Capacity-driven production planning

Computers in Industry 113 (2019) 103126

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Capacity-driven production planning Herbert Jodlbauer ∗ , Sonja Strasser Production and Operations Management, University of Applied Sciences Upper Austria, Steyr, Austria

a r t i c l e

i n f o

Article history: Received 8 May 2019 Received in revised form 26 June 2019 Accepted 9 September 2019 Keywords: Production planning Capacity planning Forward scheduling Material requirements planning Dynamic lead times

a b s t r a c t Traditional material requirements planning systems (MRP) schedule production orders for on-demand items without considering limited capacities of production resources. This article introduces a production planning approach which also incorporates consumption-based items and addresses capacity planning when planned orders are generated. The framework consists of three steps: drafted job list generation, capacity-driven forward scheduling and purchase order generation. Fewer predefined parameters for internally produced items than in MRP are necessary. No lot-sizing policy is applied. Lot-sizes are arranged in a forward scheduling procedure. No predefined constant lead times are used because lead times are also calculated dynamically in the planning procedure. The approach allows a (semi-) automatic finitecapacity production planning and capacity adjustment. The functionality of the approach is illustrated by a simple case study throughout the article. © 2019 Elsevier B.V. All rights reserved.

1. Introduction The Internet of Things (IoT), Cyber Physical Systems (CPS) and Smart Connected Things (SCT) are game-changing technologies. In the world of production, new paradigms based on IoT, CPS and SCT have arisen and are known as Smart Factories, Industrie 4.0 or Industrial Internet (Jodlbauer and Schagerl, 2016; Jodlbauer, 2017). These paradigms require automated and integrated mechanisms for production planning and control in order to support the decentralized guided cooperation of Smart Connected Things. Additionally, IoT and Smart Connected Things facilitate better and faster response data of current situations like machine status, scrap rate or available capacity required for accurate planning. Today’s production planning systems are structured hierarchically and are centrally oriented in some way (Fleischmann et al., 2015), so they do not exploit the capabilities of the distributed intelligence provided by IoT, CPS and SCT technologies. The majority of the systems are still based on material and requirement planning (MRP), introduced by Orlicky (1975). The majority of manufacturing companies employ MRP as their main method for material planning (Jonsson and Mattsson, 2006) because of its easily comprehensible algorithm for scheduling production orders and its adaptability to dynamic demand fluctuations. However, weak points of MRP, like the assumption of infinite capacity of produc-

∗ Corresponding author at: Wehrgrabengasse 1-3, 4400, Steyr, Austria. E-mail address: [email protected] (H. Jodlbauer). https://doi.org/10.1016/j.compind.2019.103126 0166-3615/© 2019 Elsevier B.V. All rights reserved.

tion resources and planned lead times, which ignore the workload of the production system, are repeatedly mentioned in the literature (Rossi and Pero, 2011; Sun et al., 2012). This leads to production plans which are usually infeasible and which require additional planning effort at the production control level (Taal and Wortmann, 1997; Pandey et al., 2000; Bakke and Hellberg, 1993). In Rong et al. (2006) a rescheduling approach based on the capacitated lot-sizing problem is presented to determine a feasible solution with capacity limits. Further approaches which try to compensate for these drawbacks have been developed in recent years. A set of different solution approaches (Rossi et al., 2016) are available for the integration of capacity constraints. A possible way is to leave the MRP run unchanged and react to capacity problems afterwards (TavaghofGigloo et al., 2016; Chen et al., 2017), although solving the problems caused at the higher MRP level is a difficult and sometimes impossible task (Taal and Wortmann, 1997). Alternative approaches already start before the MRP run and attempt to avoid capacity violations at the MPS level (Ornek and Cengiz, 2006; Clark, 2003). Some authors formulate optimization problems with capacity constraints instead of the MRP run (Maes and van Wassenhove, 1991; Özdamar ˜ and Yazgac, 1997 or Díaz-Madronero et al., 2014 for a good review). Grubbström and Huynh (2006) extended the MRP approach to a capacity-constrained model by applying Laplace transformation and maximizing net present value. Mathematical programming approaches are often used to solve dynamic production planning problems. Billington et al. (1983) developed mathematical programming approaches to replace the unconstrained MRP by

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a capacity-constrained model. In Buschkühl et al (2010) a good review of dynamic capacitated lot-sizing problems, especially the multi-level capacitated lot-sizing problem (MLCLSP), is presented. These models support finding a cost minimum capacity feasible schedule with fixed available capacity. Unfortunately, leading commercial Enterprise Resource Planning Systems do not apply MLCLSP to production planning. Disadvantages of these approaches are time consuming calculations for real-world planning problems and their theoretical formulations which impede the transfer into practical applications. Approaches which integrate heuristic measures for capacity planning into the well-known MRP run are more likely to be accepted for practical implementation. Different solution approaches which also consider dynamic and work load dependent lead times are presented in Rossi et al. (2016), Billington et al. (1986), Woodruff and Voß (2004) and Jodlbauer and Reitner (2012a). In Jodlbauer and Reitner (2012a) Material and Requirements Planning (MCRP) is introduced, which integrates capacity planning into MRP by providing simple algorithmic measures, like alternative routing, the temporary relaxation of safety stock and lot-size adaption heuristics. Furthermore, dynamic lead times, depending on cumulated capacities, are applied. Most of the planning approaches take only on-demand items into account, but if consumption-based items are planned, then their capacity requirement has to be considered too. Another challenging task in production planning is the optimal setting of planning parameters (Peirleitner et al., 2017) which has a significant influence on the performance of a production system (Gansterer et al., 2014). As a high number of planning parameters makes the optimization more difficult, therefore planning systems with a lower number of parameters are easier to handle in practical implementations. The production planning and control modules of leading Enterprise Resource Planning Systems are based on the MRP II concept, see Rossi, et al. (2016). The MRP-run does not consider capacity restrictions. In real-world applications, human interaction for capacity adjustment, finite-capacity scheduling and job release is required. This paper contributes to (semi-) automation of the planning process transforming the master production schedule into capacity feasible jobs. The goal is to (semi-) automatically find a capacity feasible job list and to (semi-) automatically adjust available capacity. We propose a capacity-driven production planning approach, which can handle internally and externally produced items as well as on-demand and consumption-based planned items. The addressed process starts with the master production schedule and ends with the planned orders for internally produced items and purchase orders for externally produced items. The presented approach is oriented towards planning methods applied in industrial practice to build up trust of practitioners and to facilitate the transformation into commercial Enterprise Resource Planning systems. The proposed method supports the development of automated and integrated mechanisms for production planning and control required by Smart Factories and Industrie 4.0 paradigms. An overview of the whole framework is provided in Section 2. It consists of three hierarchical planning levels (steps): drafted job list generation, customer driven forward scheduling and purchase order generation. The details of these levels are presented in the Sections 2.2,2.3 and 2.4. For a better understanding, a simple case study is introduced in Section 2.1 and used for the illustration of each step throughout the paper. Before stating the concluding remarks in Section 4, the capacity-driven production planning approach is compared to traditional MRP and MCRP as a representative for existing approaches which integrate capacity planning into MRP.

