Improving the supply performance of forage harvesting machines at Lely; Verbeteren van de leverprestatie van graslandmachines bij Lely

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Technology 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 88 pages and 14 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.

Specialization: Transport Engineering and Logistics Report number: 2014.TEL7855

Title: Improving the supply performance of forage harvesting machines at Lely

Author: T.M. Crapts

Title (in Dutch): Verbeteren van de leverprestatie van graslandmachines bij Lely

Assignment: Masters thesis

Confidential: yes (until June 2nd, 2019) Initiator (university): prof.dr.ir. G. Lodewijks

Initiator (company): R. Overweel (Lely Industries nv, Maassluis) Supervisor: dr. ir. H.P.M. Veeke

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Technology Department of Marine and Transport Technology Mekelweg 2 2628 CD Delft the Netherlands Phone +31 (0)15-2782889 Fax +31 (0)15-2781397 www.mtt.tudelft.nl Student: T.M. Crapts

Supervisor: Dr. Ir. H.P.M. Veeke (TU Delft) Prof. dr. Ir. G. Lodewijks Supervisor: R. Overweel

(company)

Assignment type: Master thesis Creditpoints (EC): 35

Specialization: PEL

Report number: 2014.TL.7855 Confidential: Yes

until June 2nd, 2019

Subject: Improving the supply performance of forage harvesting machines at Lely Context

Lely is a manufacturer of high-tech, innovative products aimed at the argiculturual sector. The last decade can been characterized as a period of growth for Lely. During this decade, the product portfolio has been improved and expanded, leading to a wide variety of products. In Maassluis, Lely assembles high quality forest harvesting machines. These machines are primarily used and sold in the spring. This seasonal constraint makes short lead times towards the customer a necessity, while the lead times of parts can be as long as 15 weeks. This constraint combined with a high variety of products has led to a high inventory of both parts and machines. As a result, Lely has a desire for an effective control of the supply of machines that can deal with peak demand in spring and a high variety of products. Problem statement

The supply performance is considered eligible for improvement. First, the on time delivery towards the sales department is considered too low. Furthermore, inventory of both parts and machines is considered too high.

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

Multiple uncertainties within the supply chain of Lely cause problems. For these problems, a redesign of the control of the system is required to effectively supply machines at minimal costs.

Execution

1. Analyze Lely using the Delft Systems Approach 2. Identify problems eligible for improvement

3. Propose a redesign to effectively control the supply of machines 4. Quantify the costs and benefits of the redesign

5. Study relevant literature

The professor, The TU Supervisor,

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Lely is a manufacturer of products aimed at the agricultural sector, such as automated milking robots and forage harvesting machines. About 10% of the total revenue (€567 million in 2012) comes from forage harvesting machines, of which the majority is assembled in a factory in Maassluis, where this research has been conducted. Forage harvesting machines are meant for working grasslands. Because grass grows in the spring, peak demand occurs in the spring. Furthermore, short lead times towards the customer (independent worldwide dealers) are necessary because of the short time window which is optimal for working the grass.

Assembly is divided over several assembly lines, the assembly lines have a capacity ranging from 5 to 50 machines per week. A relatively large number of parts has a lead time longer than three weeks. Because short lead times are required, machines are assembled based on a forecast. Consequently, Lely assembles to stock based on a forecast, this forecast is updated every month and states the expected number of sales per product group per month (there are 29 products groups and over 100 different machines). Historical data is used to determine the expected sales per machine per month. Based on the forecast, a production plan is made, which states the required number of machines to be assembled per week. Procurement is based on this production plan.

Two problems are apparent. First, the on-time delivery (realized production/planned

production per week) is considered too low (on average 85%). About 30% of the productions problems can be contributed to shortages: a part is not in the assembly line when it should have been. There are multiple reasons for shortages, but at least 70% of the shortages occurs when the ERP-system indicated parts were available while in practice, they were not. Based on cycle counting, the inventory record accuracy was found to be <50%, which leads to a great risk of shortages.

Second, the inventory of parts is considered too high. In 2013, there was on average 11 weeks of inventory even though the average throughput time is three weeks. A reason for the high inventory is frequent changing of the planning, due to an on-time delivery <100% or because the demand is different than was forecasted. The demand may be different because within a product group the actual mix may be different than the forecasted mix, since this is based on historical data. A high inventory also increases the risk of shortages, because a lack of storage space (which can also be seen as a surplus of inventory) leads to a relatively low inventory record accuracy, because multiple SKUs are stored in the same location. Research has shown that such parts and locations have a higher prevalence of shortags.

Consequently, the system is eligible for improvement in three ways. First, the system should have a feasible goal. Considering the on-time delivery is always below 100%, the current goal is not feasible. Second, the system needs to be able to deal with demand uncertainty effectively, meaning intervening should be performed based on goals and procedures rather than arbitrary decisions, as is the current situation. Third, transportation of parts that are currently on kitting carts account for 23% of the shortages, usage of line side Kanban storage could improve this.

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In order to have a feasible goal, production planning should be performed by distinguishing planning and scheduling. Planning should be based on historical data to ensure a feasible goal, scheduling should be based on factual data. To implement such a change, two KPI’s are required: the effectiveness (production progress compared with production plan) and efficiency (production progress compared with production schedule). A model has been used to calculate the potential cost savings of planning based on historical data, which was found to be €1.0 million, if such a planning method were to be introduced.

To be able to deal with demand uncertainty in an effective manner, a hybrid lean-agile strategy can be used. For 20% of the SKUs, a lean approach could be used. For those SKUs, the production plan should be based on long term forecasts in order to have a stable production and be cost-effective. When demand for such machines is low, inventory builds up. For the remainder, an agile strategy can be used. Because manual forecasting is performed on a group level based on historical data, the actual demanded SKU mix may be different, even if the forecast on a group level was entirely accurate. Consequently, safety stock for machines needs to be held to be more agile. This reduces the need for changing the planning, which results in lower inventory of parts.

The safety stock level can be determined by three parameters: the desired minimum fill rate (service level L), the time period in which the planning is kept fixed (frozen period F) and the planning cycle length which indicates the batch size (R). A simulation has been performed in order to determine the safety stock level for each machine as a function of those parameters. The results show that without safety stock, the fill rate is 60%. For current feasible

parameters (F=8, R=4) the average inventory value would be €3.0, €4.0 and €5.8 million respectively for L=.90, L=.95 and L=.99, which is less than the average inventory value in 2012 (€6.5 million) and 2013 (€8.9 million). Furthermore, reducing the batch size by decreasing R=4 to R=1 could have an inventory reduction of finished machines of €0.6 million (L=.90) to €1.1 million (L=.99). In conclusion, it is possible to effectively control the supply performance by maintaining safety stock based on a service level without making additional inventory costs.

