Minimizing Forklift capacity through Workload Control at Nippon Express NL

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Delft University of Technology

FACULTY MECHANICAL, MARITIME AND MATERIALS ENGINEERING

Department Maritime and Transport Technology Mekelweg 2 2628 CD Delft the Netherlands Phone +31 (0)15-2782889 Fax +31 (0)15-2781397 www.mtt.tudelft.nl

This report consists of 64 pages and 8 appendices. It may only be reproduced literally and as a whole. For commercial purposes only with written authorization of Delft University of Technology. Requests for consult are only taken into consideration under the condition that the applicant denies all legal rights on liabilities concerning

Specialization: Production Engineering and Logistics

Report number: 2014.PEL.7862

Title:

Minimizing Forklift capacity through

Workload Control at Nippon Express NL

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This report consists of 64 pages and 8 appendices. It may only be reproduced literally and as a whole. For commercial purposes only with written authorization of Delft University of Technology. Requests for consult are only taken into consideration under the condition that the applicant denies all legal rights on liabilities concerning the contents of the advice.

Title (in Dutch) Minimaliseren van de heftruck capaciteit vie Workload control bij Nippon Express Nederland

Assignment: Masters thesis

Confidential: yes

Initiator (university): Prof.dr.ir. G. Lodewijks

Initiator (company): W. van der Steen (Nippon Express Nederland)

Supervisor: Dr. W.W.A. Beelaerts van Blokland

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Student: T.A. Aarts Assignment type: Master thesis

Supervisor (TUD): Dr. W.W.A. Beelaerts van Blokland

Creditpoints (EC): 35

Supervisor (Company) W. van der Steen Specialization: PEL

Report number: 2014.PEL.7862 Confidential: Yes

Subject:

Minimizing Forklift capacity through Workload Control at Nippon Express NL

Introduction

Nippon Express NL is a third party logistic warehouse service provider. At they’re Amstelveen site, they are challenged by a varying customer demand where the maximum load is nearly twice the average workload. They wish to explore the ability to reduce they’re forklifts assets because these are the most costly and inflexible assets in house while also aiming to reduce there current lead times. Company Objective

NEN seeks to optimize their warehousing activities in order to minimize the resources used and reduce order throughput time while complying to the requirements and throughput capacity set by CENV. NEN therefore seeks alternative warehouse systems that could improve the utilization of the material of CCB and improve the control of the workload of CCB.

Problem definition

This thesis firstly explores the current situation in regards to lead times, waiting times and forklift utilization in order to try to improve the current situation through a combination of capacity control and workload control. The research question this thesis aims to answer is thus,

How to reduce order throughput time by 40% while stabilizing the usage of forklift assets to reduce on site forklift capacity by 20%?

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Structure of the report

 Explore the current situation at the warehouse location

 Using the methodology of systems thinking, Value Stream Mapping, and throughput diagrams, further analyze the current state of the warehouse and find potential for improvement.  Define improvement objectives and criteria and constraints in which a solution for the forklifts

capacity and utilization should be presented

 Explore and select techniques which comply to the objectives. Using a multi criteria analysis, select the best applicable solution, workload control

 Elaborate on the technique of the provided solution and create a calculation model to test the potential of workload control

 Discuss results, conslusions and recommendations.  Study relevant literature

The professor, Supervisor,

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Preface

This graduation thesis would not have been possible without the assistance of Nippon Express

Nederland, in particular Mr. W. van der Steen. They were so kind to find a project which could benefit both company and student and invest there time to aid the author. Hopefully, the content of this thesis will be of help to the further development of Nippon Express. Also, the author is thankful for the patience and assistance of Dr. W.W.A. Beelaerts van Blokland for giving guidance during the

completion of this thesis. Furthermore, Mr. Hartsink and Mr. Lampe have been very helpful to the completion of this project and are thusly and duly thanked for they’re time and assistance. The author’s parents are also thanked for they’re continuous trust, or perhaps faith, in the author in reaching this final point. Lastly, Miss van der Meer is thanked for her support, understanding and devotion to the author during this project.

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Summary

Nippon Express NL is a third party logistic warehouse service provider. At they’re Amstelveen site where they cater only to Canon Europe N.V., they are challenged by a varying customer demand where the maximum load is nearly twice the average workload. Within CCB a number of activities are performed for CENV. NEN seeks to optimize their warehousing activities in order to minimize the resources used and reduce order throughput time while complying to the requirements and throughput capacity set by CENV. NEN therefore seeks alternative warehouse systems that could improve the utilization of the material of CCB and improve the control of the workload of CCB. Using a flow model, the processes within CCB are identified as well as the various flows of goods within CCB. Each of these flows, or product families, are input for creating a value stream map to determine the throughput times and value added times. As example of the result of the VSM, the average lead time of inbound combination 01000010101 is 09:47:15 while the VAT is 01:49:13. Meaning the VAT is only 18,6% of the lead time. In other words, 81,2% of the time (07:58:02) the work necessary stands still somewhere in CCB. One of the main reasons for these large waiting times is the amount of work that is released unto the work floor. The capacity and workforce of CCB is being utilized to their best abilities. Thus an explanation to the relation between VAT and NVAT can be the amount of work in progress (WIP).

To investigate the effect of the large waiting times and assumingly also the large WIP and the current order execution strategy, a throughput diagram is constructed. It shows that the average WIP

balances around the 300 hours of work. Considering that the average amount of orders executed per day also equals around 300 hours. This means that the WIP equals an entire workday of orders. Using the output throughput diagram data for the forklifts it is possible to determine when tasks have been fulfilled. This can then give an indication to the utilization of these forklifts on those times. Research objective

Improving the work release strategy should also aid in reducing the WIP and thus reducing the NVAT and improving the lead times. Also, it should be able to stabilize the utilization of the forklifts which are a focus of this research in an attempt to reduce the needed capacity on site.

RQ: How to reduce order throughput time by 40% while stabilizing the usage of forklift assets to reduce on site forklift capacity by 20%?

A production planning technique or workload planning technique needs to operate within the boundaries of CCB and fulfill the objective set. To select an applicable technique which not only best serves the warehouse but also the wishes of NEN, a multi-criteria analyses is used. The different techniques will not be discussed here due to limited space. Following the multi criteria analysis, Workload Control (WLC) satisfies all criteria set by NEN.

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The main principle of WLC is to control the lengths of queues in front of workstations on the shop floor (Kingsman, 2000). The goal of WLC at CCB is to minimize the needed capacity for forklifts by stabilizing the usage of forklifts.

The approach to the objective is to calculate the effect on reducing the forklift capacity step by step to find the minimum number of forklifts needed. During these calculations, for all periods t, the forklifts output rate is set to the maximum capacity. Each run the maximum capacity is reduced by one. The capacity is plotted against the maximum value of T which resulted from the WLC

calculation. This would be the orders which is finished last. This value cannot exceed 16 hours as shown by het dashed line. The minimum number of forklifts which CCB could use without

compromising its delivery lead time is 9 forklifts. Reducing the capacity any further would exceed the delivery lead time TP.

The order throughput time has been reduced by 87% for inbound orders and 92% for outbound orders with the assistance of WLC. The minimal needed capacity in December, to not conflict with delivery constraints is 9 forklifts. There are currently 18 forklifts on site, a reduction of 50% is possible assuming December is a representative month for the every month at CCB. Thus WLC is an interesting tool to answer the RQ.

