Simon decided to start a coffee manufacturing company called Country Brew Coffee (CBC). Coffee is a competitive commodity market, therefore crucial for CBC to improve the performance of their torrefaction process, which is depicted below:
Blending Roasting Grinding & Packing
Ground coffee
Waste
Waste
Waste
The process consists of three steps: blending of different coffee beans followed by roasting of the blend, and finally grinding and packing. CBC plans to have three extremely reliable continuous processing machines for the three steps. To ensure the freshness of coffee, at no point in the process can one build work-in-process inventories. The performance of the individual processing machines is described below.
Step1 Step2 Step3
Blending Roasting Grinding & Packing
Capacity 100 tons/hr 70 tons/hr 60 tons/hr
Yield 90% 80% 85%
Simon seriously doubts his OM skills, but the good news is that he has you available to advise him with the following problems.
A. If 70 tons/hr of coffee beans are introduced to the blending stage, what would be the utilization of each of the three stages? What is the capacity of this plant (in terms of tons/hr)? * [5 points]
To mitigate the high yield losses, there are a number of new technologies that can re-process the losses from the grinding and packing stage. In particular, consider the following two competing process technologies:
Technology 1 can take all the losses from the grinding and packing stage and convert all the waste to ground coffee (the final product). This technology is illustrated below:
Blending Roasting Grinding & Packing
Ground coffee
Waste Waste Reprocessing
Technology 2, on the other hand, takes the losses from the grinding and packing stage and re-introduces them back into that stage, as illustrated below.
Blending Roasting Grinding & Packing
Ground coffee
Waste Waste
Reprocessing
Note: the system is run in a way that both technologies lead to no waste from the Grinding & Packing stage, and there is no work-in-process inventory.
B. If Technology 1 is adopted with a capacity of 10 tons/hr (for the reprocessing stage), what is the new capacity of the plant (in terms of tons/hr)? ** [10 points]
C. If Technology 2 is adopted with a capacity of 15 tons/hour, what is the new capacity of the plant (in terms of tons/hr)? *** [10 points]
2. Not so pop anymore
Nelly founded SA (NSA) Ltd. Today, NSA is a leading South African based chemical distribution and trading company. Their primary product is the chemical polyamide 6 (30% glass fiber reinforced), which is used by their customers for injection moulding purposes. NSA has five distribution centers in South Africa, which supply specific regions of the country, each of these five distribution centers act as strategic business units (SBUs), and are responsible for their individual profits and losses. Since the regions in the country are divided equally between the distribution centers, each of them have an average yearly demand of 120,000 tons of polyamide 6, with an annual standard deviation of 40,000 tons. The price offered for polyamide 6 by the South African market is 1000 Rands per ton of polyamide 6. The chemical polyamide 6 is perishable, and once delivered by NSA to the distribution center, it can only be used by their customers for injection moulding within one month of the date of manufacturing. If it is not sold by the distribution center within one month of delivery, then the degraded chemical is sold for 300 Rands per ton in the open commodity market.
Each of the distribution center places a bulk order one month in advance with the main manufacturing facil- ity, which is located in Johannesburg. The manufacturing facility then sets up the raw material orders with their suppliers, and produces the chemical as close to the end of the month as possible. The chemical is then stored in a special refrigerated cell to maintain its integrity and chemical composition and delivered to the distribution center on the first of each month. The unit cost of manufacturing the chemical in bulk at NSA is
600 Rands per ton, inclusive of transportation costs, which is the cost charged to each distribution center as well.
In addition to the bulk orders, the main manufacturing facilities can also deliver emergency or rush orders to the distribution centers. Nelly has a contract with Harry, who is a third party logistics provider, for delivering these emergency orders. Harry currently charges the distribution centers at the rate of 150 Rands per ton for delivering rush orders. In addition to the 600 Rands per ton unit cost, the main manufacturing facility also charges an order cost of 50 Rands per ton to the distribution center for rush orders, as it would like to encourage the distribution centers to order everything they need per month
in the bulk order, it believes that the distribution centers will do a better job at forecasting demand, if forced to order in bulk.
