Hml and Fsn inventory control techniques with R
Inventory is a necessary evil that every organisation big or small should have at just the right quantity and at the right time. This brings about different INVENTORY CONTROL TECHNIQUES that we can levearage on for the best inventory control measure mitigating through costs associated with understocking or overstocking.
Benefits of Inventory Analysis
First of all, some of the reasons why Inventory Analysis is so important is because purchasing is the biggest expense in any given organisation. Additionally, an Inventory Analysis, along with the classification of your products, can help improve your policies for better Inventory Control.
I expound on use of HML ANALYSIS and FSN ANALYSIS by a medium-size distribution wholesale by simulating data on 6 essential items i.e Maize bag,Mafuta 20L,Mafuta 10L,Sugar 2kg,Sugar 1kg and wheat flourbag.
The data is simulated and complete, though i will go ahead an explain how i did the two analysis.
tony_distributors<-read.csv("tony_distro.csv")
kable(head(tony_distributors),"html", escape = FALSE, align = "c",
caption = "Tony distributors data") %>% kable_styling(bootstrap_options =
c("striped", "condensed", "bordered"),full_width = FALSE)
HML ANALYSIS
In this analysis, the classification of existing inventory is based on unit price of the items. They are classified as High price,Medium price and Low cost items.
For making decisions that involve inventory control, it is good to classify the goods as per their value to know what goods are most valuable to less valuable.
I set the threshold as follows:
1.For any item > 1500 in price it is of High value
2.For any item > 500 and < 1500 its is of Medium value
3.For any item < 500 is of Low value
hml_analysis<-data.frame(ifelse(tony_distributors$price>1500,"high value",
ifelse(tony_distributors$price<500, "Low value",
ifelse(tony_distributors$price>500, "Medium value","")
)
)
)
kable(head(hml_analysis),"html", escape = FALSE, align = "c",
caption = "HML analysis") %>% kable_styling(bootstrap_options =
c("striped", "condensed", "bordered"),full_width = FALSE)FSN analysis
This analysis classifies inventory based on quantity, the rate of consumption and frequency of issues and uses. Here is we gauge according to the rate of consumption
The classification of existing inventory is based consumption of the items. They are classified as fast moving, slow moving and non-moving items.
1.For any item that quantity sold is > 20 it is Fast moving
2.For any item that quantity sold is > 5 and < 20 it is Medium moving
3.For any item that quantity sold is < 5 it is Non moving
fsn_analysis<-data.frame(ifelse(tony_distributors$sold>20,"Fast moving",
ifelse(tony_distributors$sold<5, "Non moving",
ifelse(tony_distributors$sold>5, "Slow moving","")
)
)
)
kable(head(fsn_analysis),"html", escape = FALSE, align = "c", caption = "FSN analysis") %>% kable_styling(bootstrap_options =
c("striped", "condensed", "bordered"), full_width = FALSE)Insights
What can we gain from such an analysis and what are the insights?
Generally high valuable goods are the ones we want to have in just the right quantity to reduce risk of damage or obsolescent we tend to to be careful when handling valuable items. In this inventory catalog Mafuta_20L is of high value. It should be handled with care and precisiom, also depending on consumption rate the stock should be kept as little aspossible not to limit the wholesaler’s cashflow when such inventory is hold for long.
Something perciliar is that higher priced goods have higher rate of consumption unlike low priced. However, this is expected as we are dealing with a wholesaler ,large quantities of goods are mainly sold there without breaking bulk. Wheat_flourbag might be a seasonal commodity where its consumption is dependent on celebrations and festivities hence its slow moving characteristics.
There are many other inventory control techniques that can be leveraged such as ABC, Minimum order quantity, Economic order quantity and VEN analysis.
Stay tuned.