The ability to stabilize the usage of assets exists within WLC but the approach chosen was to find the minimum capacity needed which do not conflict with delivery constraints. Because of limited

resources and time available for the author, the ability to stabilize the usage of an asset during the day is not further explored.

Furthermore, the focus of the WLC model to find the minimal number of needed forklifts neglected a constraint which would normally be present at a production or warehouse, the availability of

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Summary (Dutch)

Nippon Express in een logistieke dienstverlener die op zoek is naar een oplossing voor één van de magazijnen die zij beheren in dienst van Canon. De orders die Nippon vanuit Canon ontvangt varieren sterk per dag. Als gevolg hiervan heeft Nippon genoeg capaciteit om de maximale orders te kunnen verwerken. Maar hierdoor staat het merendeel van de tijd het materieel still omdat de pieklast wel 2 keer de gemiddelde last is.

Door middel van systeemkunde, Value Stream Mapping en Doorloop diagrammen worden de symptomen binnen het magazijn blootgesteld. Het blijkt dat de doorlooptijden heel groot zijn dat de ratio Value Added Time en Non Value Added Time wel lager dan 10% kan zijn. Dit is een gevolg van de grote work in proces. De gemiddelde WIP op elk moment van de dag is even groot als de

gemiddelde hoeveelheid werk het magazijn moet verzetten. De WIP is het gevolg van de order vrijgave strategie. Het magazijn geeft vroeg, heel veel orders vrij zodat zij gedurende dag de mensen aan het werk kunnen housen. Hoewel dit wellicht werkt veroorzaakt het ook een hoop WIP en dus goederen die stil staan waar aan gewerkt moet worden. Daarnaast varieert het verbruik van

vorkheftrucks gedurende dag. Door de doorlooptijd, en WIP is het niet transparant de capaciteit van de vorkheftrucks te sturen.

De onderzoeksvraag luidt dan ook hoe de doorlooptijd met 40% te reduceren en de vorkheftruck capaciteit met 20% te kunnen verkleinen?

Een aantal technieken die geschikt zouden zijn voor dit magazijn worden bekeken en getoetst via een Multi criterium analyse. De meest geschikte kandidaat is Workload Control. Het systeem van de Workload control is on de vrij giften van orders zo te reguleren dat er niet teveel orders worden vrijgegeven. Het doel van de workload control tool is een minimale vorkheftruck capaciteit te vinden die de voorwaarden van het magazijn niet schaden. Het blijkt dat het magazijn met 9 i.p.v. 18 heftrucks moet kunnen voldoen a.d.h.v. de productie gegevens van de maand december 2013. Verdere afname van capaciteit zou de voorwaarden schaden.

Door het gebruik van Workload Control verminderd de doorlooptijd met 87% voor inbound order en 92% voor outbound orders. En tegelijkertijd wordt een reductie van de vorkheftruck capaciteit van 50% behaald.

De onderzoeksvraag is daarmee beantwoord..

Hoewel verwacht werd dat workload control het gebruik van vorkheftrucks zou moeten kunnen stabiliseren is hier niet voldoende bewijs voor. Dit zou ook ten laste kunnen liggen van het moduleren van dit systeem in Excel. Verder onderzoek zoals simulatie zou hierover een antwoord kunnen geven.

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List of symbols

General Terms (as index)

r,j,n,t,k Indices

max Max value

Variables

Symbol Unit Meaning

I hours Input of work

C hours Output rate of work

L hours Processing time of work at a workstation

b hours Buffer time of work at a workstation

OCD date Operation Completion Date

W hours Required output of work

Z hours Actual work done

PB hours Planned Workload

TP time Total allowable leadtime

TB hours Total Workload

op units Number op operators

NT hours Normal time processing hours capacity

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List of abbreviations

NEN Nippon Express Nederland NEEUR Nippon Express Europe

EMEA Europe, the Middle East and Africa 3PL Third Party Logistics

TPL Third Party Logistics

JIT Just in Time

4PL Fourth Party Logistics CENV Canon Europe N.V.

CCB Company code for Amstelveen Canon Warehouse CIG Consumer Imaging Goods

PV Photo Video

SKU Stock Keeping Unit

GCI Generalized Complexity Index VSM Value Stream Mapping

VAT Value Added Time

NVAT Non Value Added Time

WIP Work in Process

WLC Workload Control

JIT Just In Time

TOC Theory of Constraints LOOR Load Oriented Order Release

ORLP Order Release with Linear Programming

POLCA Paired-Cell Overlapping Loops of Cards with Authorization QRM Quick Response Manufacturing

CONWIP CONstant Work In Process

m-CONWIP multiple CONstant Work In Process FIFO First In First Out

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1. Introduction

1.1 Nippon Express Nederland B.V.

Nippon Express Nederland B.V. (NEN) is a part of Nippon Express Europe (NEEUR) which in turn is a part of Nippon Express Co., Ltd. founded in 1872 in Japan. With over 130 years experience in international forwarding and global logistics needs and 1400 offices worldwide, forwarding over 400 millions tons of total freight, the company offers highly competitive services and rates. Moreover, Nippon Express is ready to design “tailor made” services to fit any customer request to increase the competitiveness of their products. Nippon Express has warehouse property totaling 3.600.000 m2

located all over the world. With this space availability the group is able to satisfy any kind of logistics and distribution needs by its customers.

Warehousing together with Freight Forwarding is NEEUR’s core business, with almost 30% of NEEUR’s activities classified as warehousing. In Western and Central EU the proportion of NEEUR’s activities which are warehousing is even higher as NEEUR has a higher concentration of high volume warehousing in this area. These operations range from large volumes of small SKU’s to full pallet operations. NEEUR operates approximately 350,000 m² of warehousing and logistics space across the EMEA region. A large portion of this warehouse space is located in Western and Central Europe (313,000 m²). NEEUR operates up to forty warehouses in the EMEA region of which eight are larger than 15,000 m².

FIGURE 1. NIPPON EXPRESS WAREHOUSING FLOORSPACE

NEN operates from several locations within the Netherlands with the most notable locations being Amsterdam, Eindhoven and Rotterdam. Nippon Express Nederland has developed strongly in the area of Warehousing and holds around 140.000m2_{of warehouse space.}

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As mentioned, NEN is a service provider with a large presence in warehousing. NEN services several clients and provides activities varying size and range. NEN can be strategically distinguished as a customer adapter who also provides standard solutions for smaller clients.

Nippon Express Nederland aims to (Anon., 2013):

1. Provide high quality service to ensure customer satisfaction. 2. Achieve a substantial profit for its shareholders and investors.

3. Pursue satisfying work that inspires pride to aid our employee's well being. 4. Work with environmentally friendly conservation to contribute to society.