2. Framework for capacity-driven production planning In this section, the basic principles of the proposed approach are presented. Our framework consists of three levels of production planning: drafted job list generation, capacity-driven forward scheduling and purchase order generation (see Fig. 1). In the first step “drafted job list generation”, a first draft of order receipts for both on-demand as well as consumption-based items are determined by applying widespread commercial production planning methods (MRP and reorder point) without taking capacity restriction into account. In the second step “capacity driven forward scheduling”, capacity is adjusted if required and drafted order receipts for internally produced items are adjusted to ensure feasibility due to capacity restrictions. In the last step “purchase order generation”, one traditional MRP-step is performed to yield the purchase orders. Before we elaborate the details of these steps, we provide assumptions and necessary data for a simple case study. The chosen case study is able to demonstrate real-world problems and to illustrate the concept. 2.1. Illustrating case study In our case study, we consider two different end items (final products) A and B. Their bill of material is displayed in Fig. 2. Items A, B, C and D are produced internally, whereas E, G and F are purchased externally. The items A, B, D, E and F are produced or purchased on-demand. The production or purchase of items C and G is consumption-based. In Table 1, the routing data for internally produced items, including processing times and set-up times, are depicted. There are three workstations W1, W2 and W3. Item A has to be produced on workstation W1. Items B, C and D can be produced alternatively on the two workstations W2 and W3 with workstation dependent standard times. Both, on-demand and consumption-based items, are manufactured on the workstations W2 and W3. The standard times displayed represent times in minutes. One time period refers to a working day, consisting of 16 h or 960 min, which corresponds to two shifts per day. In contrast to traditional ERP or MRP systems (Hopp and Spearman, 2011), for items which are produced internally on-demand, no predefined lot-size, safety stock and planned lead time are necessary in this approach. Safety stock is assumed to be zero but due to the forward scheduling applied in our approach, a capacity-driven safety time is introduced. Lead times as well as the lot-sizes are calculated dynamically (see Section 2.3.2). Concerning the current status of the workstations, we assume that W1 is running item A, W2 is set up for item B and W3 for item C, both with a remaining set-up time of 40 min. For consumption-based items (internally produced and externally purchased), as well as for externally purchased on-demand items, additional data has to be specified (see Table 2). The planned lead time, the reorder point and the lot-size are needed for consumption-based items. For items which are externally purchased on-demand, only the planned (replenishment) lead time and the lot-size are necessary. In our case study the lot sizing policies FOQ (fixed order quantity) and FOP (fixed order period) are applied (Jodlbauer and Reitner, 2012b). FOP 3 means that the net requirements of three successive time periods are cumulated into one purchase lot-size (Berry, 1972). Prior to the first step of the proposed approach, some additional input data is needed: gross requirements of the master production schedule for the end items (Table 3), the scheduled receipts (Tables 4 and 5) and the available on-hand inventory for all items (Table 6). Scheduled receipts (SR) are orders which have already been released, but not finished. To ensure good accuracy, produced parts from production orders, which are running at time 0,

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Fig. 1. Capacity-driven Production Planning.

should be booked to the on-hand inventory and only the remaining number of parts, which are not yet finished, should be referred to in the scheduled receipts. Furthermore, two types of scheduled receipts have to be distinguished: First, “SR without consumed subparts” are released orders but their necessary subparts are not yet used by the order and second, “SR with consumed subparts” are released orders, which are not finished yet, but their required subparts have already been consumed by the order. That means, the sub-material is already taken from inventory. Both “SR without consumed subparts” and “SR with consumed subparts” ask for production capacity but only “SR without consumed subparts” asks immediately for sub-material requirement. 2.2. Drafted job list generation When generating a first draft of a job list, we start with the master production schedule (MPS), which contains gross requirements of end items (Table 3), the on-hand inventory (Table 6) and scheduled receipts for all items (Tables 4 and 5). Then we have to distinguish on-demand items and consumption-based items

(Fig. 1). For on-demand items, the steps netting and offsetting are performed, whereas for consumption-based items the reorder point has to be checked and orders are scheduled forward. The goal of the drafted job list is to receive a demand-driven due date sorted job list by using the shortest possible lead time. The drafted due dates are the latest possible due dates to ensure on-time delivery to the customer. They are not directly used for planned order releases or planned orders receipts. The drafted job list will be the input for the capacity planning, capacity adjustment, job assignment to the resources and for the forward scheduling. 2.2.1. Netting and offsetting for on-demand items As is the case with MRP (Hopp and Spearman, 2011), in the netting step, the net requirements are determined by the gross requirements, on-hand inventory and scheduled receipts. However, we do not apply any safety stock for netting. A further difference to the traditional approach is skipping the lot sizing step - or in other words, the lot sizing policy lot for lot (Burcher, 2014) is applied. So the net requirements coincide with the planned order receipts of MRP. In our approach these receipts are not used for

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H. Jodlbauer and S. Strasser / Computers in Industry 113 (2019) 103126 Table 3 Gross requirements of end items. Period

1

2

3

4

5

6

7

A B

100 50

100 40

0 50

200 50

0 0

100 50

100 50

Table 4 SR without consumed subparts. Period

1

A B C D E F G

2

100 50 150

Table 5 SR with consumed subparts.