(a) How many units should the distribution centers order from the Johannesburg facility each month? *[5 points]
(b) What are the total profits of each distribution center? What are the total profits of NSA group from the polyamide 6 chemical? ** [10 points]
(c) Can you identify any forms of waste in this operating model of NSA from the perspective of the distribu- tion centers, or the main facility, or the end customer? What changes would you recommend, and what would be the impact of the changes recommended on NSA’s profits? ***[10 points]
(Hint: This answer should comprise of both qualitative arguments and a quantitative solution.)
3. To pool or not to pool
Votra is a manufacturer of office equipment and furniture and its headquarters is located in Basel, Switzer- land. They have showrooms in multiple locations around Europe, that include Dusseldorf, London, Madrid, Paris, Prague and Brussels. Currently, Votra manages inventory separately in each location, with a small warehouse attached to the showrooms in these respective locations, and profit and loss statements for each of these showrooms are assessed independently. Each location manages the business independently of territory, so managers of showrooms in Dusseldorf could bid for furnishing projects from large organizations in France and Spain as well. Their business process works as follows: their designers design furniture independently with feedback from some key clients, and modify their designs to suit the tastes of different customers.
For instance, their products in the Tham collection are suited for customers who do not prefer a Spartan look to their offices, as the furniture designed by Maddy has forms, materials, colours and finishing that are of a homely quality, creating an aesthetic contrast to a minimalist office. Based on approved designs, a small batch of furniture is manufactured in Basel. The catalog that is based on the designer’s furniture is then updated, and an updated catalog is sent every month to the country managers, with samples for display of newly designed items. If the design is successful, orders are then placed by the individual showrooms for a larger amount of that particular designed item from low-cost producers in Eastern Europe on a monthly basis.
Pat is a member of the business development department in the headquarters, and she recommends that Vitra adopt a more centralized model in their inventory management decisions. She is in favor of getting a third party to run a centralized warehouse in Switzerland for high-volume products, and this party will make decisions on the order quantities of items and volumes to be stocked in the centralized warehouse. She argues that with this model of one centralized warehouse, Voltra will be able to expand more quickly. For this purpose, she has identified a provider, Schindellegi-based third-party logistics provider Kuehne + Nagle. Kuehne + Nagle currently has 22 locations, more than two billion square feet of warehousing, and 760 employees in Europe, with additional growth likely in the next few years.
• Please be concise in your answers.
(a) Evaluate the business proposition of the central warehouse as proposed by Pat. What are the strengths and weaknesses of the central warehouse system compared to their current model? **[10 points]
(b) Can you propose other modes of cooperation between the different country locations to mimic the ben- efits of the central warehouse with the current facilities owned by Votra?**[5 points]
(c) IoT-enabled technology can track the sales data and inventory shipments made to Vitra by their suppliers. IoT sensors allows Vitra to know the location of their orders and shipments at various stages of the supply chain. What are the benefits of IoT to the current model of Voltra and the model proposed by Pat? **[10 points].