To improve the inventory record accuracy, line side Kanban storage has been tested. There are three advantages. (1) The bin volume can be chosen per SKU, with kitting carts the volume corresponds to the daily usage for all parts; (2) bins can have location coding for each part, as opposed to kitting carts and (3) the time period for intervening in case of shortage is increased when the bin volume exceeds daily usage. As a test, a single assembly line has been chosen. Two storage racks were available that can hold 40 parts. Extrapolating the logistic handlings based on the actual bin volumes during the test, the logistic handlings can be reduced with 47%, which correlates with about 1.5 FTE. But more importantly, such a replenishment process would reduce the risk of shortages for those parts to 0.

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Summary (Dutch)

Lely is een fabrikant van producten gericht op de landbouwsector, zoals geautomatiseerde melkrobots en graslandmachines. Circa 10% van de totale omzet (€567 miljoen in 2012) komt van graslandmachines, waarvan de meerderheid wordt geassembleerd in Maasluis, alwaar dit onderzoek heeft plaats gevonden. De machines hebben als functie het bewerken van het gras, aangezien het gras groeit in het voorjaar, ontstaat er in dat seizoen een piekvraag. Zodoende zijn korte levertijden naar de klant (onafhankelijke dealers wereldwijd) noodzakelijk, omdat er slechts een kort moment is waarop bewerking van het gras ideaal is. De machines worden geproduceerd middels assemblage, hetgeen is verdeeld over

meerdere assemblagelijnen. De assemblagelijnen hebben een capaciteit van 5 tot 50 machines per week. Een groot deel van de onderdelen heeft levertijden langer dan drie weken. Aangezien korte levertijden vereist zijn, worden machines geassembleerd op basis van een voorspelling. Deze voorspelling wordt maandelijks bijgewerkt, hierin staan de verwachte te verkopen aantallen per productgroep per maand (er zijn 29 productgroepen en meer dan 100 verschillende machines). Op basis van historische data wordt bepaald hoeveel machines er geassembleerd moeten worden binnen elke productgroep. Vervolgens wordt middels deze voorspelling een productieplan gemaakt, waarin staat hoeveel machines per week geassembleerd moeten worden. Dit productieplan wordt gebruikt om onderdelen in te kopen.

Twee belangrijke problemen doen zich voor bij het vervullen van de klantvraag. Allereerst is de on-time delivery (gerealiseerde productie t.o.v. geplande productie) te laag (gemiddeld 85%). Ongeveer 30% van de problemen wordt veroorzaakt door tekorten: een onderdeel is niet aanwezig terwijl het wel benodigd is. Hiervoor zijn meerdere redenen aan te wijzen, maar tenminste 70% van de tekorten doen zich voor wanneer er volgens het ERP-systeem wel onderdelen beschikbaar zouden moeten zijn. Gebaseerd op cycle counts kan de voorraadbetrouwbaarheid worden berekend, op basis van deze berekening blijkt dat de voorraadbetrouwbaarheid lager is dan 50%, waardoor het risico op tekorten groot is.

Ten tweede is de voorraad van onderdelen te hoog. In 2013 was er gemiddeld 11 weken aan onderdelen op voorraad, terwijl de gemiddelde doorlooptijd drie weken is. Een reden hiervoor is het frequent wijzigen van de planning, deze kan gewijzigd worden vanwege een te lage on-time delivery (<100%) of omdat de klantvraag is anders dan verwacht was. De klantvraag kan anders zijn, omdat de daadwerkelijke vraag binnen een productgroep verschilt t.o.v. de voorspelde vraag, aangezien er gebruik wordt gemaakt van historische data om deze mix te bepalen. Een hoge voorraad zorgt ervoor dat het risico op tekorten toeneemt, aangezien een gebrek aan opslagruimte (of een te hoge voorraad) ervoor zorgt dat meerdere onderdelen in één locatie opgeslagen worden. Onderzoek wijst uit dat er op dergelijke locaties relatief veel tekorten ontstaan.

Zodoende kan het system verbeterd worden op drie manieren. Ten eerste moet er een haalbaar doel zijn. Gegeven het feit dat de on-time delivery altijd lager is dan 100%, kan gesteld worden dat het huidige doel niet haalbaar is. Ten tweede moet het systeem om kunnen gaan met vraagonzekerheid, hetgeen betekent dat ingrijpen omdat de vraag anders is dan verwacht moet gebeuren op basis van standaarden en doelen. Ten derde kan het transport van onderdelen die momenteel op kitkarren liggen verbeterd worden door dit te

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vervangen door een Kanban-systeem, aangezien 23% van de tekorten wordt veroorzaakt door onderdelen die op kitkarren liggen.

Om een haalbaar doel te formuleren, moet er onderscheid gemaakt worden tussen planning en scheduling. Een productieplan moet gebaseerd zijn op historische data, om ervoor te zorgen dat een doel haalbaar is, scheduling moet gebaseerd zijn op feitelijke data (zoals tacttijden). Om een dergelijk systeem te kunnen beheersen, zijn twee KPI’s vereist: de effectiviteit (productievoortgang t.o.v. het productieplan) en de efficiëntie (productievoortgang t.o.v. de productieschedule). Een model is gebruikt om te te bereken wat voor

kostenbesparing dit teweeg zou hebben, deze kostenbesparing wordt geschat op €1,0 miljoen.

Omgaan met vraagonzekerheid kan worden bewerkstellig door een hybdrige lean-agile strategie te gebruiken. Voor 20% van de SKUs kan een lean benadering worden gebruikt. De planning kan hierbij gebaseerd worden op lange termijn voorspellingen, om ervoor te zorgen dat er gedurende het hele jaar een stabiele vraag is, waardoor er een focus op

kostenverlaging mogelijk is. Als er geen vraag is, bouwt de voorraad voor dergelijke machines zich op. Voor het overige deel, kan een agile strategie worden toegepast. Aangezien handmatige voorspellingen plaats vinden op productgroep niveau, kan de daadwerkelijke mix binnen een productgroep anders zijn dan verwacht, zelfs als de voorspelling op productgroep niveau volledig correct was. Zodoende moet er

veiligheidsvoorraad voor degelijke machines worden gehanteerd, waardoor de noodzaak tot wijzigen van de planning wordt gereduceerd, hetgeen een voorraadverlaging van de

onderdelen tot gevolg heeft.

