dealing with organization operating as total supply chain or multiple warehousing. Appropriate warehousing process parameters and factors for process efficiency can be achieved using multi criteria decision model and predictable approach [96,97].

7. Conclusion and implication

The study has developed a lean improvement framework for warehousing in terms of process cycle efficiency by minimizing waste using LSS via presentation of some theoretical and empirical deductions. The conclusions drawn from this study were as follow:

• i.The percentage improvement in the productivity of the warehouse is estimated as 76.9%. This is derived from the increase in the PCE from 39.8 to 70.4% by implementing VSM, Kaizen and some basic warehouse lean tools.
• ii.The percentage reduction in the lead-time is estimated at 43.5%, achieved by the reduction in the lead time from 233,160 s to 131,769 s.
• iii.The percentage reduction in the non-value added time is estimated at 72.1%, achieved by the reduction of the non-value added time from 140,460 s to 39,069 s
• iv.The success of the methodology presented in the study is an indication that it can be replicated with other process parameters like quality, total turn-around etc. Also, LSS is suitable for improvement of process and organization productivity. The methodology can also be used to achieve product quality at optimal cost.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of competing interest

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.