Fig. 2. Bill of material of the illustrating case study.

the determination of planned order releases, they are only used to generate a drafted job list with latest acceptable due dates, which serves as input for the capacitating step at the next level. In the capacitating step, the job list will be adjusted due to limited capacity, so we call the output of the current step “drafted job list” and the calculated order receipts “drafted order receipts”. In the time phasing step (offsetting), the planned lead time is calculated as the minimum required production order time, which is rounded up to the next whole time period used in the job list calculation (typically one day, one shift or even shorter). The required production order time consists of the standard set-up time and the standard processing time multiplied by the lot-size. No additional lead time is taken into account. The result of the offsetting step is called drafted order release. The time phasing is only needed for the BOM explosion, if at least one subpart is internally produced. Purchased items are not part of the drafted job list because they do not demand internal production capacity. These steps are applied to the case study defined in Section 2.1 and the details are provided in Table 7. We start with the internally produced on-demand items A, B with low-level code 0. With the gross requirements, the scheduled receipts and the on-hand

Period

1

A B C D E F G

100

2

100

Table 6 On-hand inventory. Period

0

A B C D E F G

0 50 300 190 100 170 2500

inventory in period 1 (Tables 3–6), the projected on-hand inventory is calculated first. When the projected on-hand inventory is below zero (no safety stock assumed), a net requirement is triggered which equals the drafted order receipts because lot for lot is applied. The due date of these drafted order receipts corresponds with the period where the lot-size is displayed and is the lat-

Table 1 Routing data for internally produced items. Standard workstation

Item

A B C D

Alternative workstation Work-station

Standard processing time [min]

Standard set up time [min]

0 40 40

W3 W3

14 10

60 60

40

W2

6

60

Order planning

Work-station

Standard processing time [min]

Standard set up time [min]

on-demand on-demand consumptionbased on-demand

W1 W2 W2

8 12 8

W3

5

Table 2 Additional data for externally purchased items and consumption-based items. Item

Production

E F C G

external external internal external

Material planning on-demand consumptionbased

Planned lead time [d] 3 3 4 2

Reorder point

Lot size

250 1500

FOP 3 FOP 3 FOQ 400 FOQ 1500

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Table 7 Drafted job list for internally produced. Item A (on-demand) Period Gross Requirements SR without consumed subparts SR with consumed subparts Projected on-hand inventory Drafted order receipts Lead time Drafted order release Item B (on-demand) Period Gross Requirements SR without consumed subparts SR with consumed subparts Projected on-hand inventory Drafted order receipts Lead time Drafted order release Item D (on-demand) Period Gross Requirements SR without consumed subparts SR with consumed subparts Projected on-hand inventory Drafted order receipts Item C (consumption-based) Period Gross Requirements SR without consumed subparts SR with consumed subparts Projected on-hand inventory Drafted order release Lead time Drafted order receipts

0

50

190

300

1 100 0 100 0

3

100

2 100 0 0 −100 100 1 200

6 100

7 100

100 1 100

100 1

1 50

2 40

3 50

4 50

6 50

7 50

0

50 1 50

50 1

40

−40 40 1 50

50 1

50

50 1 50

1 140 0 0 50 0

2 250 100 100 0 0

3 50

4

5 150

6 150

7

−50 50

0

150

150

0

1 100

2 200

3

4

5 100

6 100

7

200 400 4

0

0

0

300

200

200

est allowed due date of the production order. Then we calculate the shortest possible planned lead times dynamically (depending on the lot-size) and apply them for offsetting (backward scheduling). This results in drafted order releases with the latest allowed start time of the jobs. The drafted order releases are only used for the BOM explosion. If there are scheduled receipts without consumed subparts, these scheduled receipts have to be taken into account in the BOM explosion for the first time period too. Thus, for the first time period the gross requirement of the subparts is triggered by the order releases in the first time periods plus all scheduled receipts without consumed subparts of the parent parts (see Table 12). For all other time periods the gross requirement of the subparts is triggered by the planned order releases of the parent parts. In this way, on-hand inventory and drafted order receipts can be calculated for item D at low-level code 1. As D does not contain any other internally produced items, we can omit the rows “lead time” and “drafted order releases” for this item.

4 200

5

200 2 100 5

400

plus the planned lead time. The lot-size is defined by the fixed order quantity or a multiple of it. In our case study, item C is the only internally produced, consumption-based item. In Table 7 projected on-hand inventory is calculated first. In period 1 it is lower than the reorder point of 250 and so a drafted order release of 400 parts is generated (see Table 2). Applying a lead time of 4 time periods and forward scheduling results in a drafted order receipt in period 5. Now we have calculated drafted order receipts for all internally produced items and drafted order releases as far as they were needed for the BOM explosion. In contrast to traditional MRP, the generated drafted jobs are not used for order release. The drafted jobs, especially their lotsizes and due dates are only used for capacitating, addressed in the next section, and are therefore called drafted order receipts. Both lot-sizes and dates (planned starting and planned completion date) are changed in the next step “capacity driven forward scheduling”. 2.3. Capacity-driven forward scheduling

2.2.2. Consumption-based items Generally, consumption-based items are not taken into account for planning production orders. However, consumption-based items which are internally produced, also require production capacity. Consequently, drafted order receipts have to be adequately calculated for these items. The projected on-hand inventory can be determined in the same way as for on-demand items, taking gross requirements and scheduled receipts into account. When the projected on-hand inventory is below the reorder point, net requirement is triggered (Jacobs, 2011), which we call drafted order release. The involved time period corresponds with the start time of the drafted order. Consumption-based control requires forward scheduling, therefore the due date of the order is equal to start time