Het veiligheidsvoorraadniveau wordt bepaald door drie parameters: de gewenste minimale fill rate (het service level L), de duur van de periode waarin de planning vast staat (de frozen period F) en de duur van de planningscyclus die de batchgrootte bepaalt (R). Een simulatie is uitgevoerd om te bepalen hoe hoog het veiligheidsvoorraadniveau voor elke machine moet zijn voor meerdere combinaties van deze parameters. De resultaten tonen aan dat zonder veiligheidsvoorraad, de fill rate 50% is. Voor de huidige haalbare parameters (F=8, R=4) zou de gemiddelde voorraadwaarde €3,0, €4,0 en €5,8 miljoen zijn voor respectievelijk L=.90, L=.95 en L=.99, hetgeen minder is dan het gemiddelde voorraadniveau in 2012 (€6,5

miljoen) en 2013 (€8,9 miljoen). Tevens zou het reduceren van de batchgrootte (door R=4 te verminderen naar R=1) een verdere voorraadreductie van €1 miljoen kunnen hebben. Hieruit kan geconcludeerd worden dat het mogelijk is om de levering van machines zodanig te beheersen dat een hoog service level wordt behaald zonder dat extra kosten benodigd zijn Ter verbetering van de voorraadbetrouwbaarheid, is een Kanban-systeem getest. Dit heeft drie voordelen: (1) De bingrootte kan gekozen worden per SKU, bij kitkarren, komt het volume overeen met het gebruik per dag; (2) bins kunnen locatiecodering hebben per onderdeel, in tegenstelling tot bij kitkarren en (3) de tijdsduur om in te grijpen wordt vergroot indien er en tekort is geconstateerd als de bingrootte groter is dan het gebruik per dag. Als een test, is een assemblagelijn gekozen met twee kanbanrekken met ruimte voor totaal 40 onderdelen. Op basis van deze test is berekend dat het invoeren van Kanban een potentie heeft om de logistieke handelingen te reduceren met 47%, hetgeen overeenkomt met ongeveer 1,5 FTE. Het belangrijkste is echter dat het risico op lijnstilstand door tekorten in grote mate gereduceerd wordt.

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To fulfill this assignment, an analysis of the production facility be made. Based on these analyses, a definitive problem statement will be formulated. The problem statement will be solved in the chapters afterwards. These solutions are used to form a definitive conclusion on how to solve the problems identified in the problem statement.

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2 Overview of Lely Industries N.V.

2.1 Company profile

In 1948, brothers Cornelis and Arij van der Lely, founded a company called Lely in Maassluis after their invention of the finger wheel rake. In the following years, many more products that reduced physical work in the agricultural sector were introduced. Their mission to enhance the well-being of farmers led the Lely Group to become a multinational company, with

products available in over 60 countries and production facilities in The Netherlands, Germany and the USA. Worldwide, Lely employs 2,000 people of who more than 900 work in The Netherlands. For a brief overview, see Table 2.1.

Item Value

Revenue (2012) €567 million

Employees 2,000 FTE

R&D investment (as a percentage of revenue) 6%

Patents 2,550

R&D departments 7

Production sites 7

Markets 60

Table 2.1: company profile

Currently, Lely’s vision is “a sustainable, profitable and pleasant future in the agricultural sector”. This means men, animals and the environment are important (sustainable), the income of the farmers and other workers in the sector should be increased (profitable) and reliability and ease of use of the Lely machinery should be as good as possible (pleasant). The accompanying mission statement yields: “We inspire people to think of innovative solutions which allow our customers to excel in durable milk production, forage harvesting and energy supply.”

To fulfill their mission statement, Lely is focused on offering a complete portfolio of products and services for dairy farmers. Currently Lely offers products in, amongst others, products for (1) milking, feeding and housing of cows, (2) forage harvesting solutions and (3) energy winning. They have been a market leader for automated milking systems and services for many years. Lely aims to ensure its continuity and further growth by focusing on innovation and high quality products. For example, 6% of the revenue invested in R&D and many

patents have been filed since its establishment. Inheritably, product development is important for Lely, for which two business units are active within the Lely Group: Lely Technologies N.V. (for housing, feeding and milking of cows) and Forage Technology B.V., which is a joint-venture with an American company called Vermeer. As a result, Lely products are sold as Vermeer products in the USA.

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Figure 2.1: Organizational structure of the Lely Group

Forage harvesting machinery are assembled in the production facilities of Lely Industries N.V. Most, but not all, forage harvesting products are produced by in Maassluis, The Netherlands. The forage harvesting products produced in Maassluis are mowers (Dutch: maaiers), tedders (Dutch: schudders), rakes (Dutch: harken) and wrappers (Dutch: wikkelaars). Loaders (Dutch: laders), balers (Dutch: balenpersen) and choppers (Dutch: hakselaars) are produced elsewhere. Several other activities are executed in Maassluis as well, such as the repair of milking robots and the storage of spare parts, but the core activity is the assembly of mowers, tedders, rakes and wrappers.

Figure 2.2 shows the development of the revenue. It can be seen that the previous decade has been an era of growth for Lely. In 2012, the turnover was nearly € 567 million, up more than 400% from 2000. The turnover split between the business units in 2011 was 58% for dairy farms products, 33% for forage harvesting, and 9% for energy winning.

Figure 2.2: revenue of the Lely Group in millions (€). The revenue of 2013 is an estimate.

Figure 2.3: revenue per region

Lely Group Lely Industries Milking, feeding and housing Forage harvesting Energy winning Lely International N.V. Lely Technologies N.V. Forage Technology B.V. 0 100 200 300 400 500 600 Revenue overview 88% 8% 3% 1% Revenue per region

Europe North America

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The forage harvesting machines are sold to independent dealers worldwide who in turn sell their products to farmers. Although Lely operates worldwide, Europe remains the main market. Based on 2011 revenue, about 88% of the revenue was gained in Europe (Figure 2.3).

2.2 Products

In Maassluis forage harvesting products are produced. Forage harvesting is the sequential process of growing grass, mowing grass, drying grass and turning dried grass into silage. Four processes associated with forage harvesting are mowing, raking, tedding and wrapping. For each of these processes a machine is produced in Maassluis.

Mowers cut the grass while rakes divide heaps of cut grass over the field to create an even layer. To prevent the top layer from drying quicker than the grass beneath it, grass has to be tedded. After the grass had dried, wrappers can be used to make bales of silage for storage purposes. An overview of the products with their corresponding names that are made in Maassluis can be seen in Table 2.2. All these products have to be attached to tractors.

Splendimo (mower) Lotus (tedder) Hibiscus (rake) Attis (wrapper)

Splendimo Classic Lotus Stabilo Rotonde Attis PT

Splendimo M Lotus 770 / 900 / 1020 Hibiscus S/P Attis PS

Splendimo MC Lotus 1500 Hibiscus SD

Splendimo F Lotus Combi Hibiscus CD

Splendimo FC Hibiscus CD Profi

Splendimo P Splendimo PC Triplo combinations

Table 2.2: product families produced in Maassluis

Several types of mowers are available: rear mowers (Splendimo Classic and M), front

mowers (Splendimo F), trailed mowers (Splendimo P) and so called Triplo combinations. The rear mowers and trailed mowers are attached to the rear of a tractor; the front mower is attached to the front. A Triplo combination consists of two rear mowers and a front mower. Each Splendimo, Lotus, Hibiscus and Attis is available in slightly different variations, depending on chosen options, working width, power and other criteria. In total, more than 100 different products are produced in Maassluis. Figure 2.4 and Figure 2.5 show a Splendimo, Lotus, Hibiscus and Attis in action in the field.

