advanced supply chain planning lab: expert plan by quin

Advanced Supply Chain Planning Lab: Expert

Planby GROUP N

Sayuri Mancilla - 833874Rosario Meneses - 837412Federico Edoardo Pantanella - 837908Fabio Parisi - 838093Danilo Torretta - 837978

Executive summary

Problem settingGeneral strategyHeuristicDemand orderingDemand anticipationFinal result

GOAL:TOTAL COST MINIMIZATION

Production costsPenalty costs

Plan cost

Operative levers:• Delay deliveries• Produce in advance at most 3 orders• Customer order ranking• Standard time, overtime or extra shift• Alternative routings (internal or

external suppliers)

General

strategy

In-depth analysi

s

Problem setting

Executive summaryProblem settingGeneral strategyHeuristicDemand orderingDemand anticipationFinal result

Heuristic Demand ordering

Demand anticipation

• The available levers can be divided in three operational areas: Heuristic, Demand ordering and Demand anticipation.

• Even though these areas are mutually interrelated, we decided to follow a reference decisional sequence.1. Heuristic: deciding how many resources to

activate, which capacity level to deploy and the maximum delivery delay.

2. Demand ordering: setting the right priorities to efficiently allocate demand orders in the available production capacity for each time bucket.

3. Demand anticipation: deciding 3 orders to produce in advance in order to further improve the cost performance.

General strategy

Cost per hour (€/h)

Laser Cutting

Cutting Bending Assembly Final Assembly

Preferred Resource

50 – 60 - 70

50 – 60 – 70

50 – 60 – 70

50 – 60 – 70

50 – 60 – 120

Internal Alternative

/ 50 – 60 – 70

50 – 60 – 70

50 – 60 – 70

60 – 75 – 120

Contractor (open order)

/ / / 100 100

Contractor (extra)

/ / / 300 300

While making decisions in terms of heuristic management (how many resources to activate and which capacity level to deploy), we decided to focus on just two activities - assembly and final assembly – as production

costs for laser cutting, cutting and bending are not differential.

Heuristic: Production costs Table

Option 1: No extra suppliers

Delay cost = 200*7 = 1400 €Cost of Bianchi’s

capacity = 10*1*100 = 1000

€Total cost = 2400

€

Option 2: Activating extra

suppliers

Cost of Verdi’s capacity = Total

cost = 10*1*300 = 3000 €

Heuristic: How many resources to activate

Lower cost

We chose not to activate extra suppliers, as relying just on suppliers with long term contracts is always cheaper than activating extra suppliers. AN EXAMPLE• Capacity Level = 2• We have to perform final assembly

(LT=1 hour) on a 10-unit order (the average order size is 9.8), but Bianchi has reached full capacity

• With no extra suppliers, the worst case scenario is that we are forced to delay the order and use Bianchi’s capacity (the most expensive) in the following week; the cost of delay is 200 € per day (max delay cost)

• If we activated extra suppliers, we would use Verdi’s capacity

Even the worst case scenario with no extra suppliers is cheaper than activating extra

suppliers

Final Heuristic

63 days of allowed delay ensures that

each and every demand

order is satisfied

Option 1: Level 2

Cost of Bianchi’s capacity = Total

cost = 10*1*100 = 1000 €

Option 2: Level 3

Cost of the internal

alternative’s capacity = Total

cost = 2*1*75+8*1*120

= 1110 €

Heuristic: Which capacity level to deploy

Lower cost

We chose not to activate Level 3, considering that most of scenarios where Saturday overtime is deployed (Level 3) are actually more expensive than scenarios where production takes place 5 days a week, 10 hours a day (Level 2).

AN EXAMPLE• We activated only suppliers with long term

contracts• We have to perform final assembly (LT=1

hour) on a 10-unit order• With no Saturday overtime, Bianchi is the

only resource with enough available capacity

• By activating Level 3, we have available capacity on the internal alternative: we can produce 2 units in the last two hours on Friday and the remaining 8 on Saturday

Activating Level 3 may be convenient when Saturday overtime is used instead of delaying orders,but this specific situation did not occur frequently in the simulations we ran;

therefore, not activating Level 3 leads to a general cost reduction

Capacity Level

Penalty costA-Z

from lowest to highest delay cost per day

Z-A from highest to lowest delay

cost per day

Our rankingPenalty Z-A

Date A-Z

Quantity A-Z

1

3

2

Demand ordering: Available criteria and ranking

Delay Penalty (€/Day)

No. of orders

No. of delayed orders

200 12 0150 4 0140 1 0130 3 0120 4 0100 302 2380 11 250 1 140 1 020 1 1

We selected delay penalty cost as the first criterion in our customer order ranking. In this way, we were able to reduce the probability that an order with a high penalty cost were delivered in delay.

The table aside shows the number of orders for each “delay penalty” category. As the last column underlines, in our solution only low-penalty cost orders have been delivered in delay (those with a penalty of 100€/day or below).