Table A: Standardised Normal Probability Table
Z 0.00 -0.01 -0.02 -0.03 -0.04 -0.05 -0.06 -0.07 -0.08 -0.09
-2.9 0.00187 0.00181 0.00175 0.00169 0.00164 0.00159 0.00154 0.00149 0.00144 0.00139
-2.8 0.00256 0.00248 0.00240 0.00233 0.00226 0.00219 0.00212 0.00205 0.00199 0.00193
-2.7 0.00347 0.00336 0.00326 0.00317 0.00307 0.00298 0.00289 0.00280 0.00272 0.00264
-2.6 0.00466 0.00453 0.00440 0.00427 0.00415 0.00402 0.00391 0.00379 0.00368 0.00357
-2.5 0.00621 0.00604 0.00587 0.00570 0.00554 0.00539 0.00523 0.00508 0.00494 0.00480
-2.4 0.00820 0.00798 0.00776 0.00755 0.00734 0.00714 0.00695 0.00676 0.00657 0.00639
-2.3 0.01072 0.01044 0.01017 0.00990 0.00964 0.00939 0.00914 0.00889 0.00866 0.00842
-2.2 0.01390 0.01355 0.01321 0.01287 0.01255 0.01222 0.01191 0.01160 0.01130 0.01101
-2.1 0.01786 0.01743 0.01700 0.01659 0.01618 0.01578 0.01539 0.01500 0.01463 0.01426
-2.0 0.02275 0.02222 0.02169 0.02118 0.02068 0.02018 0.01970 0.01923 0.01876 0.01831
-1.9 0.02872 0.02807 0.02743 0.02680 0.02619 0.02559 0.02500 0.02442 0.02385 0.02330
-1.8 0.03593 0.03515 0.03438 0.03362 0.03288 0.03216 0.03144 0.03074 0.03005 0.02938
-1.7 0.04457 0.04363 0.04272 0.04182 0.04093 0.04006 0.03920 0.03836 0.03754 0.03673
-1.6 0.05480 0.05370 0.05262 0.05155 0.05050 0.04947 0.04846 0.04746 0.04648 0.04551
-1.5 0.06681 0.06552 0.06426 0.06301 0.06178 0.06057 0.05938 0.05821 0.05705 0.05592
-1.4 0.08076 0.07927 0.07780 0.07636 0.07493 0.07353 0.07215 0.07078 0.06944 0.06811
-1.3 0.09680 0.09510 0.09342 0.09176 0.09012 0.08851 0.08691 0.08534 0.08379 0.08226
-1.2 0.11507 0.11314 0.11123 0.10935 0.10749 0.10565 0.10383 0.10204 0.10027 0.09853
-1.1 0.13567 0.13350 0.13136 0.12924 0.12714 0.12507 0.12302 0.12100 0.11900 0.11702
-1.0 0.15866 0.15625 0.15386 0.15151 0.14917 0.14686 0.14457 0.14231 0.14007 0.13786
-0.9 0.18406 0.18141 0.17879 0.17619 0.17361 0.17106 0.16853 0.16602 0.16354 0.16109
-0.8 0.21186 0.20897 0.20611 0.20327 0.20045 0.19766 0.19489 0.19215 0.18943 0.18673
-0.7 0.24196 0.23885 0.23576 0.23270 0.22965 0.22663 0.22363 0.22065 0.21770 0.21476
-0.6 0.27425 0.27093 0.26763 0.26435 0.26109 0.25785 0.25463 0.25143 0.24825 0.24510
-0.5 0.30854 0.30503 0.30153 0.29806 0.29460 0.29116 0.28774 0.28434 0.28096 0.27760
-0.4 0.34458 0.34090 0.33724 0.33360 0.32997 0.32636 0.32276 0.31918 0.31561 0.31207
-0.3 0.38209 0.37828 0.37448 0.37070 0.36693 0.36317 0.35942 0.35569 0.35197 0.34827
-0.2 0.42074 0.41683 0.41294 0.40905 0.40517 0.40129 0.39743 0.39358 0.38974 0.38591
-0.1 0.46017 0.45620 0.45224 0.44828 0.44433 0.44038 0.43644 0.43251 0.42858 0.42465
0.0 0.50000 0.49601 0.49202 0.48803 0.48405 0.48006 0.47608 0.47210 0.46812 0.46414