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Figure 2.4: Left: Splendimo. Right: Lotus.

Figure 2.5: Left: Hibiscus. Right: Attis.

2.3 Assembly and logistics of grassland machinery 2.3.1 Assembly

The assembly of Splendimo, Lotus, Hibiscus and Attis machines is divided over several assembly lines. In general, for assembly three types of parts can be identified: welding parts, gearings and normal parts.

At Lely, no actual production is performed; meaning materials or parts are not subject to any physical transformation. As a result, Lely purchases frame parts from its suppliers, the suppliers produce these parts according to specifications set by Lely. These are called welding parts and are usually steel or cast-iron. Two separate stocks (in two separate halls) are held: one for the normal parts (GO1, Figure 2.6) and one for the other parts (GO2, Figure 2.7). Welding parts can vary in size greatly, they can be very large frame parts or they can be small plates.

Gearings are the drives of the machines and usually contain one or more gearboxes and other welding parts. Gearings are an assembly of multiple welding parts, consisting of

housings for the cutter blades or rake/tedder teeth. These housings are connected through a beam. An axis within this beam is then connected with the housings using a gear connection. Gearings are assembled on separate assembly lines, one for the hayers (Lotus/Hibiscus) and one for the mowers (Splendimo). Attis does not require a gearing.

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The remaining parts are ordinary parts that can be ordered at suppliers and are not produced according to Lely specifications. These parts can be used in the assembly lines directly without any preparing activities, in contrast to the welding parts of gearings.

Figure 2.6: normal parts stored in GO1 Figure 2.7: welding parts stored in GO2

Every welding part that is used, either for the product itself or the gearing, has to be cleaned (sand blasting) and/or painted before it can be used in the assembly line. To distinguish painted and non-painted parts, non-painted parts are called blank parts. Painting is done not just for esthetic reasons, but also to apply a coating to improve durability of the materials. Two painting methods are used: powder coating and wet coating. Wet coating is usually applied to cast-iron parts (the gearings), because powder coating requires heating of the part which could alter its physical properties. Most parts have to be painted red, but yellow or black painting has to be applied to a few parts as well. The yellow color is applied to products sold in the USA. This market is served by a joint-venture called Lely Vermeer, which uses yellow as its main color, whereas Lely uses red.

The gearings are assembled before being painted. Consequently, the required parts are first sand blasted, then assembled into a drive before they are painted by wet coating. In Figure 2.8 several assembled drives for a Lotus 770 can be seen before they are painted.

Transport of the parts from GO1 to the production hall is done by pallet trucks. Transport of parts from GO2 to Multinal, form Multinal to GO2 and from GO2 to the production line is done by forklift trucks. Each production line has its own dedicated forklift truck driver who supplies the parts required for assembly. For communication, every line has four lamps in different colors which can be used to signal different messages. This signaling system is also used to let a forklift truck driver know his assistance is required, for example when parts are required.

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Figure 2.8: assembled Lotus 770 drives, ready for wet coating

Figure 2.9: storage location for painted parts. As the yellow label indicates, this is zone 01.

Painting is not done by Lely itself but by an independent company within the Lely perimeter called Multinal. Thus, Lely has to place an order to get the welding parts painted. Multinal uses a cable-driven trajectory for both wet coating and powder coating. These parts are hung onto a hook and led through the powdering area. Depending on the size of the welding part, the trajectory can operate at several speeds. Wet coating is done in a similar way. After painting, all parts, including the assembled drives, are stored in GO2 in which blank and painted parts are stored separately. Multinal is located within GO2 in between the colored and blank division. An example of storage location for powdered parts is shown in Figure 2.9. Assembly is divided over several assembly lines, in which every assembly line produces usually, but not always, in batches. Each assembly line contains several production cells (two to five). The mechanics work on one product and move or roll the product towards the next cell once they have finalized the standard work which is defined for each cell. Figure 2.10 shows the production line of a Lely Hibiscus with four cells (the yellow markings on the floor indicate a cell). The two cells at the front are in use, but the third is one empty.

Normally, all cells should be in use, so in this case some event happened that disturbed the flow of this particular assembly line.

The end product in a production line is not completely ready for use. Not all parts can be assembled directly because it would make the machine too fragile or difficult to transport. For every machine, a box containing the final parts (to be delivered along with the product) has to be assembled as well. For each Splendimo, Lotus, Hibiscus and Attis an assembly cell for the box is available.

After the assembly is finalized, the products are stored outside on the Lely site (Figure 2.11). Most machines get packed before they are stored outside; some machines are wrapped in plastic, some are packed into wooden boxes and some are not packed at all.

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Figure 2.10: Assembly line of Hibiscus with four cells of which three are in use.

Figure 2.11: storage area for finished machines

2.3.2 Logistics

As stated before, three types of parts can usually be identified. The welding parts are often large and heavy and packaged in large crates. These parts are delivered to the assembly lines by means of forklift trucks. These forklift trucks also remove empty packaging from the assembly lines. The normal parts are transported by means of kitting carts. These carts contain several bins for parts with enough volume for one day.

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The market in which Lely operates has been growing and Lely has been growing along with it. Not only sales have increased, but also the diversity of the products and the

accompanying demand for supplies. Unfortunately, Lely has not always been able to fulfill all customer demand.

Currently, the on time delivery (OTD) is about 80% on average. This means that 20% of the products are not produced in the week they were planned to be produced. Because every day is fully planned in principle, catching up on lost production requires overtime or delaying other production orders. Both solutions are not desirable but sometimes inevitable.

Consequently, Lely aims towards an OTD of 95% or higher which is considered vital for further growth. Examples of problems that form a constraint for production are (amongst others) unreliable inventory records. Furthermore, ‘missed production’ leads to undesirable planning adjustments and increased inventory. The growth of Lely has led the logistic processes to become more important and the current logistic process is assumed to be eligible for improvement.

In conclusion, Lely would like to have analyzed the bottlenecks and accompanying problems that keep the OTD below 95% and consequently forms a constraint for growth in the future.

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3 Analysis of Lely Industries N.V. in Maassluis

Further analysis will be performed by regarding Lely in Maassluis as a system. First, the system has to be defined. Then, the system will be analyzed starting with a black box model and zooming in from there.