At this planning level we check in each time period whether the cumulated available capacity is sufficient at each relevant production resource. If there are capacity problems, different measures are suggested to solve these problems. Then the work load of the generated drafted orders is distributed to time periods using forward scheduling. The result of this planning level is planned order releases and planned order receipts for all internally produced items. 2.3.1. Capacitating The capacity planning is based on the drafted order receipts generated in the drafted job list generation. These order receipts

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H. Jodlbauer and S. Strasser / Computers in Industry 113 (2019) 103126

refer to the latest allowed completion time of the production order. Only items which are internally produced (both on-demand as well as consumption-based items) have to be considered for capacity planning of relevant resources e.g. machines, tools or employee qualification groups. Allocating similar resources to one resource group simplifies the procedure and may be useful. The goal is to provide not too much and not too little capacity. In order to achieve this goal, three steps are performed: 1 Check cumulated capacities 2 Solve capacity problems 3 Reduce available capacity if possible Check cumulated capacities: According to Jodlbauer and Reitner (2012a) or Hübl et al. (2010), no capacity feasible production schedule can be found if the cumulated required capacity is higher than the cumulated available capacity. Thus these capacities have to be calculated in a first step (see Altendorfer et al. (2014)). Table 8 provides the cumulated required and available capacity for the three workstations W1, W2 and W3. The capacity required for an item is determined by the scheduled receipts (orders which are released to the production floor but not produced), the drafted order receipts, their processing times and their set-up times (standard workstations for drafted order receipts and assigned workstation for the scheduled receipts are chosen, possible lot-summarization is not taken into account – this is done in the forward scheduling step). Partial confirmation of produced items is important to ensure realistic capacity requirements caused by scheduled receipts. The sum of required capacity can be interpreted as the required capacity to produce all items. ci,j,t = xi,j,t pi,j + si,j required capacity for itemi in time buckett on workstationj Cj,t =

n 

ci,j,t sum of required capacity in time buckett

i=1

on workstationj CCj,t =

t 

Cj,k cumulated sum of required capacity until time

k=1

buckett on workstationj xi,j,t number of itemsi to be produced in time buckett on workstationj pi,j standard processing time of itemi on workstationj si,j standard set-up time for itemi on workstationj

time period. If the difference between cumulated available capacity minus cumulated required capacity with respect to a time period t ∗ is negative then there is not enough capacity available to ensure on-time completion of all planned orders with due dates before or at time period t ∗ . For example, there is a capacity problem at workstation W2 in period 5 because the cumulated sum of required capacity is 240 min higher than the cumulated sum of available capacity. Solving capacity problems: There are several measures to solve or to reduce such capacity problems (Jodlbauer and Reitner, 2012a; Taal and Wortmann, 1997): • utilization of alternative resources • applying lot summarization to reduce the number of set-ups • adjusting available capacity (e.g. overtime, more staff, changing shift model) • postponing gross requirements in the master production schedule • accepting tardiness leading to backlog and extended delivery times.

The first measure for decreasing required capacity at a temporary bottleneck resource is activating alternative routings. Production orders, planned on the temporary bottleneck resource in overloaded periods, should be planned on alternative resources, if it is possible to unload the temporary bottleneck resource. If the capacity of alternative resources is short, lot splitting with simultaneous processing using alternative resources is suggested. Lot summarization can be useful if there is an essential change overtime and the same item is planned in different orders, all with due dates before or at the time period where the capacity problem occurs (t ∗ ). In this case all drafted orders with due dates before or equal to t ∗ are combined into one new order. Lot summarization can solve or improve the capacity problem, reduces set-up costs but increases inventory. If the first two mentioned measures do not solve the capacity problem, additional capacity (e.g. overtime, more staff, additional shift, etc.) is needed within the allowed and possible capacity limits. In general, this measure should solve the capacity problem or the capacity problem has to be accepted, resulting in postponing gross requirement, tardiness, backlog or extended delivery times. Of course, additional (penalty) costs are thereby incurred. The semi-automatic process of solving capacity problems can be supported by the following algorithm:

aj,t available capacity in time buckett on workstationj Caj,t =

t 

aj,k cumulated sum of available capacity until time

k=1

buckett on workstationj CCj,t ≤ Caj,t for all workstationsj and time bucketst For illustration, the required capacity at workstation W2 for item B in period 2 is triggered by the drafted order receipts of 40 in period 2 (see Table 7). Using standard times of Table 1 results in a required capacity of 40 · 12 + 40 = 520min. This means that the capacity of 520 min has to be used up before or during time period 2 to ensure an on-time completion of the corresponding order. The sum of capacity required is the sum of required capacities of all items which are produced at the considered resource. The cumulated sum of required capacity is the temporal sum of the sum of capacity required. The cumulated sum of available capacity is the temporal sum of available capacity. Assuming one day as a time period and two eight hour shifts a day provide 960 min of available capacity per

In our example, there is one capacity violation on workstation W2 in period 5. By transferring a drafted order receipt for item B with due date 4 from workstation W2 to W3 the capacity can be reduced by 50 · 12 + 40 = 640min and the capacity problem at W2 is solved. Because standard times for B are higher at W3, the required capacity is increased by 50 · 14 + 60 = 760min in period 4 at the alternative workstation, but this does not exceed the available capacity (Table 9). Reduce available capacity: Furthermore, we can see that it is possible to cancel some shifts on all workstations (1 shift =480 min):

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Table 8 Cumulated capacity planning. Workstation W1 Period

1

2

3

4

5

6

7

Required capacity for A Sum of required capacity Cumulated sum of required capacity Cumulated sum of available capacity Difference