1.2 Warehousing

Warehouses are a key aspect of modern supply chains and play a vital role in the success, or failure, of businesses today (Frazelle, 2002a). Although many companies have examined the possibilities of synchronized direct supply to customers, there are still many circumstances where this is not appropriate. This may be because the supplier lead times cannot be reduced cost effectively to the short lead times required by customers, and hence these customers need to be served from inventory rather than to order (Harrison & van Hoek, 2005). Similarly, it may be beneficial to hold strategic inventory at decoupling points in the supply chain to separate lean manufacturing activities (which benefit from a smooth flow) from the downstream agile response to volatile market places

(Christopher & Towill, 2001). Alternatively, the supply and distribution networks may be of sufficient complexity that there is a need for goods to be consolidated at inventory holding points so that multi-product orders for customers can be delivered together i.e. at break-bulk or make-bulk consolidation centers (Higginson & Bookbinder, 2005). The operations of such warehouses are critical to the provision of high customer service levels. A large proportion of warehouses offer a same-day or next-day lead-time to customers from inventory (Baker, 2004) and they need to achieve this reliably within high tolerances of speed, accuracy and lack of damage. According to the current principles of supply chain management, modern companies attempt to achieve high-volume production and distribution using minimal inventories throughout the logistic chain that are to be delivered within short response times. Low volumes have to be delivered more frequently with shorter response times from a

significantly wider variety of stock keeping units (SKUs) (van den Berg & Zijn, 1999).

Warehousing by 3PL’s

Nowadays, many firms outsource their logistics processes to other companies thus introducing the third-party logistics (TPL or 3PL) provider. The first party is the shipper or supplier and the second party is the buyer. The third party is a firm acting as a middleman not taking title to the products but to which logistics activities are outsourced. The activities performed can include all or a part of the logistics activities but at least management and execution of transport and warehousing should be included (Berglund, 2000). Some researchers examined how organizational characteristics and strategies of users of logistics services are related to the decision to enter in 3PL arrangements. Rao

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and Young (1994) identified three main characteristics of shippers’ business profile that drive their logistics outsourcing behavior and influence the formation of a favorable or unfavorable climate for outsourcing:

a) Network complexity, referring to both the geographic dispersion of the firm’s trading partners as well as the intensiveness of transactions with selected trading partners.

b) Process complexity, referring to time and task compression (or lack thereof) in the logistics process.

c) Product complexity, relating to the special circumstances required by products and materials due to the complexity of the environment (temperature, humidity, etc.) governing their transportation, storage and handling.

With regard to the external environmental factors impacting 3PL, a comprehensive descriptive analysis of the several economic, regulatory and technological drivers behind the rise of 3PL has been provided by Sheffi (1990). Increased global competition, deregulation of the transportation industry, rising customer expectations on superior logistical service, growing focus of companies on core

competencies, increasing popularity of just-in-time (JIT), and revolution in computers and communication technology are indicated as the main forces causing 3PL services to experience explosive growth.

When successful, 3PL relationships can give both parties a competitive advantage in the marketplace (Tate, 1996). Literature has shown that involvement in 3PL arrangements, especially cooperative, partnership-like relationships, can result in multiple economic, organizational and financial benefits for shippers such as reduced logistics cost, improved service levels and end-customer satisfaction, improved access to and application of technology, reduced capital investment in facilities, equipment and manpower, increased flexibility and productivity, improved employee morale, increased access to wider markets and new competencies (Bowersox, 1990; Daugherty, et al., 1996; Ellram & Cooper, 1990; Larson & Gammelgaard, 2001).

Balancing between adaptation and general ability for problem solving

The way the 3PL providers manage the relationship to its customers and handle effects on the total network of relationships will be of basic importance for their strategic edge on the market. To develop skills, competencies, and gain scale/scope advantages that are superior to customers will be

necessary in order to add customer value. Such a development mostly necessitates co-utilization of resources, creation of specific knowledge, and coordination of activities of a portfolio of customers. Therefore, a main challenge for a 3PL provider is to balance between an ability of high adaptation to individual customers and organizing the systems and the business for coordination of several

customers. The way this is balanced will guide the strategic development of the 3PL providers and is of vital importance for the resources needed, activities to be performed, and core competence development.

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The customer coordination could be interpreted to reflect in the degree of problem-solving ability since there should be a need of a higher general ability when coordinating several customers. The ability of customer adaptation as the other dimension is well suited for the specific purpose.

Håkansson and Johanson (1982) applied these dimensions when studying the traditional logistics and transport firms and Hertz and Alfredsson (2003) continued on this concept. (See Figure 2. Problem-solving abilities—3PL provider position ). In the matrix, the 3PL providers are classified as ranging from relatively high to high in both the dimension of customer adaptation and in a general problem-solving ability. This implies that balancing these dimensions would be one of their main tasks for their strategic development.

 The standard 3PL provider (3 in the figure) could be seen as supplying the standardized 3PL

services like warehousing, distribution, pick and pack, etc. This firm would often offer these services at the side of their normal business.

 The 3PL as service developer (1) is seen as offering advanced value-added services. This could involve differentiated services for different customers, forming specific packaging, cross-docking, track and trace, offer special security systems, etc. The focus would be more on creating economies of scale and scope.

 The customer adapter (4) could be described as the 3PL firm taking over customers’ existing

activities and improving the efficiency in the handling but actually not making much development of services. This type of provider might take over customers’ total warehouses and the logistics activities and relies on a few very close customers.

 The customer developer (2) is the most advanced and difficult form. It involves a high

integration with the customer often in the form of taking over its whole logistics operations. The possibilities to coordinate customers rather lie in the know-how, the methods, the knowledge development, and the design of the supply chain. The number of customers would be limited and the work for each customer extensive. The customer developer or ‘‘logistics integrator’’ or ‘‘complexity manager’’ would be similar to what some call a 4PL. Such a firm is sharing the risk and rewards of the logistics management with the customer (Moore, 1987).

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NEN closely resembles the standard 3PL provider and the customer adapter. NEN services several clients but the activities per client vary and thus for some clients, standard solutions are offered while for others, a more specialized service is offered. The warehouse subject to this research can be considered a specialized costumer solution as NEN offers a complete logistic package and handles all of its western European logistics.

1.3 Scope

This research will focus on one of three warehouses which are serviced for a single client, Canon Europa N.V. (CENV). NEN receive products from the production locations of Canon Inc. throughout the world, holds and manages all of the stock for the western European consumer market as well as supplying the main distribution center of the eastern European market. The warehouse which is the focus of this research, from this point onwards named CCB in accordance to company location codes, only holds photo and video consumer imaging products (CIG PV). Such products are photo and video camera’s, lenses, batteries but also cartridges, instruction manuals, bags, etc. It houses over 3400 Stock Keeping Units (SKU’s) in an area of 25.000 m2_{. One SKU is equivalent to one product which is}

stored in one specific way. Meaning a product that is stored as a pallet, a case and as an individual item thus can be picked as a pallet, a case and a single piece thus is equivalent to three SKU’s. This research will aim to provide insights into one of the current problems within CCB, namely the variability of the use of its forklifts assets, and suggest an approach to mediate this problem.

Firstly, a description of the processes and problems currently within CCB will be explored in chapter 2 which leads to a problem definition according to Nippon Express. Chapter 3 will elaborate on these issues using a systems approach combined with the use of a lean manufacturing tool, Value Stream Mapping (VSM) and the use of the funnel model to quantify current production performance. Chapter 4 summarizes these findings and offers potential improvements to further explore. One of these options leads to the research question which this thesis aims to answer. Namely, will improving production planning by workload control aid to efficiently utilize its forklift assets? Chapter 5 will further elaborate on workload control and production planning techniques which are best applicable in the CCB warehouse leading to one strategy which will be used to answer the research question. Chapter 6 gives detailed information into the workings of this strategy and its application within the CCB warehouse. A calculation model in Microsoft excel is created following the principles of the workload control strategy explained in chapter 6. The results of this model is shown in chapter 7 and discussed in chapter 8. Thus in chapter 9 the research question and objective formed in chapter 4 is reviewed in respect to the workload strategy applied. Providing the conclusions from this thesis and recommendations for further research and implementation within the CCB framework.

