I just need help with 8, bu there's relevant data listed in 7, and the company data is in the excel attached file.

a. Timbuk2 has seen rapid growth and now outsources its traditional-channel product line. One of the products that it outsources from China is a grey messenger bag. This is a "classic" product for Timbuk2, and has a long product life cycle. Although the demand is uncertain, its mean is pretty stable. This bag is sold through outdoor gear and sports retailers such as Dicks Sporting Goods, REI, Backcountry, and Eastern Mountaineering Sports, bike retailers such as Trek, and e-tailers such as e-bags, Zappos (now owned by Amazon) and Sierra Trading Post. Suppose Timbuk2 customizes the bag for some of 2 these retailers by sewing the retailer logo in addition to the Timbuk2 logo on the bag. Sewing the logo earmarks the bag to a specific retailer, and so essentially there are six different products. (Dicks Sporting Goods and ebags do not offer a monogrammed bag with its logo.) The bag is currently being fully manufactured in China, and all the cost parameters are as in Table 4 of the case. Based on past data analysis, you estimate that the monthly mean demand and the monthly standard deviation of the demand at the six retailers are as given below. The demands across the six retailers are independent of each other. REI Backcountry EMS Zappos Sierra Trading Post Trek Mean 1040.14 698.67 882.56 517.22 435.25 100.14 Std.dev 368.17 308.59 273.92 189.89 205.33 42.39 The production and transportation lead time from China is three months, and Timbuk2 follows a continuous review policy. Its target service level is 99%. Estimate the total safety stock the Timbuk2 will need to keep for this "customized" grey messenger bag. Make any reasonable assumptions if needed. 12 points b. Timbuk2 decides to evaluate a postponement strategy. Since the six bags are essentially the same, a new MBA hire at the firm suggests importing the "vanilla" bag (without the retailer logo) and sewing the logo in San Francisco. (S)he says that this "postponement" strategy could be profitable depending on the cost of sewing the logo in San Francisco. (S)he finds that the increase in cost of sewing the logo in San Francisco, rather than in China is only 20 cents/bag. The cost of holding inventory is $1/bag/month. Should Timbuk2 adopt the postponement strategy? What other considerations would you keep in mind while deciding whether or not to adopt the postponement strategy? (Note that approach is akin to the Dell Direct Sales model that it used for some time.) 12 points 9. In this question, you are given the raw datarather than just the summary mean and standard deviation data as was the case in

Question 8. Also, you are no longer given that the demands across the six retailers are independent. Although I did not specifically show you how to find the safety stock in a situation of this type, I have reviewed the necessary tools to analyze this problem. Suppose Timbuk2 sells the customized grey messenger bag through Macy's, in addition to the six retailers listed in Question 8. Macy's is a larger retailer, with relatively stable sales. The sales data for the last three years is given in Timbuk2.xlsx. Also, the cost of sewing the logo in San Francisco is $0.50/bag. All other data is as discussed in Question 8. Should Timbuk2 adopt a postponement strategy in this case? If yes, why? If not, can you suggest a modified strategy that might be profitable?

Sales Volume (Units). Classic Grey Messenger Bag (Customized)
Month	Period	REI	Backcountry	EMS	Zappos	Sierra Trading Post	Trek	Macy's
Jan-20	1	1075	528	413	836	324	69	7361
Feb-20	2	1758	726	1045	897	339	79	7779
Mar-20	3	891	932	1118	401	0	97	7381
Apr-20	4	908	701	598	651	333	177	8449
May-20	5	1117	243	1052	465	639	106	8964
Jun-20	6	451	1063	683	675	615	30	9255
Jul-20	7	1298	0	947	157	211	141	8657
Aug-20	8	1833	550	968	684	763	104	8278
Sep-20	9	1299	380	1187	544	156	94	8134
Oct-20	10	1002	1386	934	197	465	37	9566
Nov-20	11	1354	714	1053	322	663	186	9001
Dec-20	12	771	518	639	337	591	82	8058
Jan-21	13	1250	341	1173	652	448	66	8037
Feb-21	14	952	664	911	677	954	108	7653
Mar-21	15	583	864	1065	376	488	80	7705
Apr-21	16	1119	595	859	768	241	97	7412
May-21	17	646	687	1079	676	456	105	8135
Jun-21	18	1119	961	707	582	240	29	7359
Jul-21	19	1261	882	801	306	477	128	7969
Aug-21	20	1267	666	740	370	497	123	7480
Sep-21	21	858	373	1143	365	688	112	8435
Oct-21	22	664	1054	647	668	429	102	8533
Nov-21	23	1265	1071	135	500	241	58	7265
Dec-21	24	1636	915	825	543	77	58	8976
Jan-22	25	68	901	1311	363	429	185	8892
Feb-22	26	903	860	841	453	313	122	7788
Mar-22	27	1319	650	966	581	452	122	7126
Apr-22	28	901	612	612	393	461	32	7368
May-22	29	478	1146	350	475	552	136	8253
Jun-22	30	1430	792	1196	338	489	112	8251
Jul-22	31	852	469	1186	393	324	113	7388
Aug-22	32	1274	1	686	620	349	129	6841
Sep-22	33	653	406	1402	995	235	61	8438
Oct-22	34	1099	968	723	507	839	34	8952
Nov-22	35	1143	532	839	379	564	135	8829
Dec-22	36	948	1001	938	474	327	156	7837