z 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09
0.0 0.50000 0.50399 0.50798 0.51197 0.51595 0.51994 0.52392 0.52790 0.53188 0.53586
0.1 0.53983 0.54380 0.54776 0.55172 0.55567 0.55962 0.56356 0.56749 0.57142 0.57535
0.2 0.57926 0.58317 0.58706 0.59095 0.59483 0.59871 0.60257 0.60642 0.61026 0.61409
0.3 0.61791 0.62172 0.62552 0.62930 0.63307 0.63683 0.64058 0.64431 0.64803 0.65173
0.4 0.65542 0.65910 0.66276 0.66640 0.67003 0.67364 0.67724 0.68082 0.68439 0.68793
0.5 0.69146 0.69497 0.69847 0.70194 0.70540 0.70884 0.71226 0.71566 0.71904 0.72240
0.6 0.72575 0.72907 0.73237 0.73565 0.73891 0.74215 0.74537 0.74857 0.75175 0.75490
0.7 0.75804 0.76115 0.76424 0.76730 0.77035 0.77337 0.77637 0.77935 0.78230 0.78524
0.8 0.78814 0.79103 0.79389 0.79673 0.79955 0.80234 0.80511 0.80785 0.81057 0.81327
0.9 0.81594 0.81859 0.82121 0.82381 0.82639 0.82894 0.83147 0.83398 0.83646 0.83891
1.0 0.84134 0.84375 0.84614 0.84849 0.85083 0.85314 0.85543 0.85769 0.85993 0.86214
1.1 0.86433 0.86650 0.86864 0.87076 0.87286 0.87493 0.87698 0.87900 0.88100 0.88298
1.2 0.88493 0.88686 0.88877 0.89065 0.89251 0.89435 0.89617 0.89796 0.89973 0.90147
1.3 0.90320 0.90490 0.90658 0.90824 0.90988 0.91149 0.91309 0.91466 0.91621 0.91774
1.4 0.91924 0.92073 0.92220 0.92364 0.92507 0.92647 0.92785 0.92922 0.93056 0.93189
1.5 0.93319 0.93448 0.93574 0.93699 0.93822 0.93943 0.94062 0.94179 0.94295 0.94408
1.6 0.94520 0.94630 0.94738 0.94845 0.94950 0.95053 0.95154 0.95254 0.95352 0.95449
1.7 0.95543 0.95637 0.95728 0.95818 0.95907 0.95994 0.96080 0.96164 0.96246 0.96327
1.8 0.96407 0.96485 0.96562 0.96638 0.96712 0.96784 0.96856 0.96926 0.96995 0.97062
1.9 0.97128 0.97193 0.97257 0.97320 0.97381 0.97441 0.97500 0.97558 0.97615 0.97670
2.0 0.97725 0.97778 0.97831 0.97882 0.97932 0.97982 0.98030 0.98077 0.98124 0.98169
2.1 0.98214 0.98257 0.98300 0.98341 0.98382 0.98422 0.98461 0.98500 0.98537 0.98574
2.2 0.98610 0.98645 0.98679 0.98713 0.98745 0.98778 0.98809 0.98840 0.98870 0.98899
2.3 0.98928 0.98956 0.98983 0.99010 0.99036 0.99061 0.99086 0.99111 0.99134 0.99158
2.4 0.99180 0.99202 0.99224 0.99245 0.99266 0.99286 0.99305 0.99324 0.99343 0.99361
2.5 0.99379 0.99396 0.99413 0.99430 0.99446 0.99461 0.99477 0.99492 0.99506 0.99520
2.6 0.99534 0.99547 0.99560 0.99573 0.99585 0.99598 0.99609 0.99621 0.99632 0.99643
2.7 0.99653 0.99664 0.99674 0.99683 0.99693 0.99702 0.99711 0.99720 0.99728 0.99736
2.8 0.99744 0.99752 0.99760 0.99767 0.99774 0.99781 0.99788 0.99795 0.99801 0.99807
2.9 0.99813 0.99819 0.99825 0.99831 0.99836 0.99841 0.99846 0.99851 0.99856 0.99861