800 800 800 960 160

800 800 1600 1920 320

0 0 1600 2880 1280

1600 1600 3200 3840 640

0 0 3200 4800 1600

800 800 4000 5760 1760

800 800 4800 6720 1920

Workstation W2 Period Required capacity B Required capacity C Required capacity D Sum required capacity Cumulated sum of required capacity Cumulated sum of available capacity Difference

1 0 0 0 0 0 960 960

2 520 0 0 520 520 1920 1400

3 640 0 0 640 1160 2880 1720

4 640 0 0 640 1800 3840 2040

5 0 3240 0 3240 5040 4800 −240

6 640 0 0 640 5680 5760 80

7 640 0 0 640 6320 6720 400

Workstation W3 Period Required capacity B Required capacity C Required capacity D Sum required capacity Cumulated sum of required capacity Cumulated sum of available capacity Difference

1 0 0 0 0 0 960 960

2 0 0 1040 1040 1040 1920 880

3 0 0 290 290 1330 2880 1550

4 0 0 0 0 1330 3840 2510

5 0 0 790 790 2120 4800 2680

6 0 0 790 790 2910 5760 2850

7 0 0 0 0 2910 6720 3810

Period

1

2

3

4

5

6

7

Required capacity for A Sum of required capacity Cumulated sum of required capacity Cumulated sum of available capacity Difference

800 800 800 960 160

800 800 1600 1920 320

0 0 1600 2880 1280

1600 1600 3200 3840 640

0 0 3200 4800 1600

800 800 4000 5760 1760

800 800 4800 6720 1920

Workstation W2 Period Required capacity B Required capacity C Required capacity D Sum required capacity Cumulated sum of required capacity Cumulated sum of available capacity Difference

1 0 0 0 0 0 960 960

2 520 0 0 520 520 1920 1400

3 640 0 0 640 1160 2880 1720

4 0 0 0 0 1160 3840 2680

5 0 3240 0 3240 4400 4800 400

6 640 0 0 640 5040 5760 720

7 640 0 0 640 5680 6720 1040

Workstation W3 Period Required capacity B Required capacity C Required capacity D Sum required capacity Cumulated sum of required capacity Cumulated sum of available capacity Difference

1 0 0 0 0 0 960 960

2 0 0 1040 1040 1040 1920 880

3 0 0 290 290 1330 2880 1550

4 760 0 0 760 2090 3840 1750

5 0 0 790 790 2880 4800 1920

6 0 0 790 790 3670 5760 2090

7 0 0 0 0 3670 6720 3050

Table 9 Adjustments in cumulated capacity planning. Workstation W1

• W1: reduce 1 shift in period 3, reduce 2 shifts in period 5 • W2: reduce 1 shift in periods 6, 7 • W3: reduce 1 shift in periods 1, 3, 4, 5, 6, 7

The reduction of the available capacity is important because it minimizes capacity costs while ensuring on-time completion of all orders and ensures shorter lead times and less holding costs (Jodlbauer, 2005a; Jodlbauer and Stöcher, 2006). Details of the lead time determination are presented in Section 2.3.2. The results of this capacitating step are adjusted order receipts for each item and adjusted available capacity for each workstation (Table 10).

2.3.2. Forward scheduling After the successful capacity adjustment, there is sufficient capacity available to ensure on-time completion of all orders. Now, a forward scheduling approach is applied to assign orders to time periods. The idea of forward scheduling is to produce items as early as possible to maximize flexibility to enable the system to handle unexpected disruptions and to maximize service-level restricted to the available capacity. In the step before “capacity reduction”, the goal is to reduce incurred capacity costs, lead times and holding cost. To ensure a feasible schedule we have to start with the capacity requirements of higher low-level codes. All adjusted order receipts are ranked by their completion time. Orders on the same workstation with the same due date are ranked according to pre-

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H. Jodlbauer and S. Strasser / Computers in Industry 113 (2019) 103126

Table 10 Reductions in cumulated capacity planning. Workstation W1 Period

1

2

3

4

5

6

7

Required capacity for A Sum of required capacity Cumulated sum of required capacity Cumulated sum of available capacity Difference

800 800 800 960 160

800 800 1600 1920 320

0 0 1600 2400 800

1600 1600 3200 3360 160

0 0 3200 3360 160

800 800 4000 4320 320

800 800 4800 5280 480

Workstation W2 Period Required capacity B Required capacity C Required capacity D Sum required capacity Cumulated sum of required capacity Cumulated sum of available capacity Difference

1 0 0 0 0 0 960 960

2 520 0 0 520 520 1920 1400

3 640 0 0 640 1160 2880 1720

4 0 0 0 0 1160 3840 2680

5 0 3240 0 3240 4400 4800 400

6 640 0 0 640 5040 5280 240

7 640 0 0 640 5680 5760 80

Workstation W3 Period Required capacity B Required capacity C Required capacity D Sum required capacity Cumulated sum of required capacity Cumulated sum of available capacity Difference

1 0 0 0 0 0 480 480

2 0 0 1040 1040 1040 1440 400

3 0 0 290 290 1330 1920 590

4 380 0 0 380 1710 2400 690

5 0 0 790 790 2500 2880 380

6 0 0 790 790 3290 3360 70

7 0 0 0 0 3290 3840 550

defined local optimization criteria. For instance, considering the optimal changeover sequence to reduce set-up cost, preferring orders for important customers to improve service-level for important customers or preferring items with low holding cost to reduce inventory cost. The following algorithm supports the capacity driven forward scheduling

Table 11 supports the capacity-driven forward scheduling step for each workstation separately. The first two rows “Sum of required capacity” and “Difference” are taken from 10. The row “available capacity” is the available time per period after capacity adjustment (480 min for one shift, 960 min for two shifts). Then the adjusted order receipts and scheduled receipts are pooled for each item at the corresponding workstation. The rows with planned order releases describe the distributed workload (in pieces) of the orders to several periods. The distribution of the workload to several periods is performed by a forward scheduling approach which is detailed in the case study below. The utilization in each period is the ratio of required capacity for production plus set-up time needed to available capacity. The distribution of workloads has to be performed in such a way that the utilization is less or equal to 100% in each period. For illustration, the first steps of the capacity-driven forward scheduling are explained in the presented case study. We start with the first time period. There are only receipts for A at workstation W1, so a planned order of 100 items A is assigned to W1 in this period. Note that W1 is already running A at the beginning and no set-up process is needed (see Section 2.1). We proceed with the second time period, where three orders are due, namely 200 items D at