3.1 The system Lely Industries N.V.

This research is performed at Lely in Maassluis, where mowers, tedders, rakes and wrappers are produced in order to fulfill customer demand. The system includes the critical

departments for fulfilling this demand. Those two departments are sales (to forecast demand) and the production facility (to fulfill demand).

To analyze the system, a CATWOE-analysis can be performed to identify the relevant parameters that define the system (Appendix C). The results of this analysis are shown in in Table 3.1.

Element Description

Customers dealers, Lely International, Vermeer

Actors Suppliers, employees of Lely, employees of Multinal, sales agents of Lely International, dealers

Transformation Assembly of forage harvesting machines

World view Enhance the well-being of farmers by creating innovative, durable, reliable and easy-to-use products

Owner Lely Group, Lely Industries N.V. and Lely International N.V. Environment

constraints Seasonal effect on customer demand

Table 3.1: CATWOE analysis of Lely

The main conclusion to be drawn from this analysis is the effect the seasonal effect has on the system, since it requires short lead times, which in turn requires machines to be

assembled on demand. Furthermore, an organizational differentiation between sales (Lely International) and production (Lely Industries) is apparent. Based on this analysis, a root definition can be defined which can then be used to analyst the system by means of a black box model. The root definition is as follows:

A system that produces forage harvesting machines with an acceptable lead time and quality so that the Lely International can fulfill the customer demand at minim.

3.2 Black box model

The simplest representation of a system is a black box model which is shown in Figure 3.1. This model represents the transformation of a certain input (materials) to the desired output (forage harvesting machines). The function of this system, the transformation of input into output, is subject to certain requirements. How well this transformation is applied can be observed by measuring the performance. These four elements will be elaborated on further.

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Figure 3.1: black box model

3.2.1 Input

There are two types of supplies required for production: (1) welding parts, (2) normal parts. In total, there are more than 6000 different SKUs. In 2013, purchased supplies were worth in total € 34,408,933 (€753.794 per week (average)). However, as stated before, Lely does not have a steady demand, so there is peak demand. This is reflected in the procurement value per month of supplies as well: during spring, much more supplies are purchased than during other seasons of the year.

Figure 3.2: procurement value per month (total: €30.2 million). The distribution of supply procurement over the year indicates the peak demand during spring.

The lead times are shown in Figure 3.3. In total, there are more than 6000 different item numbers. In the chart below it can be seen that the average lead time of parts is 26 days, whereas almost 30% has a lead time longer than 30 days. Note that the chart below is for all parts for over a 100 different machines, the actual lead time distribution could vary per family or product group.

produce

supplies delivered machines

requirements

 short lead time performance revenue / sales

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Figure 3.3: lead time distribution. Average lead time: 26 days; maximum lead time: 201 days. 71.6% has a lead time less than 30 days.

3.2.2 Output

The output of the system is delivered machines. This means the machines are sold and delivered to the dealer along with additional box parts if necessary. This output consists of Splendimo, Lotus, Hibiscus and Attis machines, which are defined as product families, which is divided into specific item numbers according to a certain hierarchy. In total, 29 product groups can be identified.1 In turn, these product groups are divided over more than 110 different item numbers.

In 2013, approximately 7500 machines were sold and assembled. The weekly assembly data is shown in Figure 3.4. The seasonal effect that has been identified as an environmental constraint is clearly visible: production during the first six months is 5199 units whereas 2450 units have been sold during the last half of the year.

Figure 3.4: assembled machines per month in 2013. In total, approximately 7500 machines were assembled.

1

A diagram with all the product groups is shown in 0

71.6% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0 500 1000 1500 2000 2500 cum u lativ e per ce n tage n u mb er o f parts

lead time [days]

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jan feb mrt apr mei jun jul aug sep okt nov dec

Attis Hibiscus Lotus Splendimo

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However, the above chart merely displays the assembled machines, whereas the output of the system is defined as delivered machines. Consequently, Figure 3.5 offers a comparison between sold (delivered) and assembled machines.

Without zooming in on the system, it can already be observed that machines are not assembled-to-order, because that would mean assembly would not exceed sales.

Consequently, forecasting demand is necessary. In spring of 2013, demand apparently fell short of expectations for three months in a row leading to a surplus of stock, which allowed for higher sales of machines than were assembled in the fall of 2013.

Figure 3.5: number of assembled machines compared with number of sold machine

When the chart above and the procurement value per month are compared at a glance, it is apparent that the peaks are shifted. The total procurement value for 2013 was € 32,336,997 which was used to assemble 7649 machines, leading to an average material cost of €4228 (Table 3.2). Product Family Average material cost [EUR] Average cost price [EUR] Average sales price [EUR] Attis 3,706 4,365 4,208 Hibiscus 7,106 8,295 8,543 Lotus 4,660 5,617 6,354 Splendimo 3,453 4,198 4,965 Overall 4,228 5,083 5,747

Table 3.2: average material price, cost price and sales price

This average allows for a translation of assembled machines into supply cost, which in turn can be compared with the procurement value per month. The results are shown in the chart below (Figure 3.6). This chart shows that the gap between the procurement value and assembled machines value is about one month

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Assembled machines Sold machines

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Figure 3.6: procurement value compared with parts value of assembled machines. It can be seen that there is shift of about one month.

3.2.3 Requirements and performance

Requirements are goals that indicate the expected behavior of a system. For Lely, there are two main actors that dictate this behavior: the customer and the board of directors.

The customer demand as observed in 2013 can be seen in Figure 3.5. It is apparent that peak demand occurs in spring. Furthermore, market conditions dictate that the lead time from Lely to the dealer should be as short as possible during peak season, because farmers cannot afford to wait on their equipment when the grass has already started growing.

Hence, the main requirement for fulfilling this demand is a short lead time to the end customer, which means machines should be able to be delivered from stock.

The performance of the system can thus be evaluated by measurement of how well the requirements have been met. Currently, this is done by measuring revenue, profit and deviation from the budget, which determines if more or less costs than expected have been made to fulfill the expected demand.

3.3 Relation between orders and materials

The black box model is used to analyze overall inputs, outputs, performance and

requirements. However, there is a relation between the demand (order flow) and production (material flow), because machines have to be assembled to stock. Further analysis of the system can be best performed by ‘opening’ the system at a certain aggregation layer, thus opening the black box and identifying these relations. Consequently, the PROPER-model is used for further analysis, in which at least three aspects can be distinguished (Veeke, Ottjes, & Lodewijks, 2007):

1. The order flow, which is the driver for the transformation of supplies into a machine by transforming unhandled customer orders into handled customer orders.

2. The material flow, the transformation of supplies into assembled machines 3. The resources (people and means) that are required for transformation These aspects are represented in Figure 3.7 by means of three flows.