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2. Process Description

2.1 Warehouse Amstelveen

Within CCB a number of activities are performed for CENV. In short, the function of warehousing which NEN fulfils for CENV can be described as follow:

Products are received and stored from which a shipment of products is consolidated in accordance to specific orders and requirements set by CENV and it’s customers which then can be loaded and shipped through carriers.

FIGURE 3. BLACK BOX OF CCB

2.2 Processes

The main activities include: receiving, transfer and put away, order picking/selection, shipping.

Receiving includes the unloading of products from the transport carrier, updating the inventory record,

inspection to find if there is any quantity or quality inconsistency. Subsequently, the loads are

prepared for transportation to the storage area. This means that a label is attached to the load, a bar code. If the storage modules (e.g., pallets, totes or cartons) for internal use differ from the incoming storage modules, then the loads must be reassembled or repackaged.

Transfer and put away involves the transfer of incoming products to storage locations. It may also be

physical movements (from the receiving docks to different functional areas, between these areas, from these areas to the shipping docks).

Order picking is the process of retrieving a requested product from storage. An order lists the

products and quantities requested by CENV. Order picking involves the process of clustering and scheduling the orders, assigning stock on locations to order lines, releasing orders to the floor, picking the products from storage locations and the disposal of the picked articles. Orders consist of order lines, each line for a unique SKU, in a certain quantity. Multiple order picking systems are employed within CCB. An item picking operation, an operation in which single items are picked from storage positions. This can be either a pallet-picking in which pallet loads are picked, case-picking which only picks a full case from a storage location, or a less-than-case picking or piece-picking where one or more single items are picked from their respective locations. When an order contains multiple SKUs, these must be accumulated and sorted (note that each SKU in an order corresponds to a unique item

Consolidate Receive Factory Products Shipped Consolidated Products Requirements Performance

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of supply), so the order can be consolidated into a submanifest to meet CENV’s order. Thus

accumulation and sorting is performed.

Finally, products ready for customers will be transported to the shipping area where they can be checked, labelled, and temporarily stored until they can be loaded into a truck and be shipped. Furthermore, NEN provides a value added activity within CCB called kitting which consists of combining different pieces within the warehouse into one container or kit. This requires picking activities and put away activities. Lastly, to ensure smooth operations within the warehouse, it is necessary to replenish picking locations and relocate loads to clear space for incoming loads. Thus the final activities are replenishing and relocating.

These activities are split into two main operations. Namely, the inbound operations and the outbound operations. At the inbound side, loads are received from factory locations. Most are received through ocean freight and few by airfreight. They are processed and stored. These containers often contain little product variety but in a large quantity. Outbound contains the activities of order picking and shipping. Customers from CENV place orders at CENV who in turn collect these orders and forward them to NEN at the end of the day. Furthermore, NEN holds up to 3 weeks worth of CENV’s inventory according to CENV specifications. Kitting, relocating and replenishment are not categorized within inbound and outbound, they can be considered in-house activities to support the inbound and outbound operations.

FIGURE 4. MAIN ACTIVITIES WITHIN CCB SPLIT IN INBOUND AND OUTBOUND OPERATIONS

2.3 Order Variety

The number of orders NEN receives from CENV vary per day. As a result, NEN finds it difficult to make optimum use of its current capacity. CCB holds equipment to handle the maximum workload.

However, the utilization of this equipment is only maximum when the workload offered is the

maximum of the CCB capacity. Although most of the manual workforce is flexible and thus varies each day, the availability of the equipment and space is not flexible and therefore on most days not

optimally utilized.

To give an illustration of the variability of the inbound and outbound operations, a summary of the data from 15-10-2012 till 04-10-2013 is shown in the table and graph below. It can be seen that there is a large difference in the average number of pallets moved in a day and the maximum number of pallets moved during one day. Assuming that 100% utilization of the capacity CCB is equivalent to handle the maximum number of pallets which were received and shipped. Under such assumptions, on average, inbound and outbound are utilized for 50% and 37% respectively. Note that on 80% of

Inbound Outbound

Receive Factory Products

Receive Store Pick Load

Shipped Consolidated

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the year that pallets are received and shipped, CCB only utilizes 64% or less of inbound capacity and 47% or less of outbound capacity. These are simple assumptions to showcase the variability of the orders CCB has to deal with and are not actual utilization numbers of the assets.

TABLE 1. PALLETS MOVED A DAY, AVERAGE, MIN AND MAX.

Pallets Received Pallets Shipped Total pallets moved

pallets pallets pallets

Average 552 529 1283

Min 126 127 558

Max 1107 1425 2170

80% of days 711 672 1551

In this example only the pallet movements are considered. For these specific movements, the equipment and personnel used for inbound and outbound are the same. Thus the movements of inbound and outbound per day should be added to give a representation of the total needed capacity for these movements.

FIGURE 5. VARIABILITY IN PALLETS MOVED PER DAY FROM 15-10-2012 TILL 04-10-2013

The graph shows the volatility of the inbound and outbound pallet loads. However, pallet movements alone are not a sufficient metric to determine the associated amount of work. Because the shipments that arrive at inbound undergo a number of transformations to conform them to the requirements of the warehouse. These activities also vary and thus the amount of work associated varies. The same is valid for the outbound area. An order consisting of full pallets is easier assembled compared to an order which holds only piece picked products.

The major reason for the variability in orders throughout the year is due to CENV. CENV is a sales driven organization, as a result sales peak at the end of the month leading to a larger number of orders sent from NEN and thus CCB. Also, CENV provides its customers with a service enabling them to order items approximately 24 hours before the desired shipping date. NEN has 24 hours or less, according to the order, to collect the order and prepare it for shipment. Most shipments contain a

0 500 1000 1500 2000 2500

15-Oct 15-Nov 15-Dec 15-Jan 15-Feb 15-Mar 15-Apr 15-May 15-Jun 15-Jul 15-Aug 15-Sep Pallets Shipped Pallets Received 80% of Total pallets moved

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large variety of SKU’s each in a small quantity. Usually, at 18:00 NEN has received orders from CENV what to ship the following day. These orders are around 90% complete, last minute changes and last minute add-ins make up the last 10%. NEN has no influence on the number and composition of outbound orders. Inbound shipments are also provided by CENV, however NEN has some influence in the number and type of inbound container to receive.

Currently, planning uses the order information of CENV to plan the inbound and outbound workforce needed the following day. The order picklines within the order information gives an indication of the work needed. For example, to complete one pickline of piece picking or one pickline of pallet picking, one worker needs for about 2 minutes. During these two minutes either one box containing a few items of one SKU is collected or one pallet of one SKU is collected. The following graphs show the volatility of the picklines and the ratio of piece, case and pallet picklines. It should be noted that a clear trend isn’t visible in the behavior of the picklines. Except at every end of the month there is an increase in picklines, which is also reflected in the peaks of the pallets shipped in the graph above. This is a result of the sales driven organization of CENV. Large peaks are also visible around

October/November, these are usually the result of the holiday season. Furthermore, planning uses the orderlines as an indicator for the expected workload. Using the average picks per line and picktype per order, the size of the needed workforce the following day can be determined. They review the

averages each quarter to maintain planning accuracy.