Demand ordering: Penalty cost

In choosing between Date A-Z and Date Z-A, we tried to figure out how this decision affected delay costs. Therefore, we analysed how orders are differently allocated according to these two criteria.

Demand ordering: Delivery date (1/3)

By allocating orders with nearest delivery date first (Date A-Z), we had several delayed orders with a reduction in the average number of delay weeks per order. On the contrary, by allocating orders with farthest delivery date first (Date Z-A), we had fewer delayed orders but an increase in the average number of delay weeks. In the two scenarios, these effects tend to neutralize each other, causing nearly the same delay costs.

After running a few simulations, we saw that, by choosing Date A-Z, we could achieve a slight decrease of delay costs.

In an “infinite capacity” simulation, 2 orders are in overflow on TB 2. Hence, these orders exceeding capacity have to be relocated on the following time

buckets, causing a delay.

AN EXAMPLE• Only 1 resource• Capacity = 30 units in every

time bucket• Orders to be processed have

the same quantity (10 units)• Delivery dates

• 1 order on Time Bucket (TB) 1

• 5 orders on TB 2• 3 orders on TB 3• 2 orders on TB 4• 1 order on TB 5


Date Z-A Date A-Z

Number of delayed orders = 2Average delay weeks per order

= 2.5

Number of delayed orders = 5Average delay weeks per

order = 1


At this stage of allocating demand orders, weekly delay cost and delivery date are the same for each order we consider, as these two criteria come first in our customer order ranking.

Demand ordering: Order quantity (1/2)

Therefore, in choosing between Quantity A-Z and Quantity Z-A, we tried to minimize the number of delayed orders as we knew that it would lead for sure to a decrease in delay costs (as said, at this stage weekly delay cost is a fixed parameter).

We decided to allocate orders with fewest quantity first (Quantity A-Z), as this option allows a smarter distribution over the time buckets, characterized by a lower number of delayed orders.

AN EXAMPLE• Only 1 resource• Capacity = 50 units

in every time bucket• Orders to be

processed • 1 order : 20 units• 4 orders: 10 units• 2 orders: 5 units

• Delay cost = 700 €/week for every order

• Delivery date on Time bucket 1

Number of delayed orders = 1

Total delay cost = 700 €

Number of delayed orders = 3

Total delay cost = 2100 €

Quantity A-Z

Quantity Z-A

Demand ordering: Order quantity (2/2)

Demand Sequence

Plan cost minimization

Production costs

Moving orders from expensive resources

(e.g. external contractors) to more efficient ones (e.g. preferred resource)

Delay costs Producing on time (or reducing delay time)

orders in delay

Demand anticipation: StrategyLow

potential gains

Better option

How to reduce delay

costs?

1. Anticipate orders with the highest total delay cost (= daily penalty * days of delay)

2. Anticipate orders to release production capacity and deliver other critical orders on time or at least with reduced delay

3. A mix of the two! Our strategy!

Demand anticipation: In detail• First we analysed the “infinite capacity” scenario, which showed that the most critical

time bucket is week 25 (20/06): 290 items to deliver.In our “finite capacity” solution, this situation resulted in a huge number of delayed orders from week 26 to week 30.

• Second, we isolated the orders with the highest total delay cost: order 204 (delay cost = 1750€) was produced in week 30 (5 weeks in delay), whereas orders 210, 217 and 219 (delay cost = 1400€ each) in week 27.

Order Delivery Date

Planned Date

Delay(days)

Delay Penalty (€/day)

Total Delay

Cost (€)OV2016020

420/06/2016 25/07/2016 35 50 1750

OV20160210

20/06/2016 04/07/2016 14 100 1400

0V20160217

20/06/2016 04/07/2016 14 100 1400

0V20160219

20/06/2016 04/07/2016 14 100 1400

Demand anticipation: Final decision

• If we had anticipated order 204, we would have set some production capacity free in week 30, but, evidently, this capacity could not have been used to produce any other order on time, since no delayed orders were scheduled in week 31, 32 and 33.

• After running some simulations, we verified that, even though order 204 had the highest total delay cost, by anticipating orders 210, 217 and 219 we could release capacity in week 27 and produce on time (or with lower delay) some delayed orders that were previously allocated in week 28, 29 and 30.

Orders we decided to anticipate

OV20160210 OV20160217

0V20160219

BEFORE . . .

Thanks to the anticipation, the number of delayed orders decreased by 13. This is attributable to two reasons: • the 3 orders we anticipated are no longer in delay;• other 10 delayed orders have been delivered on time

thanks to the released capacity that was previously used to produce the 3 orders anticipated.

Accordingly, delay penalty cost decreased by 35%.Since load cost remained approximately steady, the plan cost is lower.

. . . AFTER

Demand anticipation: Result

Plan cost = 363.556,67 €

Final result

GROUP NSayuri Mancilla - 833874

Rosario Meneses - 837412Federico Edoardo Pantanella -

837908Fabio Parisi - 838093

Danilo Torretta - 837978

THANK YOUFOR YOUR ATTENTION

advanced supply chain planning lab: expert plan by quin

Engineering