W3, 40 items B at W2 and 100 items of A at W1. As D is the item with the highest low-level code, it has to be planned first. In two shifts, producing a maximum amount of 88 D is possible in period 1 with set-up taking place, the remaining 112 parts are assigned to time period 2 without set-up. As forward scheduling is applied, 40 parts of B are assigned to W2 in period 1 and 20 parts of A are assigned to W1 in period 1 to exploit the available capacity. The remaining 80 parts of A are assigned to W1 in period 2. In time period 3 there are two jobs: 50 parts of B at W2 and 50 parts of D in W3. Starting with the higher low-level code item D, 50 parts are added to the already planned 112 parts in period 2, so the total amount is now 162, which is still possible without set-up. The order size for B is split into two parts: 36 pieces are assigned to period 1 (in order to achieve the maximum possible output of 76) and 14 pieces are planned in period 2. This forward scheduling procedure is continued until the end of the planning horizon. The final planned order releases and utilizations are displayed in Table 11. Planned order releases in consecutive periods of the same item are summarized to one production lot (framed in bold in Table 11). Planned order releases which need a set-up process before production can start are displayed in bold.

2.4. Purchase order generation For the generation of purchase orders, we apply traditional MRP because there is no demand on internal production capacity. The gross requirements for purchased parts are defined by the planned order receipts of the parent items corrected by the cumulated SR with consumed subparts and the BOM. Application to the case study reveals an interesting effect in period 1 for gross requirements of F (Table 12). F is a subpart of D, which has planned order receipts of 88 in the period. There is a SR with consumed subparts in period 2 for D with 100. These 100 items are already booked out of the inventory (not included in the on-hand) and assigned to the SR. To ensure a correct inventory balance, these 100 items have to be taken into account by reducing the gross requirements 88 by 100 to -12.

H. Jodlbauer and S. Strasser / Computers in Industry 113 (2019) 103126

9

Table 11 Capacity-driven forward scheduling. Workstation W1 Period

1

2

3

4

5

6

7

Sum of required capacity Difference Available capacity Adjusted and scheduled receipts A Planned order releases A Utilization

800 160 960 100 120 100%

800 320 960 100 120 100%

0 1280 480

1600 640 960 200 120 100%

0 1600 0

800 1760 960 100 120 100%

800 1920 960 100 60 50%

1 0 960 960

2 520 1400 960 40

3 640 1720 960 50

4 0 2680 960

5 3240 400 960

6 640 240 480 50

7 640 80 480 50

Workstation W2 Period Sum of required capacity Difference Available capacity Adjusted and scheduled receipts B Adjusted and scheduled receipts C Adjusted and scheduled receipts D Planned order releases B Planned order releases C Planned order releases D Utilization Workstation W3 Period Sum of required capacity Difference Available capacity Adjusted and scheduled receipts B Adjusted and scheduled receipts C Adjusted and scheduled receipts D Planned order releases B Planned order releases C Planned order releases D Utilization

60 50%

0%

400 76

14 94

28

120

32 66

40

120

99%

100%

100%

100%

99%

100%

70%

1 0 480 480

2 1040 400 960

3 290 590 480

4 380 690 480 50

5 790 380 480

6 790 70 480

7 0 550 480

200 6

50 34

150

150

10

162 99%

99%

60 100%

96 100%

96 100%

48 50%

88 100%

Table 12 Purchase order list for on-demand externally sourced items. Item E Period

1

2

3

4

5

6

7

Gross requirement Scheduled receipts Projected on-hand Net requirement Planned order receipts Lead time Planned order release

76 50 74

20 0 54

44

0

32

68

0

10

10

−22 22 90 3

68

5 48

6 0

7 0

82 130 3

48

0

Item F Period Gross requirement Scheduled receipts Projected on-hand Net requirement Planned order receipts Lead time Planned order release

100

90

170

1 −12 0 182

2 162 150 170

3 60

4 192

110

−82

130

3. Comparison to MRP and MCRP In this section some specific characteristics of the introduced capacity-driven production planning approach in comparison to other similar planning systems MRP (Hopp and Spearman, 2011) and MCRP (Jodlbauer and Reitner, 2012a) are emphasized: • As the name suggests, capacity-driven production planning integrates capacitating in the planning approach, whereas orders have to be revised manually after the MRP run to guarantee feasible production plans. • Capacity-driven production planning takes internally produced consumption-based items into account (e.g. item C at W2 in the case study) in order to get a realistic workload distribution. In

other planning approaches, like MRP, only on-demand items are considered. • There is no predefined lot sizing policy in our approach, orders of one material are combined automatically to “production lots”, as long as no other order of a different material is due (e.g. in the case study, item B, workstation W2 in period 1 and 2). This results in set-up savings, which is also used in TOC-based approaches for bottleneck resources (Stein, 1997). • Depending on the sequence, set-up is occurred or not (e.g. workstation W2, item B: no set-up between period 1 and 2, but between period 2 and 5). • In our planning approach, workload intensive orders are distributed on several consecutive periods (e.g. workstation W3, item D, period 1 and 2).