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Procurement value

Parts value of assembled machines

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Figure 3.7: PROPER model of the system. This model distinguishes three separate aspects (orders, materials and resources)

The three flows are accompanied by three parallel transformations. The function control coordinates these transformations by generating executable tasks and assigning resources. Furthermore, it sets standards to ensure fulfilling the requirements. The performance can be evaluated by interpreting the results. Note that the functions are merely functions and not departments or job descriptions, functions can overlap departments and one department or person can fulfill several functions.

3.3.1 Control

Coordinating the order flow, material flow and resource flow is performed by updating the Sales & Operations Planning (S&OP) on a monthly basis. The theoretical principal of

planning in a company using MRP-II is shown Figure 3.8. The S&OP update process will be explained based on this diagram.

assign release resource used resource use handled customer order use customer order task progress supplies finished machine control requirements

 short lead time

performance

 revenue/profit

results standards

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Figure 3.8: planning and scheduling using MRP II

The input for the S&OP update process is a sales forecast made by salesmen. The sales forecast is made by salesmen around the world by specifying the forecasted demand per product group per month. Planning however occurs on a weekly basis on item level, not product group level. Thus, the forecast has to be translated into ‘demand per week per item’. The translation from product group to item number is based on historical sales data, the translation from ‘month’ to ‘week’ is done by dividing the forecasted quantity over the number of weeks for that month. By comparing the forecasted demand with the forecasted available stock, the number of machines to be assembled can be determined. The result is a planning which states the number of machines to be assembled per item number, per week. To ensure feasibility, the quantities to be assembled per week are based on standard tact times. Consequently, there is no differentiation between planning and scheduling: the master planning is the master production schedule.

The production plan has a scope of about 18 months. However, most attention goes out to the coming three months, because almost all lead times of parts are shorter than three months. Furthermore, in order to have the right amount of mechanics available, new mechanics have to be hired at least three months in advance of their first assembly week because of training which takes three months.

No safety stock is held on machines. This means that if the planning perfectly accurate, stock outs of machines may still occur because demand is only specified per month and per

product group.

Based on this planning, a material requirements planning is generated automatically.

Furthermore, a global capacity check is performed by looking upon statistics from the past to check whether the planning is feasible. To ensure meeting the weekly targets, it is

determined how much FTE is required for each production cell and assembly line in order to meet the demand. Consequently, the capacity requirements planning not only involves

master planning global capacity

planning

master production

scheduling global capacity check

material requirements planning

capacity requirements planning

order release

priority control capacity control

OK? OK?

priorities capacities

planning

execution

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calculation of the required FTE, it may also involve a reconfiguration of the assembly line and cells.

In general, there are three types of uncertainties in the S&OP planning:

1. When? Due to the seasonal demand, peak demand is difficult to product. If the grass starts growing earlier than expected, peak demand may occur earlier than expected. 2. What? There are over a 100 different SKUs distributed over various product groups.

Demand within a product group may vary, but demand within product families may vary as well.

3. How many? The overall volume may be hard to predict, because demand is not steady throughout the year.

It is crucial to understand the impact of the seasonal demand. This impact can be better understood with an example, as is shown in Figure 3.9.

Figure 3.9: forecast and sales (for 2012-2013)

Three data series are present: a 12-month forecast trend, a 3-month forecast trend and the actual sales per month. It can be seen that the forecast 12 months in advance is actually better than the forecast 3 months in advance; especially in the beginning of the season the forecast is very optimistic.

The observation that the highest positive forecast errors occurs in the beginning of the year, leads to the preliminary conclusion that salesmen do not wish to risk not having enough machines when the season start. As a result, forecasts are overly optimistic in advance of the season. This leads to build-up of inventory prior to the high season.

3.3.2 Standards and results

The most important standard is the S&OP, because all operations are dependent on this planning. Results that indicate the performance are the sold machines (per time unit), assembled machines (per time unit) and costs made to sell and assemble those machines. From these data points, the revenue, profit and deviation from the yearly set budget can be calculated.

Based on the observed facts so far, a PROPER-model applicable to Lely can be made prior to further analysis of the order, material and resource flow, as is down in Figure 3.10.

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Figure 3.10: PROPER-model for assembly and sales of grassland machinery

3.3.3 Requirements and performance (stratum 1)

Fulfilling customer demand must be accompanied by offering short lead times (1 week) during peak demand. The notion of short lead times is important, since demand is indicated by the sales numbers, which does not give any indication about unfulfilled demand.

The performance is indicated by revenue and sales. Revenue seems directly related to sales numbers, but discounts given in the fall (to sell overproduced machines from the first half of the year) could affect revenue. Another important performance indicator is the deviation of the yearly set budget. Exceeding budget would imply higher cost of resources than expected and could indicate a different efficiency than expected.

3.3.4 Order flow

The order flow is modeled by means of four elements: (1) the function ‘encode’, (2) the buffer OB1 (order buffer 1), (3) the function ‘handle’ and (4) the buffer OB2 (order buffer 2).

The function encode translate the order into either an order that can be handled directly (i.e. the required product is in stock) or into an order that has a lead time because the machine is not in stock, leading to the order having to wait, thus entering OB1. Again, during peak demand the latter should not happen.

Once it is known that a machine is in stock, a task for shipment of the ordered product has to be given. During transport, the order has to wait again, thus entering OB2. Once the machine has been delivered, the order can leave the system.

handled customer order handle customer order control requirements

 short lead time

performance  revenue/profit results  shipped machines  assembled machines  costs standards  planning assign release task progress supplies delivered machine assemble deliver resource used resource use MB2 MB1 OB2 OB1 encode

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20 3.3.5 Material flow

This flow is modeled by means of four elements. MB1 and MB2 are short for ‘material buffer 1’ and ‘material buffer 2’. Supplies enter the system in a buffer by default, because of the internal lead time of 5 days. Consequently, MB1 is the warehouse of the factory.

After being stored in the warehouse, supplies are assembled into machines. These machines are stored outside the production facility. After a machine has been sold, the sales

department, Lely International, gives the signal for transportation of the machines. There are multiple physical locations for MB2. In general, there are four types of storage locations. These are:

1. IN1: the land outside the production facility

2. AU1: warehouse in Australia. Due to the long distance, Australia holds a separate warehouse.

3. IE1: warehouse for the UK and Ireland. 4. Other: mainly showroom models at dealers.

The system boundary is restricted to IN1 since most demand is fulfilled from this stock location.

3.3.6 Resource flow

The resources that have to be assigned to fulfilling the task of assembly are the engineers that assemble the machines and the logistic employees transporting that transport the parts. An overview of the number of assembly engineers per month is shown in the chart below. An increase of flexible workers can be seen during spring, when peak demand occurs. Training of new engineers has duration of about three months. Hence, mid-term planning regarding global capacity should be planned three months in advance at minimum. Furthermore, the the high percentage of flexible contracts allows Lely to absord the peak demand in spring.

