Sales and Operation Analysis: A Case Study in Pt. Berkat Popok Bahagia

: The purpose of this paper is to help solve one of the current existing issues of PT. Berkat Popok Bahagia which is SKUs being out of stock. It turns out that there are several reasons for this. The first reason is the amount of the stock of each SKU in the system differs from the amount of stock in the warehouse. The second reason is the demand from the distributors is not in sync with the sales and operational planning team, making it when the distributors order products (SKU) from the factory, the product is not available because it might not be in the production process. The third reason is the forecasting method that is currently being used is less suitable. Author compared the forecasting accuracy of each forecasting method in the top 2 SKUs of baby care category from simple moving average method, simple exponential smoothing method and exponential smoothing with trend method. The results of the forecasting accuracy is based on the MAPE value in which lower value indicates better method. The forecasting accuracy measurement result when comparing the 3 methods between the 2 SKUs are respectively 24.3% and 20.24% for simple moving average, 20.28% and 16.19% for simple exponential smoothing and 18.07% and 15.65% for exponential smoothing with trend. The results indicate that exponential smoothing with trend provides the best performance among the three which therefore author recommend this method compared to the current simple moving average method.


INTRODUCTION
PT. Berkat Popok Bahagia is a company that operates in the category of tissue and hygiene paper.Currently, this company's position in terms of market share is one of the biggest in the industry according to the data from Nikkei Asia [1].In Indonesia, there are 275.77 million people with the number of babies and toddler (under 4 years old) reaching 22.09 million people [2].Comparing to the neighboring countries in terms of market population, Indonesia has the most potential in the baby diapers market as the usage of diapers in Indonesia is considered low based on the data from Euromonitor International in 2018 with only an annual usage of 282 diapers/disposable pants while the usage of diapers/disposable pants in Malaysia and Thailand averages at 430 annually.Japan's usage of diapers/disposable pants reached 1735 in the same period [3].
In terms of product category within the company, products are divided into 4 categories which are baby care products, feminine napkin, adult care and family care which is also part of the analysis in this paper.
The objectives of this paper are to assess and analyze the current existing process of the sales and distribution in DKI Region, to figure out the root cause that affect the current sales and distribution situation and to finally propose and recommend a solution that can improve the current situation.In this research, author combine both qualitative and quantitative research methods.Qualitative research is derived from interview and observation while quantitative research is derived from the internal data that is obtained from the company before the data is processed.
In this research, author will focus more on the KPI of distribution because one of the issues that occur lately in the company is that several stock keeping units (SKU) being out of stock.There are 5 distributors in the region which caters 4 segments which are baby shops, wholesale, retailer, and local key account.Author will analyze the performance of each of the distributors in the region before conducting a deeper analysis on one of the distributors.Author's priority is to then find out and analyze who the key customers are, what SKU that they frequently order, how much is the contribution of their orders towards the total revenue of the SKU they ordered.Then author also propose 3 solutions to address the out-of-stock issues.The company's stakeholders are involved in deciding one of the three solutions that they think are the most feasible to implement in the future so that this paper will be more objective.Once the solution is formulated, author will focus on that solution.The goal is to reduce the probability and risk of SKUs being out of stock in the future.

A. Optimization
Optimization in business is the automated improvement of business processes using prespecified quantitative measures of performance [4].Optimization is needed because there is a gap between the current capabilities and the expected capabilities or result.There might be a bottleneck in the current system that inhibits the firm, business unit, or company to run at its full potential.This optimization process is often overlooked or ignored as to achieve significant improvements, a business has to undergo fundamental changes which is not easy to do [5].The objective of optimization is to improve the processes and ultimately to reduce costs [6].When a company manages to implement this optimization in every step of the processes, efficiency is bound to be achieved.

B. Distribution
Distribution can be defined as the transportation of products from the point of production to the point of demand in order to satisfy the expectations of both producers and consumers [7].Distribution is a part of supply chain, with the purpose to deliver goods to consumers to the demand points with the right place, time and quantity with the lowest possible amount of cost.Fisher (1997) stated that the most decisive factor that leads to the selection of the distribution channel and the SCM in general [8].Also, the nature of product whether it is functional with predictable demand or innovative with unpredictable demand like high-tech electronic devices.Every distribution system have objectives that must be achieved such as: 1.The management of the distribution channel with the lowest possible cost through certain procedures [9]: a) planning financial resources to be used within the supply chain, b) planning of the distribution networks and routes of which it is composed, c) selection of partners within the distribution channel, and d) control of the system performance.2. Ensuring that the products being distributed are of high quality.Namely the maintenance of quality in a stable manner and the ability to respond to consumers' needs and desires.3. Ensuring the highest level of customer service, with the aim to convert them into loyal customers.4. Ensuring maximum flexibility in the distribution network even in cases where problems like adverse weather conditions occur, in order to maintain the credibility of the company.[10]

C. Simple Moving Average
Simple moving average is one of the forecasting methods that practitioners like to use.Various surveys were conducted by Ali and Boylan shows that simple moving average was ranked as the top choice of in most surveys that is conducted.These surveys are conducted to ensure the usage, familiarity, and satisfaction of forecasting methods among practitioners [11].According to Makridakis et al, the accuracy of simple moving average forecasting method is similar to single exponential smoothing forecasting method [12].However, the limitation of simple moving average is that this method is not compatible for products with seasonal demand patterns, or in other words it is not suitable for seasonal products [13].This below is the formula of the current inventory forecasting method which is simple moving average.