10

H. Jodlbauer and S. Strasser / Computers in Industry 113 (2019) 103126

Table 13 Comparison of different planning approaches. MRP

MCRP

Capacity driven PP

capacity planning material planning

no capacity planning only on-demand items

integrated capacity planning only on-demand items

safety stock

predefined planning parameter

planned lead time lot sizing

product specific constant planned lead time predefined lot sizing policy

predefined planning parameter and methods for safety stock relaxation dynamic lead time

integrated capacity planning on-demand and consumption-based items no safety stock, but dynamic safety time

time phasing

backward scheduling

• Forward scheduling is applied for the assignment of jobs to time periods (not backward scheduling as in traditional MRP). • As a consequence of forward scheduling, excess capacity (utilization lower than 100%) leads to longer capacity-driven lead times and in some way to planned safety lead times. If necessary, a safety stock can be considered for on-demand items leading to earlier drafted order receipts in the first step “drafted job list generation”. The reorder point includes safety stock for consumption-based items to face demand uncertainties. • Lead times are not fixed values, like in MRP, they are calculated dynamically according to order size, set-up and the forward scheduling approach. Note that available capacity influences lead times and reducing available capacity (less excess capacity) shortens these capacity-driven lead times (Jodlbauer, 2005b). • In forward scheduling, the orders are ranked by the due dates. Orders of different items with the same due date are ranked by their low-level code, but in a reverse sequence compared to traditional MRP. Planning starts with items with higher low-level code. If orders have the same due date and the same low-level code, then the planned sequence should take local optimization criteria into account (e.g. optimal sequence of set-up). The main differences of the capacity-driven production planning in comparison to MRP and MCRP are summarized in Table 13. 4. Conclusion In this article a framework for dynamic production planning including capacity planning is introduced. Although the same basic principles (e.g. netting, BOM explosion) are adopted from traditional MRP, there are significant differences which promise a higher degree of automation for the production planning process. Our approach can handle on-demand items, as well as consumptionbased items and considers limited production capacities. Necessary planning parameters are reduced to a minimum amount and so the elaborating process of setting optimal parameters for the production planning system can be omitted. No lot sizing policy, safety stocks and lead time have to be defined in advance for internally produced on-demand items. The lead-times and the lot-sizes are dynamically determined by ensuring feasibility (due dates are met without capacity violations) and minimum incurred cost (lot-summarization reduces set-up cost, capacity reduction reduces labour and holding cost). The proposed approach is able to (semi-) automatically generate capacity-feasible production jobs and purchase orders. Within predefined capacity rules, the algorithm dynamically extends the available capacity to ensure on-time delivery and dynamically reduces the available capacity to reduce capacity costs. Machine assignments are automatically performed by levelling the workload-demand. Human interaction is only required if capacity violations cannot be solved automatically. The introduced planning

predefined lot sizing policy and methods for lot size adaption backward scheduling

dynamic lead time dynamic lot sizes backward and forward scheduling

approach contributes to the (semi-) automation of the production planning process required by Industrie 4.0 and Smart Production paradigms. The Internet of Things supports faster and more accurate data acquisition which is required for the proposed planning method. The introduced production planning approach supports the implementation of industry 4.0 paradigms (such as autonomy or decision making without human interaction) and requires industry 4.0 tools (such as new data acquisition methods). The presented approach is applied to a simple example to illustrate the ideas and the concept in this article. In the considered example, a multi-stage, multi-item production system with ondemand as well as consumption-based items and with alternative routings is referred to. Standard processing times and standard set-up times are modelled with respect to the item and the workstation. Available capacity is, to a certain extent, a decision variable (for example overtime, more or fewer shifts are needed or not). The approach may need to be extended or adapted for more complex production system environments. In the case of outsourced production steps, a predefined fixed planned lead time has to be introduced. In the case of alternative materials or parallel workstations, additional possibilities to (semi)automatically solve capacity feasibility problems can be developed. Such extensions are planned to be addressed in further research. Additionally, we plan to implement this capacity-driven production planning approach in a simulation software in order to test more complex scenarios in order to be able to learn more about the ability to automate the production planning under predefined capacity rules and compare the performance to other planning frameworks like MRP or MCRP.

Declaration of Competing Interest None.

Acknowledgements The authors gratefully acknowledge financial support with the project “Smart Factory Lab”, which is funded by the European Fund for regional development (EFRE) and the regional government of Upper Austria as part of the program “Investing in Growth and Jobs 2014-2020”.

References Altendorfer, K., Hübl, A., Jodlbauer, H., 2014. Periodical capacity setting methods for make-to-order multi-machine production systems. International Journal of Production Research 52 (16), 4768–4784, http://dx.doi.org/10.1080/00207543. 2014.886822. Bakke, N.A., Hellberg, R., 1993. The challenges of capacity planning. International Journal of Production Economics 30-31, 243–264, http://dx.doi.org/10.1016/ 0925-5273(93)90096-4. Berry, W.L., 1972. Lot Sizing Procedures for Requirements Planning Systems: A Framework for Analysis. Production and Inventory Management 13 (2).