Figure 3.11: (absolute) number of fixed and flexible assembly engineers per month

Figure 3.12: percentage of fixed contracts and flexible contracts 0 20 40 60 80 100 120 jan feb mrt ap r me i jun jul _aug _sep ok t no v

fixed contract flexible contract

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% jan feb mrt ap r me i jun jul _aug _sep ok t no v

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21 3.3.7 Inventory analysis

The defined performance indicators for results are sold machines and inventory value. The data regarding sales has already been depicted in Figure 3.5, the data regarding inventory value for MB1 and MB2 is depicted below (Figure 3.13). The average inventory value of machines was €8.9 million in 2013 and €6.5 million in 2012, whereas the parts had an average stock value of €8.3 million (2013). The annual turnover is about €50 million.

Figure 3.13: average inventory value for machines (MB1) and parts (MB2)

Expressing the average inventory value in machines or weeks gives more insight into the size of the inventory. For this, the following figures are required:

 Average material cost per machine = €4,228  Average sales price per machine = €5,747

 Average part turnover per week = 7649*€4,228/52 = €621,923  Average machine turnover per week = 7649* €5,747/52 = €844,626 This leads to the following average inventory:

 [ ] [ ] [ ]  [ ] _{[ ]} [ ] It is difficult to evaluate whether the inventory for machines is too high or too low considering the high number of item numbers (>100) and the seasonal effect on demand. Management however, has stated the inventory is considered too high because cash flow problems have emerged.

For parts, the inventory van be evaluated by using the throughput times. It is clear that if the average throughput time was 1 week, the inventory would only be €0.62 million in case of no disturbances. This is not the case however.

There are three flows: normal parts, gearings and welding parts. The throughput times of each flow can be calculated:

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22 1. Twelding = Tstorage + Tpaint + TWIP

2. Tgearing = Tstorage + TWIP+ Tpaint +Tstorage +TWIP

3. Tnormal = Tstorage + TWIP

The parameters have the following values:

 Tstorage = 1.5 week. Parts have to be available at least one week prior to the start of

the week in which a machine is planned. Suppliers may ship from Monday to Friday, the average of supplies is assumed to be delivered on Wednesday. Consequently, each part is storage 1.5 week prior to the week in which a machine is scheduled.  Tpaint = 1 week. Due to batching for powder coating, it can take up to 5 days for a part

to be painted, so parts have to ordered to be painted 5 days in advance.

 TWIP = 0.5 week. Orders for the upcoming week are released at the start of the week,

so the average throughput time per part is 0.5 week. This leaves the following throughput times:

1. Twelding = 1.5+1+0.5 = 3 weeks

2. Tgearing = 1.5+0.5+1+1.5+0.5 = 5 weeks

3. Tnormal = 1.5+0.5 = 2 weeks days

Figure 3.14: value distribution of flows Figure 3.14 displays the value distribution of flows

Consequently, the average inventory value should be:

(32.3*106/52)*(0.36*2+0.39*3+0.25*5) = €1.95 million

However, the inventory value was well above €1.95 million during the final months of 2013: it’s more than six times higher. Assuming the value distribution of the internal lead times used in the previous calculation is correct, it can be concluded that apparently some inefficiencies are present leading to unnecessary inventory. Three main reasons for such inventory are: (1) a different output than planned upon ordering parts; (2) minimum order quantities and (3) safety stock.

welding 39% gearing 25% normal 36%

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23 In summary

 Lely assembles to stock

 The planning is updated every month

 Peak demand does not form a constraint on capacity but a constraint on forecast accuracy

Identified problems

 Unfulfilled demand is not measured

 The average inventory of parts is more than 4x times too high.  There is no distinction between planning and scheduling

 There is no standard for the duration in which the planning should remain fixed  Controlling machine inventory on an item level per week is not possible because

demand is forecasted is on group level per month

3.4 Order flow

The order flow starts with customers. Customers are independent dealers all over the world. Sales is the responsibility of Lely International N.V., which is the main ‘client’ for the factory. Consequently, machines are produced by Lely Industries while finished machines are owned by Lely International. Note that both organizations fall within the system boundary as defined earlier.

For each customer order (CO), the salesmen first checks whether the ordered number exceeds the available to promise (ATP) number. The ATP is the difference between the number of finished machines that do have a distribution order (DO) and machines that do not have a DO (i.e. claimed and unclaimed machines). If the ATP exceeds the ordered number (enough machines are available), a DO is made for the required machine. Lely International N.V. has ownership over the finished machines.

If the ATP is less than the ordered number, it is checked whether machines with DO and without CO can be claimed. Such machines, not directly linked to a customer, are to be supplied to a distribution center in the United Kingdom, which is the only stock holding location of forage harvesting machines besides the factory in Europe.

After this check, there are two options: either the machine can be delivered right away or the customer has to wait until the ATP exceeds the ordered number after new machines have been produced. This date can be determined by the production planning. This way, backorders are possible.

The order flow is shown in Figure 3.15. First, the order is encoded, which means it is

translated into a customer order for internal usage. Then, it is checked whether the order can be accepted based on the required lead time (‘will the ATP exceed the ordered number on the required delivery date?’). If the order is accepted, either the machine is already

assembled or it is not. If it is, a distribution order is released. If not, a waiting time occurs until the machine is assembled. After the machine has been assembled, the orders may enter a buffer, for instance when the order is finished earlier than required. Once the machines are ready the order has to be finished by arranging transportation and finalizing administrative details (releasing the DO).

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The order flow shows several control functions: initiate, compare, decide and intervene. Initiate is done by the sales department, setting sales targets and other standards for salesmen.

The function ‘compare’ is performed by the planning department within sales which determines the ATP and continuously checks whether the order can be delivered on time. The function ‘decide’ determines the delivery date based upon the ATP and ATP in the future (which, in turn, is based on the production planning). Depending upon the priority of the order, this may lead to ‘last-minute’ changes in the production planning. If necessary, the priority of orders can be changed by adjusting delivery dates by the function intervene which is also performed by production planning.

Figure 3.15: order flow stratum 2

Conclusively, the ATP is the main indicator for sales for order acceptance. In turn, the ATP is determined by the production output, which is determined in the S&O-meeting. As a result, salesmen can determine what to sell based on current and forecasted inventory of machines based on the production planning.