Table B: Standardised Normal Loss Table
z 0.00 -0.01 -0.02 -0.03 -0.04 -0.05 -0.06 -0.07 -0.08 -0.09
-2.9 2.90054 2.91052 2.92051 2.93049 2.94047 2.95046 2.96044 2.97042 2.98041 2.99040
-2.8 2.80076 2.81074 2.82071 2.83069 2.84066 2.85064 2.86062 2.87060 2.88058 2.89056
-2.7 2.70106 2.71103 2.72099 2.73096 2.74093 2.75090 2.76087 2.77084 2.78081 2.79079
-2.6 2.60146 2.61142 2.62137 2.63133 2.64129 2.65125 2.66121 2.67117 2.68113 2.69110
-2.5 2.50200 2.51194 2.52188 2.53183 2.54177 2.55171 2.56166 2.57161 2.58156 2.59151
-2.4 2.40272 2.41264 2.42256 2.43248 2.44241 2.45234 2.46227 2.47220 2.48213 2.49207
-2.3 2.30366 2.31356 2.32345 2.33335 2.34325 2.35316 2.36307 2.37298 2.38289 2.39280
-2.2 2.20489 2.21475 2.22462 2.23449 2.24436 2.25423 2.26411 2.27400 2.28388 2.29377
-2.1 2.10647 2.11629 2.12612 2.13595 2.14579 2.15563 2.16547 2.17532 2.18517 2.19503
-2.0 2.00849 2.01827 2.02805 2.03783 2.04762 2.05742 2.06722 2.07702 2.08683 2.09665
-1.9 1.91105 1.92077 1.93049 1.94022 1.94996 1.95970 1.96945 1.97920 1.98896 1.99872
-1.8 1.81428 1.82392 1.83357 1.84323 1.85290 1.86257 1.87226 1.88195 1.89164 1.90134
-1.7 1.71829 1.72785 1.73742 1.74699 1.75658 1.76617 1.77578 1.78539 1.79501 1.80464
-1.6 1.62324 1.63270 1.64217 1.65165 1.66114 1.67064 1.68015 1.68967 1.69920 1.70874
-1.5 1.52931 1.53865 1.54800 1.55736 1.56674 1.57612 1.58552 1.59494 1.60436 1.61380
-1.4 1.43667 1.44587 1.45508 1.46431 1.47356 1.48281 1.49208 1.50137 1.51067 1.51998
-1.3 1.34553 1.35457 1.36363 1.37270 1.38179 1.39090 1.40002 1.40916 1.41831 1.42748
-1.2 1.25610 1.26496 1.27384 1.28274 1.29165 1.30059 1.30954 1.31851 1.32750 1.33650
-1.1 1.16862 1.17727 1.18595 1.19465 1.20336 1.21210 1.22086 1.22964 1.23844 1.24726
-1.0 1.08332 1.09174 1.10019 1.10866 1.11716 1.12568 1.13422 1.14279 1.15138 1.15999
-0.9 1.00043 1.00860 1.01680 1.02503 1.03328 1.04156 1.04986 1.05819 1.06654 1.07491
-0.8 0.92021 0.92810 0.93603 0.94398 0.95196 0.95997 0.96801 0.97607 0.98417 0.99229
-0.7 0.84288 0.85048 0.85810 0.86576 0.87345 0.88117 0.88892 0.89669 0.90450 0.91234
-0.6 0.76867 0.77595 0.78325 0.79059 0.79797 0.80537 0.81281 0.82028 0.82778 0.83531
-0.5 0.69780 0.70473 0.71170 0.71870 0.72573 0.73281 0.73991 0.74705 0.75422 0.76143
-0.4 0.63044 0.63701 0.64362 0.65027 0.65695 0.66367 0.67042 0.67721 0.68404 0.69090
-0.3 0.56676 0.57296 0.57920 0.58547 0.59178 0.59813 0.60452 0.61094 0.61740 0.62390
-0.2 0.50689 0.51271 0.51856 0.52445 0.53038 0.53634 0.54235 0.54840 0.55448 0.56060
-0.1 0.45094 0.45635 0.46181 0.46731 0.47285 0.47842 0.48404 0.48969 0.49539 0.50112
0.0 0.39894 0.40396 0.40902 0.41412 0.41926 0.42444 0.42966 0.43492 0.44022 0.44556