0 1000 2000 3000 4000 5000 6000 7000 8000 9000

15-Oct 15-Nov 15-Dec 15-Jan 15-Feb 15-Mar 15-Apr 15-May 15-Jun 15-Jul 15-Aug 15-Sep Outbound picklines 80% of Outbound picklines

CIG-PV Outbound (260) PIECE picklines 64,81% CASE picklines 27,99% PALLET picklines 7,20% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

orderlines

CIG-PV Outbound (260) Pallets 64,19% Manifests 35,81% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Shipped

Pallets

Equivalent

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2.4 Capacity

As mentioned, the current CCB warehouse is equipped to handle even the maximum workload throughout the year. However, this capacity may be redundant for most days in the year. Under the simple assumptions that the capacity of CCB can handle the maximum number of picklines in a year, in the current situation the assets available in CCB may not be used in the most efficient and

economical way.

Also, the outbound orderliness are usually executed hours before the shipment needs to be ready for loading to ensure that the shipment is ready and shipped on time. Thus, outbound orderliness are released to the work floor several hours before they need to be loaded. As a result, more work is in progress and outbound docks are being used for several hours while most of this time nothing

happens to the shipment. The docks are used as a buffer for ready shipments. While it provides safety margin for the order fulfillment, it uses more space then necessary and pallets on docks are more prone to damages when compared to in storage. Lastly, the capacity of both the workforce and the assets like forklifts are not consistently utilized throughout the day.

Meaning, workload fluctuates each day, is released in large quantities onto the warehouse and the execution of the workload also fluctuates.

2.5 Nippon Problem Definition

NEN seeks to optimize their warehousing activities in order to minimize the resources used and reduce order throughput time while complying to the requirements and throughput capacity set by CENV. NEN therefore seeks alternative warehouse systems that could improve the utilization of the material of CCB and improve the control of the workload of CCB.

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3. System Approach

Using a flow model, the processes within CCB are identified as well as the various flows of goods within CCB. Each of these flows, or product families, are input for creating a value stream map to determine the throughput times and value added times. The funnel model gives insights in the amount of work in process (WIP) and productivity of CCB and each individual activity necessary for the inbound and outbound processes.

3.1 Flow model

The main activities of CCB are receiving goods, storing goods, picking goods and loading goods for shipping, as can be seen in Figure 6. These processes are divided into inbound and outbound processes. This report shall maintain this division of activities and add another division, in-house processes.

FIGURE 6. MAIN ACTIVITIES WITHIN CCB SPLIT IN INBOUND AND OUTBOUND OPERATIONS

Inbound

At the inbound side, cargo arrives via ocean or airfreight. Furthermore, the steps necessary to store the cargo varies because of the many different factories of Canon Inc. As a result, CCB performs several operations to transform the cargo according to CENV and NEN requirements into storage. Modes of inbound cargo:

Slipsheets: cartons stored on slipsheets instead of pallets. They need to be palletized and afterwards,

some need their serial numbers scanned and logged into the Warehouse Management System (WMS) while others can be stored directly. Slipsheets always arrive via sea freight.

Pallets: Either via sea or air, different pallet type cargos arrive requiring different operations

depending on the cargo. Thus pallets flow through different routes through inbound into storage. Some need to be rebuild, others suck as CO8 cargo requires unwrapping and serial scanning while some can be logged into the WMS directly for storage.

Cartons: It s also possible that containers arrive that carry cartons that are not stacked onto a certain

carrier. These cartons are need to be unloaded and palletized unto a pallet before continuing through inbound. The same applies to the cartons as the pallets. Some can be logged into the WMS directly while others require re-labeling, and/or serial scanning.

Inbound Outbound

Receive Factory Products

Receive Store Pick Load

Shipped Consolidated

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RMA’s: There are also return goods from customers. They arrive via truck through parcel or truckloads from other distribution centers. They are unloaded and recorded into WMS and storage from where the RMA department can further investigate.

After the receiving goods are logged into the WMS, also referred to as arrivalling the cargo, they can be stored in the warehouse. CCB has 3 modes of storage; highbay storage, highbay storage and floor storage. Depending on the type of product and available space within CCB goods are stored at their respective locations.

The inbound operations can be described in the following flow scheme shown in Figure 7.

Receive Factory Products 0.0 Receive Truck 1.4 Unload boxes 1.1 Unload slipsheets 1.2 Unload pallets 1.3 Palletize cartons 3.1 Re-Palletize 3.2 Re-Label 2.0

AIR Sortout & Palletize 5.0 Print label & Scan Inbound 4.0 Scan Serial numbers 6.1 Bring to buffer Highbay 6.2 Bring to buffer Rack 7.3 floor 7.1 Highbay 7.2 Rack CIG-PV Inbound

FIGURE 7. FLOW SCHEME INBOUND

The warehouse management system currently in use in CCB records various information in order to track the location of goods within the warehouse so to increase the picking accuracy towards CENV’s costumers. Using the WMS data available from 02-12-2013 till 31-12-2013, it is possible to view all possible routes incoming goods may take from receiving to storage. Table 2 shows a selection of the results from this data. Appendix BAppendix B: Inbound Information Dec-2013 gives a complete overview of the inbound goods from 02-12-2013 till 31-12-2013. The combination code contains information concerning the mode of incoming goods and the operations that are necessary until the goods can be arrived into the WMS and then moved to storage. This combination thus also serves to identify product families that can be used to create a value stream map in the next paragraph.

TABLE 2. SELECTION OF INBOUND COMBINATION OF GOODS PER TRUCK FROM 02-12-2013 TILL 31-12-2013

Inbound Combination Count of Truck Reference 1000000000 42 0001000001 17 0100010001 4 0000010010 14 0000010001 22

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0000101000 7

0010001001 3

0000010000 23

0000001000 15

Each number in the combination code identifies if that corresponding operation is executed or not by the number 1 for yes and 0 for no. Table 3 gives an example of the first combination shown in the table above and the operations associated. In this example, the slipsheets goods are unloaded and palletized and then ready to be arrived into the system.

TABLE 3. EXAMPLE INBOUND COMBINATION CODE AND CORRESPONDING OPERATIONS

Slips, Sea Plts, Sea Plts, Air CO8, Sea plts CO8, Air plts Crtn, Sea Crtn, Air Rebuild plts Relabel ctns Serial Scanning 1 0 0 0 0 0 0 0 0 0

Outbound

From the storage, orders are collected and made ready for shipment. As mentioned in the previous chapter, costumers from CENV can order in varying quantities thus CCB picks these orders from in the form of pallets, half-pallets, cases and pieces.

Full Pallets: Can be picked from all storage locations, highbay, rack or floor. A full pallet can be moved directly to the allocated outbound lane location near the dock. These items are identified by A1 and A2 picks.

Half Pallets: Are pallets that are not full according to the storage requirements. This can happen when cases have been picked from the pallets or when it is wiser to collect an order by removing a few cases from a full pallet instead of picking a lot of cases to create half a pallet. Picked from either floor locations or rack locations. These pallets need to be rebuild or/and wrapped before they can be moved to the outbound lane. These items are identified as A3, B1 and B2 picks. B picks take more time to process thus they are given a different identification.