In summary

 Salesmen have insight into the current and upcoming inventory of machines

planning customer order handled customer order encode make CO inititate standards rejected order compare quantity, delivery date decide intervene delivery date evaluate quantity sales numbers, revenue adjusted delivery dates orders with leadtime

ATP, forecasted stock quatities

control

requirements

 short lead time

performance

 revenue/profit

release DO

task progress

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 The planning can be adjusted if necessary to match customer demand, so short term planning control may be necessary

 Backorders are possible and prevalent Identified problems

 Rejected orders are not registered

3.5 Material flow

The material flow transforms supplies into forage harvesting machines that are ready for pick-up, thus forming an essential step in the supply chain. An integrated approach for logistics can be modeled by the SCOR-model. (SSC, 2002) This model identifies four separate functions within the supply chain: plan, source, make and deliver. The latter three functions are required to transform supplies into delivered machines. These functions are depicted in Figure 3.16.

Figure 3.16: functions for the material flow in the PROPER model

standards  planning  BOM supplies delivered machines control requirements

 short lead time

performance  revenue/profit assemble deliver encode MB2 task progress paint MB3 assemble gearing MB1 results  shipped machines  assembled machines  costs determine demand per parts BO M q u a n ti ty p e r w e e k initiate parameters create A&B signals compare stock levels handle A&B signals measure supplier performance

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26 3.5.1 Standards and results

As for the order flow, the main standard is the planning. However, procurement of supplies is dependent on the parameters for each part. This is an important standard because it

determines how supplies are sourced.

An important parameter is the internal lead time, which determines the throughput time. In general, all parts required for assembly need to be ready one week before the machine should be assembled. This is the internal lead time, which is 5 days prior to the start of the week.

Because some parts have to be painted and assembled into gearings, these parts have a longer throughput time. However, gearings are assembled in different lines and are manually planned to be ready before the machines are planned to be assembled. Parts that have to be painted have different item numbers painted and blank. The blank part is then required to be available 5 days prior to when the painted part is required to be available.

Other examples of procurement parameters are safety stock and minimum order quantities, respectively indicating the reorder point and order quantity. However, there is no requirement which indicates how these parameters should be set.

3.5.2 ‘Plan operate’, ‘source’

Just as with the order flow, the S&OP-planning is the main standard. The S&OP planning is entered into Movex, the MRP-system which is used by Lely. This is the function ‘plan operate’. Entering the planning in Movex results in two signals:

1. A&B signals. These are the signals used by the material planners to purchase the required parts (this is the function ‘source’).

2. Planned MOs. This planning is used to schedule on a daily basis and determine when parts have to be painted and when gearings have to be assembled.

As can be seen, the planned MOs are the primary signals for both sourcing and assembly. Changes in this planning thus affect multiple functions. The signals that allow the planning to be changed are the S&OP planning and the finished MOs (i.e. forecasts and realized

production).

The function ‘source’ has the purpose of purchasing the supplies so that they are in stock when the MO is due, because the MO can only be released when the required parts are in stock. Based on the planned MOs, Movex generates A&B-signals (action messages) for the purchasing department. These signals require a certain action to ensure the required amount is delivered on the right date. The required amount and date is determined by the following:

1. Calculated demand to ensure availability of parts for the planned MOs

2. Procurement parameters such as: minimum order quantities, safety stock and internal lead times

The relevant signals for the purchasing department and the required actions are listed in Table 3.3.

The procurement parameters however, are not evaluated. For example, some safety stock is being used, but this is based on estimates rather than calculations or policy. Furthermore,

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minimum order quantities are based on economic order quantities and are not compared with actual usage.

Actions following A and B signals are taken on a daily basis. Supplies are purchased by creating planned purchase orders (PO). These planned POs are released once they are required (the release date). The release date is based on the lead time of the supplier and the safety margin of five days. POs are released to ensure the planned MOs can be released on their due date. Consequently, Lely uses a pull and Just-In-Time strategy for their

supplying activities. In 2012, 700 PO lines were booked per week on average (or 3000 per month on average). Roughly put, one third of these are from Multinal (parts that have to be painted).

Code Required action Example

A1 Release PO outside of lead time Lead time of part A is 20 days. Required delivery date is due in 21 days or less.

B1 Increase lead time of released PO Agreed delivery date of part A is due in 20 days. Required delivery date is due in 19 days or less.

B3 Decrease lead time of released PO Agreed delivery date of part A is due in 20 days. Required delivery date is due in 21 days or more.

B7 Cancel released PO Agreed delivery date of part A is due in 20 days. Delivery is unwanted.

Table 3.3: MRP signals for supply purchasing

It is clear that if the planning was fixed for a period equal or longer than the longest lead time and the function source was not subject to any disturbances, there would only be A1 signals. However, this is not the case in practice because changes in the planning occur all the time, this leads to changes in the yielded demand for parts. Conclusively, the number of B1 and B3 signals would indicate changes in the planning. An overview of these signals with respect to the due date is seen in Figure 3.17.

Figure 3.17: B1 and B3 signals with respect to their need date. Each signal correlates with a PO.

The signals in the chart above are all troublesome. B1 and B3 signals require a lead time shorter than agreed upon with the supplier. Hence, the planning has been changed while it

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was not known for certain whether this was feasible, since it would only be feasible if the supplier would deliver in a lead time shorter than usual. Furthermore, B3 signals would require increased lead times for released PO. If the supplier would decline the request, this could have led to an early delivery, leading to unnecessary inventory.

Knowing 3000 POs are booked monthly and 1000-2000 A1 and B1 signals occur, it can be assumed that the changes in planning are on relatively short term, given the fact that about 30% of the parts have a lead time of more than 30 days. This assumption is supported by the charts in Figure 3.18 and Figure 3.20. The average time shift is about 20 days.

Figure 3.18: average time shift of B1 and B3-signals with respect to initial plan date

Figure 3.19: lead time of B1 and B3-signals. The presence of items with a lead time shorter than 4 weeks indicate short-term planning changes have occurred frequently.

3.5.3 ‘Assemble’

The main function is the transformation of supplies into machines: assembly. The machines are assembled over various assembly lines (twelve in total). Each line contains two or more cells. The tact times per assembly line may vary per machine.

-60 -40 -20 0 20 40 60

aug sep okt nov dec jan feb mrt apr mei jun jul

2012 2013 time shif t B1-signals B3-signals 0 500 1000 1500 2000 2500 0-5 6-10 11 -1 5 16 -2 0 21 -2 5 26 -3 0 31 -3 5 36 -4 0 41 -4 5 46 -5 0 51 -5 5 56 -6 0 61 -6 5 66 -7 0 71 -7 5 76 -8 0 81 -8 5 86 -9 0 91 -9 5 96 -1 00 > 10 0 B1-signals B3-signals

Improving the supply performance of forage harvesting machines at Lely; Verbeteren van de leverprestatie van graslandmachines bij Lely

Summary (Dutch)

Table of contents

2 Overview of Lely Industries N.V.

3 Analysis of Lely Industries N.V. in Maassluis