Athol furniture case solution service operations management

athol case solution

1. ATHOL
FURNITURE
Presented by Group 16
PGP/27/072 HUDA EBRAHIM
PGP/27/158 S NIVETHA
PGP/27/232 UDISH SWAMI
PGP/27/252 GAURAV GUPTA

2. Question 1
Huff Location Model : store size and
location recommendation
1.
Data given
λ = 1.0
>10,000 sqft
Increments of 5,000 sqft
Attractiveness of Facility Calculation
(Aij) = (Sj) / [(Tij)^ λ]
where,
-Aij => Attractiveness of facility j for consumer i
-Sj => Size of facility j
-Tij => Travel time from customer i's location to facility j
-λ => Parameter estimated empirically to reflect the effect of
travel time on various kinds of shopping trips

3. Question 1
Huff Location Model : store size and
location recommendation
1.

4. Question 1
Huff Location Model : store size and
location recommendation
1.
Calculation of Probability
Probability, Pij=Aij/Σ Aij
where,
Pij => Probability of customer from area i travelling to facility j
Aij => Attractiveness of facility j for consumer i

5. Question 1
Huff Location Model : store size and
location recommendation
1.
Probablity a Customer from area I will travel to facility j

6. Question 1
Huff Location Model : store size and
location recommendation
1.
Probablity a Customer from area I will travel to facility j

7. Question 1
Huff Location Model : store size and
location recommendation
1.
RECOMMENDATION
The store should be located at site Y, 20,000 Size

8. Question 2
2. The expected annual net operating profit
before taxes and expected market share for
the outlet.
Estimate, Ejk=Σ (PijCiBik)
where,
Ejk => Total Annual Customer Expenditure for product class k at facility j
Pij => Probability of customer from area i travelling to facility j
Ci => Number of customer at area i
Bik => Avg Annual Customer Budget at area i for product class k
Calculation of Total Expenditure

9. Question 2
2. The expected annual net operating profit
before taxes and expected market share for
the outlet.

10. Question 2
2. The expected annual net operating profit
before taxes and expected market share for
the outlet.

11. Question 2
2. The expected annual net operating profit
before taxes and expected market share for
the outlet.
Market Share Estimate, Mjk=Ejk/ Σ (CiBik)
where,
-Mjk => Market share captured by facility j of product class k
-Ejk => Total Annual Customer Expenditure for product class k at facility j
-Ci => Number of customer at area i
-Bik => Avg Annual Customer Budget at area i for product class k
Calculation of Market Share

12. Question 2
2. The expected annual net operating profit
before taxes and expected market share for
the outlet.

13. Question 2
2. The expected annual net operating profit
before taxes and expected market share for
the outlet.
Recommendation
The store should be located at site Y as it has high market share and
customer expenditure.
The expected net operating profit before taxes would be 2.9

14. Question 3
3. Alternative λ = 0.5 and 5.0
λ = 0.5

15. Question 3
3. Alternative λ = 0.5 and 5.0
λ = 0.5

16. Question 3
3. Alternative λ = 0.5 and 5.0
λ = 0.5

17. Question 3
3. Alternative λ = 0.5 and 5.0
λ = 0.5

18. Question 3
3. Alternative λ = 0.5 and 5.0
λ = 0.5

19. Question 3
3. Alternative λ = 0.5 and 5.0
λ = 0.5

20. Question 3
3. Alternative λ = 0.5 and 5.0
λ = 5.0

21. Question 3
3. Alternative λ = 0.5 and 5.0
λ = 5.0

22. Question 3
3. Alternative λ = 0.5 and 5.0
λ = 5.0

23. Question 3
3. Alternative λ = 0.5 and 5.0
λ = 5.0

24. Question 3
3. Alternative λ = 0.5 and 5.0
λ = 5.0

25. Question 3
3. Alternative λ = 0.5 and 5.0
λ = 5.0

26. Question 3
3. Alternative λ = 0.5 and 5.0
The measurement of attractiveness to site Y for different λ values suggests
that the propensity of customer travel is higher when λ is lower and tends to
decrease as λ value increases.
CONCLUSION

27. Simplicity and Assumptions: The model assumes homogeneity in consumer preferences and uniform
attractiveness, which doesn't reflect the diversity of consumer behavior. The distance decay function
used is also a simplification, not accounting for real-world travel complexities.
1.
Static Nature: It provides a static analysis, not accounting for changes over time like new competitors
or shifts in consumer preferences.
2.
Limited Variables: The model primarily considers attractiveness and distance, neglecting factors like
price, brand loyalty, and socioeconomic status.
3.
Data Requirements: The model's accuracy relies on high-quality data, and inaccuracies can lead to
misleading results.
4.
Over-simplification: It oversimplifies complex purchasing decisions, ignoring psychological, cultural,
and situational factors.
5.
Non-Spatial Factors: The model doesn't account for non-spatial elements like online shopping trends.6.
Geographic Limitations: The model's assumptions may not hold in areas with complex geographic
and transportation dynamics.
7.
4. Shortcomings of the model

28. THANK YOU
Group 16