Cases: Picked from the picking location by an operator with a trolley. Full cases are picked according to the order information. The cases then travel by a transportation belt, APA, to the sortation center where the weight is checked for product error and then transported to a sub-shute assigned to the corresponding order and/or route to be stacked onto a submanifest. This submanifest is then checked, labeled, wrapped and moved to the outbound lane. These items are C picks and F1 picks. C picks are located on the ground at the picking locations. F1 picks are stored in the racks, thus they require more time for picking.

Pieces: Picked from the picking location by an operator with a trolley containing empty boxes, repacks, in which the pieces are collected according to the order. Each case contains one order. Repacks then travel by a transportation belt, APA, to the sortation center where the weight is checked for product error and quantity error and then transported to a sub-shute assigned to the

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labeled, wrapped and moved to the outbound lane. These items are D picks and F2 picks. D picks are located on the ground at the picking locations. F2 picks are stored in the racks, thus they require more time for picking.

Goods on the outbound lane are checked and marked as ready for shipment. When the truck for the shipment arrives, it is loaded into to the truck and shipped.

The outbound operations can be described in the flow scheme shown in Figure 8.

14.0 Packing Rebuild 15.0 Transfer to Dock 7.2 Rack Export 16.0 Check 17.0 Loading 18.0 Loading Check 19.0 Ship Truck 9.2 Pick from Rack 9.3 Pick from Floor 20.0 Fold Cartons 9.5 Case Pick 9.6 Piece Pick 21.0 Prepare Trolley 10.1 Simple Packing 12.0 Create submanifest 13.0 Check sub chute Shipped Consolidated Products CIG-PV Outbound 9.1 Pick from Highbay 10.2 Complex Packing 11.0 Handling APA Rejects 7.3 Floor 7.1 Highbay

FIGURE 8. FLOW SCHEME OUTBOUND

Using the WMS data available from 02-12-2013 till 31-12-2013, it is possible to view all possible routes outgoing goods may take from storage till shipping. Table 4 shows a selection of the results from this data. Appendix C: Outbound information Dec-2013Appendix B gives a complete overview of the outbound goods from 02-12-2013 till 31-12-2013. The combination code contains information

concerning the compilation of outbound goods and the operations that are necessary to pick and send the items. This combination thus also serves to identify product families that can be used to create a value stream map in the next paragraph.

TABLE 4. SELECTION OF OUTBOUND COMBINATION OF GOODS PER ROUTE/TRUCK FROM 02-12-2013 TILL 31-12-2013

Outbound combination # Route # Orders / Route # picklines 000001 20 25 32 000010 68 100 154 000011 23 47 203 000100 18 18 27 000101 13 15 48 000110 3 6 8 000111 4 8 23 001000 43 49 67

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Each number in the combination code identifies if that corresponding picking operation is executed or not by the number 1 for yes and 0 for no. Table 5 gives an example of the first combination shown in the table above and the operations associated. In this example, the only picks needed for these routes are F2 picks..

TABLE 5. EXAMPLE OUTBOUND COMBINATION CODE AND CORRESPONDING OPERATIONS

A1 & A2 Picks

A3, B1 & B2 Picks

C Picks F1 Picks D Picks F2 Picks

0 0 0 0 0 1

In-House

As mentioned, to facilitate inbound and outbound flow of goods CCB has to perform extra activities. Kitting, Relocating and replenishing. Due to the goal of this research, improving throughput and optimizing use of forklift capacity, the process of kitting is not explored in detail but considered as a black-box wherein products are repacked into pre determined kits. These three in-house operations are all very similar., see Figure 9. Replenishment and relocating pick items from the storage locations and place them at another location to either replenish the picking locations or create space and/or consolidate SKU’s. Kitting also picks goods, then kits the products into one box, palletize the boxes and return the kitting goods in storage ready for future picking.

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Replenishment 7.1 Highbay 7.2 Rack 7.3 Floor 7.2 Rack 8.3 Pick from Floor 8.1 Pick from Highbay 8.2 Pick from Rack Kitting 7.1 Highbay 7.2 Rack 7.3 Floor 7.2 Rack 22.3 Pick from Floor 22.1 Pick from Highbay 22.2 Pick from Rack 5.0 Print label & Scan Inbound 7.3 Floor Relocations 7.1 Highbay 7.2 Rack 7.3 Floor 7.2 Rack 24.3 Pick from Floor 24.1 Pick from Highbay 24.2 Pick from Rack 7.1 Highbay 7.3 Floor 23.0 Kit (Picklocations)

FIGURE 9. FLOW SCHEME IN-HOUSE

Due to the simplicity of these processes, replenishment and relocations only have one flow when disregarding the different storage types. Thus each process has one product family relevant for creating the VSM. Kitting consists of basically 3 processes, collecting goods, kitting and put away goods. Because kitting itself is not considered, the product families for kitting are picking and stocking.

3.2 Value Stream Mapping

Now that the processes and correlating product flows within CCB are known it is possible to further identify production information associated with these flows. Value stream mapping from Lean manufacturing theory will be used to investigate the throughput times, value added times (VAT) and non-value added times (NVAT) from the inbound, outbound and in-house orders.

There is no agreed upon definition of lean that could be found in the reviewed literature by Pettersen (2009), and the formulations of the overall purpose of the concept are divergent. There seems to be quite a good agreement on the characteristics that define the concept, leading to the conclusion that the concept is defined in operational terms alone (Pettersen, 2009). This thesis follows the goals and theory laid out by Womack & Jones (1996) whose core idea is to maximize customer value while minimizing waste. Simply, lean means creating more value for customers with fewer resources. A lean

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organization understands customer value and focuses its key processes to continuously increase it. The ultimate goal is to provide perfect value to the customer through a perfect value creation process that has zero waste (Womack & Jones, 1996).

Lean methods target eight types of waste or muda (Brashaw & McCony, 2007):

1. Defects: money and time wasted for finding and fixing mistakes and defects

2. Over-production: making products faster, sooner, and more than needed

3. Inventory: raw materials, works in process (WIP), and finished goods more than the one

piece required for production

4. Over-processing: Tightening tolerances or using better materials than what are necessary.

5. Transportation: movement of people, materials, products, and information

6. Motion: Any people and machines movements that add no value to the product or service.

7. Waiting: time lost because of people, material, or machines waiting

8. Not using the talent of our people: not using experiences and skills of those who know the

processes very well

These wastes can be seen in all logistics activities such as distribution and warehousing. In other words, waste is anything that adds no value to a product or service. Lean manufacturing combines best features of both mass production and craft production, which means it intends to reduce cost and improve quality while increasing diversity of production (Womack, et al., 1990; Pavnaskar, et al., 2003). Lean manufacturing can lead to improved product quality and production levels; reduced cycle time, WIP, inventories, and tool investment; improved on-time delivery and net income; better space, machine, and labor utilization; decreased costs; and quicker inventory investment (Pavnaskar, et al., 2003).

Five principles govern the lean philosophy (Womack & Jones, 1996): 1. Identifying customer value

2. Managing the value stream 3. Developing a flow production 4. Using pull techniques

5. Striving to perfection

By applying these principles, waste within CCB can be identified and, depending on the goal of NEN, a strategy devised to mitigate these wastes. The first step will be to identify and quantify the value stream.

Identifying Customer Value

Value is an important and meaningful term in the lean context, meaning something that is worth

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NEN adds value by storing and consolidating goods in accordance to CENV’s and its costumer’s wishes.