z 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09
0.0 0.39894 0.39396 0.38902 0.38412 0.37926 0.37444 0.36966 0.36492 0.36022 0.35556
0.1 0.35094 0.34635 0.34181 0.33731 0.33285 0.32842 0.32404 0.31969 0.31539 0.31112
0.2 0.30689 0.30271 0.29856 0.29445 0.29038 0.28634 0.28235 0.27840 0.27448 0.27060
0.3 0.26676 0.26296 0.25920 0.25547 0.25178 0.24813 0.24452 0.24094 0.23740 0.23390
0.4 0.23044 0.22701 0.22362 0.22027 0.21695 0.21367 0.21042 0.20721 0.20404 0.20090
0.5 0.19780 0.19473 0.19170 0.18870 0.18573 0.18281 0.17991 0.17705 0.17422 0.17143
0.6 0.16867 0.16595 0.16325 0.16059 0.15797 0.15537 0.15281 0.15028 0.14778 0.14531
0.7 0.14288 0.14048 0.13810 0.13576 0.13345 0.13117 0.12892 0.12669 0.12450 0.12234
0.8 0.12021 0.11810 0.11603 0.11398 0.11196 0.10997 0.10801 0.10607 0.10417 0.10229
0.9 0.10043 0.09860 0.09680 0.09503 0.09328 0.09156 0.08986 0.08819 0.08654 0.08491
1.0 0.08332 0.08174 0.08019 0.07866 0.07716 0.07568 0.07422 0.07279 0.07138 0.06999
1.1 0.06862 0.06727 0.06595 0.06465 0.06336 0.06210 0.06086 0.05964 0.05844 0.05726
1.2 0.05610 0.05496 0.05384 0.05274 0.05165 0.05059 0.04954 0.04851 0.04750 0.04650
1.3 0.04553 0.04457 0.04363 0.04270 0.04179 0.04090 0.04002 0.03916 0.03831 0.03748
1.4 0.03667 0.03587 0.03508 0.03431 0.03356 0.03281 0.03208 0.03137 0.03067 0.02998
1.5 0.02931 0.02865 0.02800 0.02736 0.02674 0.02612 0.02552 0.02494 0.02436 0.02380
1.6 0.02324 0.02270 0.02217 0.02165 0.02114 0.02064 0.02015 0.01967 0.01920 0.01874
1.7 0.01829 0.01785 0.01742 0.01699 0.01658 0.01617 0.01578 0.01539 0.01501 0.01464
1.8 0.01428 0.01392 0.01357 0.01323 0.01290 0.01257 0.01226 0.01195 0.01164 0.01134
1.9 0.01105 0.01077 0.01049 0.01022 0.00996 0.00970 0.00945 0.00920 0.00896 0.00872
2.0 0.00849 0.00827 0.00805 0.00783 0.00762 0.00742 0.00722 0.00702 0.00683 0.00665
2.1 0.00647 0.00629 0.00612 0.00595 0.00579 0.00563 0.00547 0.00532 0.00517 0.00503
2.2 0.00489 0.00475 0.00462 0.00449 0.00436 0.00423 0.00411 0.00400 0.00388 0.00377
2.3 0.00366 0.00356 0.00345 0.00335 0.00325 0.00316 0.00307 0.00298 0.00289 0.00280
2.4 0.00272 0.00264 0.00256 0.00248 0.00241 0.00234 0.00227 0.00220 0.00213 0.00207
2.5 0.00200 0.00194 0.00188 0.00183 0.00177 0.00171 0.00166 0.00161 0.00156 0.00151
2.6 0.00146 0.00142 0.00137 0.00133 0.00129 0.00125 0.00121 0.00117 0.00113 0.00110
2.7 0.00106 0.00103 0.00099 0.00096 0.00093 0.00090 0.00087 0.00084 0.00081 0.00079
2.8 0.00076 0.00074 0.00071 0.00069 0.00066 0.00064 0.00062 0.00060 0.00058 0.00056
2.9 0.00054 0.00052 0.00051 0.00049 0.00047 0.00046 0.00044 0.00042 0.00041 0.00040
~ End ~
