Question

Ken Wilson had done it all at Wilson Manufacturing, a company founded by his grandfather 63 years ago. Among his many duties, Ken oversaw all the plant’s operations, a task that had grown in responsibility given the company’s rapid growth over the past three decades. When Ken’s grandfather founded the company, there were only two manufacturing sites. Expansion and acquisition of competitors over the years had caused that number to grow to over 50 manufacturing plants in 18 states. Wilson Manufacturing had a simple process the produced only two products, but the demand for these products was strong and Ken had spent millions of dollars upgrading his facilities over the past decade. Consequently, most of the company’s equipment was less than 10 years old on average. Wilson’s two products were produced for local markets, as prohibitive shipping costs prevented shipping the product long distances. Product demand was sufficiently strong to support two manufacturing shifts (day and night) at every plant, and every plant had the capability to produce both products sold by Wilson. Recently, the management team at Wilson noticed that there were differences in output levels across the various plants. They were uncertain what, if anything, might explain these differences. Clearly, if some plants were more productive than other, there might be some meaningful insights that could be standardized across plants to boost overall productivity. Ken Wilson asks your team, an industrial engineering group at the company’s headquarters, to conduct a study of the plant’s productivity. You randomly sampled 159 weeks of output from various plants together with the number of plant employees working that week, the plants’ average age in years, the product mix produced that week (either product A or B), and whether the output was from the day or night shift. The sampled data are contained in the file Case Study 1 Wilson Manufacturing. The Wilson management team is expecting a written report and a presentation by you when it meets again next week. Instructions 1. Identify the primary issue of the case 2. Analyze the data 3. Draw your conclusions (should include next steps)

Data: Output Number of Employees Avg Age of Plant (Years) Product Mix (A or B) Shift (Day or Night ) 2687 292 2 B Night 2305 165 5 A Day 2576 243 3 B Night 2499 295 11 A Day 2260 179 10 A Day 2974 276 10 B Night 2123 262 12 A Day 2397 239 2 A Day 2391 180 3 A Day 2579 282 1 A Day 2510 272 11 A Day 2802 229 12 B Night 2493 269 10 A Day 2288 249 7 A Day 2620 238 6 A Day 2552 244 12 B Night 2754 184 6 B Night 2507 194 8 A Day 2506 274 2 A Day 2754 252 5 B Night 2340 275 3 A Day 2768 266 8 B Night 2022 193 10 A Day 2792 214 13 B Day 2207 250 10 A Day 2450 231 9 A Day 2282 206 4 A Day 2344 179 7 A Day 2264 215 9 A Day 2137 233 7 A Day 2589 256 3 A Day 2115 246 12 A Day 2205 246 3 A Day 2317 265 2 A Day 2838 203 9 B Night 2655 184 3 B Night 2618 238 9 A Night 2800 188 4 B Night 2851 211 6 B Night 2573 188 2 B Night 2932 279 7 B Night 2211 283 9 A Day 2394 237 12 A Day 2612 244 7 A Day 2271 296 8 A Day 2835 228 3 B Night 2605 185 6 A Day 2367 276 7 A Night 2965 235 2 B Night 2160 156 6 A Day 2468 173 7 B Night 2169 207 2 A Day 2105 159 12 A Day 2182 195 5 A Day 2284 267 1 A Day 2125 220 13 A Day 2163 287 13 A Night 2573 296 7 B Night 2675 258 4 A Day 2531 185 11 A Day 2624 238 11 B Night 2239 187 8 A Day 2106 192 10 A Day 2542 225 3 B Night 2910 276 2 B Night 2568 238 5 A Day 2216 296 7 A Day 2404 188 12 A Day 2077 189 6 A Day 2536 197 11 A Day 2587 292 7 A Day 2812 172 9 B Day 2529 175 1 B Night 2615 237 7 A Day 2101 245 11 A Night 2470 179 12 A Day 2248 207 9 A Day 2610 188 4 B Night 2532 199 2 A Night 2349 251 12 A Night 2519 218 7 A Day 2440 188 2 A Day 2758 157 11 B Night 2388 237 3 A Day 2465 198 11 B Night 2209 171 12 A Day 2848 201 12 B Night 2672 286 3 A Day 2911 203 8 B Night 2377 214 9 A Day 2240 264 5 A Day 2049 165 13 A Day 2323 167 8 A Day 2688 280 11 B Night 2686 234 8 B Night 2391 251 5 A Day 2830 160 12 B Night 2764 167 10 B Night 2895 210 10 B Night 2834 186 6 B Night 2712 212 2 B Night 2450 203 5 A Day 2538 234 1 A Night 2374 200 1 A Day 2532 225 1 B Night 2006 182 12 A Day 2963 270 6 B Night 2702 250 11 B Night 2880 279 5 B Night 2468 165 7 A Day 2211 267 10 A Day 2301 199 11 A Night 2140 266 9 A Day 2549 167 11 B Night 2587 156 1 A Day 2427 186 3 B Night 2623 260 11 A Day 2372 236 4 A Night 2842 220 1 B Night 2233 275 5 A Day 2349 240 13 A Day 2534 196 10 B Night 2739 292 4 A Day 2321 241 4 A Day 2498 276 12 A Day 2448 257 7 A Day 2843 240 6 B Night 2355 184 12 A Night 2819 257 12 B Night 2244 294 6 A Day 2422 260 2 A Day 2714 213 4 B Night 2564 300 10 A Day 2534 274 3 A Day 2186 186 13 A Day 2729 297 4 B Night 2001 162 9 A Day 2356 183 13 A Day 2954 215 5 B Night 2609 247 2 A Day 2180 159 9 A Day 2258 184 8 A Day 2516 238 13 A Day 2600 234 1 B Night 2479 197 5 A Day 2533 264 5 B Night 2265 177 2 A Day 2417 262 4 A Day 2472 201 6 A Day 2498 230 9 B Night 2254 193 2 A Day 2319 220 5 A Day 2521 206 7 B Night 2348 265 12 A Day 2508 186 3 A Day 2918 247 2 B Night 2963 237 4 B Day 2284 268 5 A Day 2099 157 3 A Day

Answer 1

1. Production, Q = f ( N, PA,A/B, D/N)

The issue is to identify the relationship and effect of these four independent variables on the production level.

2. The output is:

	R²	0.121
	Adjusted R²	0.098
	R	0.348
	Std. Error	280.171
	n	159
	k	4
	Dep. Var.	Output
ANOVA table
Source	SS	df	MS	F	p-value
Regression	16,68,320.4432	4	4,17,080.1108	5.31	.0005
Residual	1,20,88,386.5128	154	78,496.0163
Total	1,37,56,706.9560	158
Regression output					confidence interval
variables	coefficients	std. error	t (df=154)	p-value	95% lower	95% upper
Intercept	2,503.7857
Num2er of Employees	0.1807	0.4888	0.370	.7122	-0.7850	1.1463
1vg 1ge of Pl1nt (Ye1rs)	-11.4339	6.0125	-1.902	.0591	-23.3115	0.4436
Product Mix (1 or 2)	328.5093	84.1195	3.905	.0001	162.3323	494.6863
Shift (1 or 2 )	-307.1610	81.6744	-3.761	.0002	-468.5078	-145.8142

3. The significant factors are Product Mix (1 or 2) & Shift (1 or 2 ).