Managing the Value Stream

Once value is identified, all required steps that create this value must be specified. Wherever possible, steps that do not add value must be eliminated. There are three types of activities in the value

stream; one kind adds value, and the other two are “muda” (the Japanese word for waste):

 Value-Added: Those activities that unambiguously create value.

 Type One Muda: Activities that create no value but seem to be unavoidable with current

technologies or production assets.

 Type Two Muda: Activities that create no value and are immediately avoidable.

Value Stream Mapping

To create a value stream map it is necessary to divide a companies products into families as to easier analyze its flow through the organization. This step is managed in the previous chapter. Next step is to gather the production information of each product family like cycle times, hours worked, batch sizes etc. Also order information is needs to be collected concerning customers’ requirements and demand. Lastly, production control information and control loops are needed like forecasting and planning. Luckily, NEN holds a comprehensive WMS which collects both order data and production information. In accordance with the requirements set by CENV, each movement of goods within the warehouse is logged into the WMS to ensure picking accuracy. Also, the planning of NEN is relatively basic because they can only plan the size of the workforce a day ahead. However, there are still moments when there is no exact information in WMS of an action taken. For instance, when a pallet is moved after being arrived into WMA after unloading it is moved to a buffer area before the storage space as can be seen in Figure 7 where after it is stored. In WMS the only actions registered are arrived pallet and stored pallet. For the whole unloading process the same applies, a truck is registered and the start of unloading is registered. The next registration in WMS is the arrived pallet. Although WMS might not provide detailed information regarding the hands on time of operators on goods, lead time between operations can be derived. Thus the necessary information to complete the VSM for each product family is created. Figure 10 shows such a VSM of inbound combination 0100010101.

On the left side, Canon Inc. suppliers of air and sea freight as suppliers. On the right side is CENV as the customer. Regarding inbound, CENV gives information which trucks are expected to arrive at CCB. NEN has some influence to change the order/number of truck by sea freight as they can be stored for a limited time at the terminal at the harbor if the truck planning by CENV doesn’t comply with CCB capacity. The truck planning combined with the total number of orders are used by NEN to plan the workforce the following day. Next, after the truck arrives it is unloaded according to specification. The numbers mentioned in the VSM are based on average times per truck calculated according to the data from 02-12-20130 till 31-12-2013. In the example of Figure 10 trucks with pallets from sea and loose

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cartons arrive. A number of the pallets need to be re-palletized and some of these also need their serial numbers scanned. The cartons also need their serial numbers scanned before the goods can be arrived into WMS and then put in storage. The cycle times (CT) mentioned are the CT per respective unit according to the CCB workload planning tool based on the time one FTE needs to perform the task. Furthermore the lead time is given as well as the projected hands-on time, CT times the number of units per operation. The end result is the sum of the lead time and the CT. In this example the average lead time of this product family is 09:47:15 while the VAT is 01:49:13. Meaning the VAT is only 18,6% of the lead time. In other words, 81,2% of the time (07:58:02) the work necessary stands still somewhere in CCB.

FIGURE 10. VSM INBOUND COMBINATION 0100010101

Due to the great number of product families this report will only show an example of a VSM for an inbound and an outbound product family. Examples can be seen in Appendix D: Inbound Combination 0100010101 VSM and Appendix E: Outbound Combination 000010 VSM.

Ratio VAT vs Lead Time

Below in Table 6 a selection of the results are shown. A summary of the results from the VSM of inbound and outbound are given in Appendix F: Summary VSM Inbound and Appendix G: Summary VSM Outbound. At outbound, the waiting times are exceptionally large for most of the orders.

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TABLE 6. SELECTION OF SUMMARY OF VSM´S Inbound Combination Aver age # ID's Arr Averag e Lead Time Averag e VAT Averag e NVAT Ratio VAT vs Lead Time 0100010100 79,00 10:53:34 09:04:22 01:49:12 80,37% 0000001010 61,00 10:54:42 07:03:47 03:50:55 64,73% 0101010001 58,67 19:49:13 10:49:12 09:00:01 56,60% 0100000100 58,43 11:20:26 06:05:46 05:14:40 58,12% 0100010101 54,71 11:19:48 09:47:15 01:32:33 95,58% 0001010001 47,40 14:06:16 10:40:58 03:25:18 79,15% 0010101001 44,78 12:37:48 10:45:20 01:52:27 86,42%

One of the main reasons for these large waiting times is the amount of work that is released unto the work floor. The capacity and workforce of CCB is being utilized to their best abilities. Thus an

explanation to the relation between VAT and NVAT can be the amount of work in progress (WIP). Currently CCB releases most of the workload of the day unto the floor before midday while working hours are from 06:00 till 22:00. Figure 11 sums the amount of work released and on which times this work is released. It shows that most of the work released onto the work floor is released before the end of the morning shifts. By doing this, they are able to “jump start” some workers on orders which need to be completed later in the day to keep the workers busy. Basically they try to balance the workforce throughout the day because the operators work in shifts and during a shift CCB wishes to keep these operators working. By releasing al of the work at the start of the day, the supervisors are able to mitigate operators between jobs and priority.

FIGURE 11. TIMES WORK RELEASED

Unfortunately, CCB doesn’t keep track of their operators’ efficiency nor do they monitor the utilization of the forklifts. While the abovementioned strategy to keep the operators busy would certainly be effective in achieving that goal. It is unclear what the results are for the utilization of its forklifts.

00:00:00 48:00:00 96:00:00 144:00:00 192:00:00 240:00:00 288:00:00 336:00:00 5: 30: 0 0 6: 15: 0 0 7: 00: 0 0 7: 45: 0 0 8: 30 :0 0 9: 15: 0 0 10 :0 0: 00 10 :4 5: 00 11 :3 0: 00 12 :1 5: 00 13 :0 0: 00 13 :4 5: 00 14 :3 0: 00 15 :15 :00 16 :0 0: 00 16 :4 5: 00 17 :3 0: 00 18 :1 5: 00 19 :0 0: 00 19 :4 5: 00 20 :3 0: 00 21 :1 5: 00 22 :00 :00 Work Inbound Work Outbound Outbound Combination Avera ge # Picks / Route Averag e Lead Time Averag e VAT Averag e NVAT Ratio VAT vs Lead Time 000001 2,50 13:24:40 00:29:51 12:54:49 3,71% 000010 3,48 15:09:36 00:23:43 14:45:52 2,61% 000011 19,19 14:24:17 00:49:48 13:34:29 5,76% 000100 6,39 14:33:01 00:34:36 13:58:25 3,96% 000101 19,35 15:04:10 01:13:13 13:50:57 8,10% 000110 15,33 10:11:26 00:38:15 09:33:10 6,26% 000111 20,00 17:00:35 00:58:02 16:02:33 5,69%

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3.3 Throughput diagram

In chapter 2 it was already hinted that due to the varying costumer demand from CENV, the

necessary in-house forklift capacity varies per day. However, it is unclear how the use of this capacity varies throughout the day. Also, the VSM’s show that most orders have large waiting times. Thus the WIP within CCB should also be large. Operators and supervisors within CCB have a certain freedom that enables them to choose which task they want to execute. As long as the order is finished on time, there is no problem. To investigate the effect of the large waiting times and assumingly also the large WIP and the current order execution strategy, a throughput diagram is constructed and using the theory of Logistic Operation Curves (LOC) the WIP is calculated. To put the amount of WIP and waiting times into perspective, Table 7 summarizes the amount of work and thus operators and forklift capacity needed per day in December 2013.