H. Jodlbauer and S. Strasser / Computers in Industry 113 (2019) 103126 Billington, P.J., McClain, J.O., Thomas, L.J., 1983. Mathematical programming approaches to capacity-constrained MRP systems: Review, formulation and problem reduction. Management Science 29 (10), 1126–1141. Billington, P.J., McClain, J.O., Thomas, L.J., 1986. Heuristics for Multilevel Lot-Sizing with a Bottleneck. Management Science 32 (8), 989–1006, http://dx.doi.org/10. 1287/mnsc.32.8.989. Burcher, P., 2014. Lot Sizing in MRP. In: Cooper, C.L. (Ed.), Wiley encyclopedia of management. WILEY, Chichester, West Sussex, United Kingdom, pp. 1–2. Buschkühl, L., Sahling, F., Helber, S., Tempelmeier, H., 2010. Dynamic capacitated lotsizing problems: a classification and review of solution approaches. OR Spectrum 32 (2), 231–261. Chen, J.C., Chen, T.-L., Harianto, H., 2017. Capacity planning for packaging industry. Journal of Manufacturing Systems 42, 153–169, http://dx.doi.org/10.1016/ j.jmsy.2016.12.007. Clark, A.R., 2003. Optimization approximations for capacity constrained material requirements planning. International Journal of Production Economics 84 (2), 115–131, http://dx.doi.org/10.1016/S0925-5273(02)00400-0. ˜ M., Mula, J., Peidro, D., 2014. A review of discrete-time optimizaDíaz-Madronero, tion models for tactical production planning. International Journal of Production Research 52 (17), 5171–5205. Fleischmann, B., Meyr, H., Wagner, M., 2015. Advanced Planning. In: Stadtler, H., Kilger, C., Meyr, H. (Eds.), Supply chain management and advanced planning. Concepts, models, software and case studies. Springer, Berlin, pp. 71–95. Gansterer, M., Almeder, C., Hartl, R.F., 2014. Simulation-based optimization methods for setting production planning parameters. International Journal of Production Economics 151, 206–213. Grubbström, R.W., Huynh, T.T.T., 2006. Multi-level, multi-stage capacityconstrained production–inventory systems in discrete time with non-zero lead times using MRP theory. International Journal of Production Economics 101 (1), 53–62. Hopp, W.J., Spearman, M.L., 2011. Factory physics. Long Grove (IIlinois), Waveland. Hübl, A., Altendorfer, K., Jodlbauer, H., Pilstl, J. (Eds.), 2010. Customer Driven Capacity Setting. Advances in Production Management Systems. New Challenges, New Approaches. Springer, Berlin Heidelberg. Jacobs, F.R., 2011. Manufacturing planning and control for supply chain management. McGraw-Hill Professional, New York, London: McGraw-Hill [distributor]. Jodlbauer, H., 2005a. Definition and properties of the input-weighted average leadtime. European Journal of Operational Research 164 (2), 354–357. Jodlbauer, H., 2005b. Range, work in progress and utilization. International Journal of Production Research 43 (22), 4771–4786, http://dx.doi.org/10.1080/ 00207540500137555. Jodlbauer, H., 2017. Digitale Transformation der Wertschöpfung. Kohlhammer., Stuttgart. Jodlbauer, H., Reitner, S., 2012a. Material and capacity requirements planning with dynamic lead times. International Journal of Production Research 50 (16), 4477–4492, http://dx.doi.org/10.1080/00207543.2011.603707. Jodlbauer, H., Reitner, S., 2012b. Optimizing service-level and relevant cost for a stochastic multi-item cyclic production system. International Journal of Production Economics 136 (2), 306–317, http://dx.doi.org/10.1016/j.ijpe.2011.12. 015.

11

Jodlbauer, H., Schagerl, M., 2016. Reifegradmodell Industrie 4.0. Ein Vorgehensmodell zur Identifikation von Industrie 4.0 Potentialen. In: Mayr, H.C., Pinzger, M. (Eds.), INFORMATIK 2016. Lecture Notes in Informatics, Bonn. Jodlbauer, H., Stöcher, W., 2006. Little’s Law in a continuous setting. International Journal of Production Economics 103 (1), 10–16. Jonsson, P., Mattsson, S., 2006. A longitudinal study of material planning applications in manufacturing companies. International Journal of Operations & Production Management 26 (9-10), 971–995, http://dx.doi.org/10.1108/ 01443570610682599. Maes, J., van Wassenhove, L.N., 1991. Capacitated dynamic lotsizing heuristics for serial systems. International Journal of Production Research 29 (6). Orlicky, J., 1975. Material requirements planning. The new way of life in production and inventory management. McGraw-Hill, New York. Ornek, A.M., Cengiz, O., 2006. Capacitated lot sizing with alternative routings and overtime decisions. International Journal of Production Research 44 (24), 5363–5389, http://dx.doi.org/10.1080/00207540600600106. Özdamar, L., Yazgac, T., 1997. Capacity driven due date settings in make-to-order production systems. International Journal of Production Economics 49 (1), 29–44. Pandey, P.C., Yenradee, P., Archariyapruek, S., 2000. A finite capacity material requirements planning system. Production Planning & Control 11 (2), 113–121, http://dx.doi.org/10.1080/095372800232315. Peirleitner, A.J., Altendorfer, K., Felberbauer, T., 2017. Simulation based manufacturing system improvement focusing on capacity and mrp decisions — A practical case from mechanical engineering. Proceedings of the 2017 Wintersimulation Conference, 3876–3887. Rong, C., Takahashi, K., Morikawa, K., 2006. MRP rescheduling heuristics with capacity extension under deterministic demand. Computers & Industrial Engineering 51 (1), 2–13. Rossi, T., Pero, M., 2011. A simulation-based finite capacity MRP procedure not depending on lead time estimation. International Journal of Operational Research 11 (3), 237, http://dx.doi.org/10.1504/IJOR.2011.041343. Rossi, T., Pozzi, R., Pero, M., Cigolini, R., 2016. Improving production planning through finite-capacity MRP. International Journal of Production Research 55 (2), 377–391, http://dx.doi.org/10.1080/00207543.2016.1177235. Stein, R.E., 1997. The Theory of Constraints. Applications in Quality Manufacturing, second edition. Taylor & Francis. Sun, L., Heragu, S.S., Chen, L., Spearman, M.L., 2012. Comparing dynamic risk-based scheduling methods with MRP via simulation. International Journal of Production Research 50 (4), 921–937, http://dx.doi.org/10.1080/00207543.2011. 556152. Taal, M., Wortmann, J.C., 1997. Integrating MRP and finite capacity planning. Production Planning & Control 8 (3), 245–254, http://dx.doi.org/10.1080/ 095372897235307. Tavaghof-Gigloo, D., Minner, S., Silbermayr, L., 2016. Mixed integer linear programming formulation for flexibility instruments in capacity planning problems. Computers & Industrial Engineering 97, 101–110, http://dx.doi.org/10.1016/j. cie.2016.04.013. Woodruff, D.L., Voß, S., 2004. A Model for Multi-Stage Production Planning with Load Dependent Lead Times. System Sciences, 88–96.