TABLE 7. WORK IN HOURS PER DAY FOR CCB AND FOR FORKLIFTS

Prod.Date W #FTE.shfts W.FL Cap.FL

2-12-2013 393:57:19 33,00 68:55:01 7,00 3-12-2013 357:43:07 32,00 62:37:59 6,00 4-12-2013 370:16:19 33,00 81:32:01 8,00 5-12-2013 360:02:57 31,00 69:57:16 7,00 6-12-2013 314:35:18 25,00 67:17:07 6,00 9-12-2013 315:52:32 25,00 75:30:59 7,00 10-12-2013 304:50:31 27,00 68:29:16 7,00 11-12-2013 231:31:14 20,00 56:46:21 6,00 12-12-2013 290:16:06 25,00 72:12:25 7,00 13-12-2013 269:27:04 22,00 77:36:12 7,00 16-12-2013 438:45:39 38,00 92:13:17 9,00 17-12-2013 374:56:42 34,00 96:04:20 9,00 18-12-2013 230:48:32 20,00 64:19:30 6,00 19-12-2013 276:00:32 24,00 70:07:09 8,00 20-12-2013 191:44:08 16,00 55:01:13 5,00 23-12-2013 250:03:50 22,00 72:11:01 8,00 24-12-2013 196:02:20 18,00 54:14:24 6,00 27-12-2013 272:41:13 25,00 82:30:17 9,00 30-12-2013 725:02:47 56,00 142:53:07 12,00 31-12-2013 78:15:41 11,00 18:47:15 3,00

Throughput diagram

The work content in time, y-axis, is plotted versus the working hours, x-axis, of both input orders and executed output. Input is the released order at time x and output the completed order at time x. The summation of both input and output is then plotted. A throughput diagram can be helpful to visualize the WIP and the expected average lead time. Figure 12 shows the basic throughput diagram

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the WIP. The horizontal distance between the input curve and the output curve gives an average lead time for orders to complete.

FIGURE 12. THROUGHPUT DIAGRAM ACCORDING TO WIENDAHL (1997)

When applying the throughput diagram to CCB for all orders, inbound, outbound and in-house, Figure 13 and Figure 14 are the result. It shows that the average WIP balances around the 300 hours of work. Considering that the average amount of orders executed per day also equals around 300 hours. This means that the WIP equals an entire workday of orders.

FIGURE 13. THROUGHPUT DIAGRAM CCB 02-12-2013 TILL 05-12-2012

00:00:00 120:00:00 240:00:00 360:00:00 480:00:00 600:00:00 720:00:00 840:00:00 960:00:00 1080:00:00 1200:00:00 In Out WIP(t)

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FIGURE 14. TRHROUGHPUT DIAGRAM CCB DEC-2013

Throughput diagram Forklifts

Assuming the use of forklifts is also an activity, one can derive a throughput diagram for these tasks as well. The result is an average WIP of 17 hours. Meaning at any random time, 17 hours of work still needs to be executed. The average amount of work per day is around 70 hours. Figure 15 shows the plot for the first 3 days of December.

FIGURE 15. THROUGHPUT DIAGRAM FORKLIFTS 02-12-2013 TILL 04-12-2013

Appendix H: Summary of Production data, gives a summary of the production in the month of December 2013. For each workstation, the average workload per order, WIP, waiting times, lead times etc. are calculated. For some operations like those within the inbound unloading operations, the information is not complete due to the lack of information from WMS.

00:00:00 1200:00:00 2400:00:00 3600:00:00 4800:00:00 6000:00:00 In Out WIP(t) 00:00:00 24:00:00 48:00:00 72:00:00 96:00:00 120:00:00 144:00:00 168:00:00 192:00:00 216:00:00

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Utilization of forklifts

Using the output throughput diagram data for the forklifts it is possible to determine when tasks have been fulfilled. This can then give an indication to the utilization of these forklifts on those times. CCB currently has 18 forklifts ready for use. Dividing the forklift workload at a given time interval of 5 minutes by the total available capacity gives an indication of the utilizations. Figure 16 and Figure 17 plot this utilization. It shows that the utilization fluctuates throughout the day. Even though there is a significant queue of work, WIP, the utilization is still not constant. A cause can be the current strategy used to release orders and for operators to select tasks.

FIGURE 16. UTILIZATION FORKLIFTS IN DECEMBER 2013

0 0,1 0,2 0,3 0,4 0,5 0,6 02 -12 -20 13 06: 0 0: 0 0 02 -12 -20 13 17: 0 0: 0 0 03 -12 -20 13 11: 0 0: 0 0 03 -12 -20 13 22 :0 0: 0 0 04 -12 -20 13 16: 0 0: 0 0 05 -12 -20 13 10: 0 0: 0 0 05 -12 -20 13 21: 0 0: 0 0 06 -12 -20 13 15: 0 0: 0 0 09 -12 -20 13 09: 0 0: 0 0 09 -12 -20 13 20: 0 0: 0 0 10 -12 -20 13 14: 0 0: 0 0 11 -12 -20 13 08: 0 0: 0 0 11 -12 -20 13 19: 0 0: 0 0 12 -12 -20 13 13: 0 0: 0 0 13 -12 -20 13 07: 0 0: 0 0 13 -12 -20 13 18: 0 0: 0 0 16 -12 -20 13 12: 0 0: 0 0 17 -12 -20 13 06: 0 0: 0 0 17 -12 -20 13 17: 0 0: 0 0 18 -12 -20 13 11: 0 0: 0 0 18 -12 -20 13 22: 0 0: 0 0 19 -12 -20 13 16: 0 0: 0 0 20 -12 -20 13 10 :0 0: 0 0 20 -12 -20 13 21: 0 0: 0 0 23 -12 -20 13 15: 0 0: 0 0 24 -12 -20 13 09: 0 0: 0 0 24 -12 -20 13 20: 0 0: 0 0 27 -12 -20 13 14: 0 0: 0 0 30 -12 -20 13 08: 0 0: 0 0 30 -12 -20 13 19: 0 0: 0 0 31 -12 -20 13 13: 0 0: 0 0

Minimizing Forklift capacity through Workload Control at Nippon Express NL

Specialization: Production Engineering and Logistics

Report number: 2014.PEL.7862

Title:

Minimizing Forklift capacity through

Workload Control at Nippon Express NL

Minimizing Forklift capacity through Workload Control at Nippon Express NL

Preface

Summary

Summary (Dutch)

List of symbols

General Terms (as index)

Variables

List of abbreviations

Contents

1. Introduction

1.1

Nippon Express Nederland B.V.

1.2

Warehousing

Warehousing by 3PL’s

Balancing between adaptation and general ability for problem solving

1.3

Scope

2. Process Description

2.1

Warehouse Amstelveen

2.2

Processes

2.3

Order Variety

orderlines

Shipped

Pallets

Equivalent

2.4

Capacity

2.5

Nippon Problem Definition

3. System Approach

3.1

Flow model

Inbound

Outbound

In-House

3.2

Value Stream Mapping

Identifying Customer Value

Managing the Value Stream

Value Stream Mapping

Ratio VAT vs Lead Time

3.3

Throughput diagram

Throughput diagram

Throughput diagram Forklifts

Utilization of forklifts

Ut.HT / hour