D. Simple Exponential Smoothing
Exponential Smoothing Forecast is a forecast method that utilizes weights.This method allocates the most weight to the most recent data and the weight declines in an organized way when older data are included [14].Exponential smoothing methods are very popular in supply chain management and business analytics due to their simplicity, transparency, and accuracy [15].Since the exponential smoothing places a higher focus on recent data, this results in a significant reduction in the error in the forecast.Unlike simple moving average which needs a lot of historical data, this method requires less data in computing [14] There are no consistent guidelines on what the appropriate value of the smoothing constant  is, but introductory treatments of forecasting suggested that the smoothing constant be kept small between the 0.1 to 0.3 range [18] and [17].

E. Exponential Smoothing with Trend
Similar with the previous exponential smoothing, but with an additional variable of trend.Research from Pujiati stated that this method is useful when the data shows trend both upward and downward [19].Trend is smoothed estimate based on the average growth at the end of each period [20].According to Jacobs, exponentially smoothed forecasts can be corrected by adding in a trend adjustment.There is also an additional constant  for trend adjustment.Both  and  are used to reduce the error that occurs between the actual and the forecast.[17] This is the equation of exponential smoothing with an additional variable of trend [17]: =   +   Where:   = The exponentially smoothed forecast that does not include trend for period t   = The exponentially smoothed trend for period t   = The forecast including trend for period t  −1 = The forecast including trend made for the prior period  −1 = The actual demand for the prior period  = Smoothing constant (alpha)  = Smoothing constant (delta)

F. Measurement of Forecast Accuracy
Measurement of forecast is necessary to compare the forecasting methods.Hartini stated that here are 3 parameters that will be measured which are Mean Absolute Deviation (MAD), Mean Square Error (MSE), and Mean Absolute Percentage Error (MAPE).The forecasting method is chosen based on the smallest value of those 3 parameters.

Mean Absolute Deviation (MAD)
MAD is the average value of the difference between the actual data and the forecast result over the number of period that is calculated.As the term 'absolute', if the error number is negative (actual data greater than forecast), it is still calculated as a positive result.Systematically, this is the formula for MAD [21]: Mean Square Error (MSE) MSE is calculated by the average of squaring the number of absolute error (actualforecasting) of each period which is then divided by the number of periods.This is the equation used to get the MSE results [21]:

Mean Absolute Percentage Error (MAPE)
MAPE is a more useful method compared to MAD because it can give a clearer representation in the form of percentage.The lower the amount of percentage means that the forecast is more accurate.Before MAPE is calculated, there is APE (Absolute Percentage Error) which can be seen at every period [21].Chang also categorize the MAPE into four categories as shown in table 1 [22].

G. Pareto Analysis
Pareto analysis is one of the most common tools and one of the easiest tools to use in decision making.It is a statistical technique in decision making that is used for the selection of a limited number of tasks that produce significant overall effect.[23] Pareto analysis is used to determine the tasks or factors in an organization that will have the most significant impact.[24] Factors or tasks (data) that are gathered are then ranked in descending order from the highest frequency of occurrences to the lowest frequency of occurrences which in total summed up to 100%.The vital few with the highest occurrences occupy about 80% of the cumulative occurrences while the rest 'useful' factors occupy the rest 20% of the occurrences.This is known as the 80-20 rule which is developed by Italian Economist Vilfredo Pareto.[25] The results of a pareto analysis are presented in the form of a chart.The chart is in the form of a bar graph consisting of factors or tasks that is ranked in descending order from the most vital (highest frequency of occurrences) to determine which factors are vital and can provide the most benefit by providing a clear indicator through superimposing a line graph that cuts an 80 percent cumulative percentage.The line graphs can determine those factors which have least amount of benefits and vice-versa.[23] The pareto chart is used to determine which customers has the biggest impact and also to determine the major factors that have been causing the delivery of products to consumer to be delayed.The analysis of the pareto chart will be discussed further in the results and discussion section of this paper.

A. Data Collection Method
For this paper, data will be collected through several ways.There are three methods that will be used in collecting the data, which are interviews, observation, and internal data obtained from the regional sales manager.In this final project, interviews and observation serves as the primary data while the internal data serves as the secondary data.

Interview Method
Interviews are a qualitative research method that follow a deceptively familiar logic of human interaction: they are conversations where people talk with each other, interact and pose and answer questions [26].Interviews will often be used as a standalone method or combined with other qualitative methods, such as focus groups or ethnography, or quantitative methods, such as surveys or experiments [27].In this final project, there are two interviews be conducted.The first is with the newly appointed regional sales manager.The type of question that the author is going to ask will be open-ended questions, which allow the subject to be flexible in answering the questions and may somehow provide the author with data that the author needs in accomplishing this final project.The questions that the author ask to the regional sales manager started from trivial questions like whether the total sales target is achieved for the period, the KPI that is used and several other questions.The author also requested internal data like actual sales data and the target of the period to strengthen the validity of the interview and to measure how often the company reached the sales target that is predetermined in the previous period.
The second interview that author conducted is with the distributor supervisor at one of the distribution centers under the same area with the regional sales manager.the questions that are asked to the supervisor is mostly related to the distribution process starting from when the products (SKUs) that are ordered arrived at the warehouse up to when the products (SKUs) is received by the customers.The author also asked about things like the KPI within the distributor, the fleets that are used in distributing products to customers, the quality of the product when the product first reached the warehouse, and several other questions.

Observation Method
Observation is one of the oldest and most fundamental research methods approaches [28].Observation involves collecting data using one's senses, especially looking, and listening in a systematic and meaningful way [29].
Observation is done on the basis of observing the day-to-day operation of the company related to the sales and distribution aspects.So, the observation is not conducted in the headquarters, but more on the distribution centers before the product is shipped to retailers and wholesalers.The goal of this observation is to find out the parts of the operation related to distribution activities that both the company and distributor can improve in the future.

B. Data Analysis Method
The method that will be used for the data analysis is both qualitative and quantitative methodology.The interviews that are conducted will be backed by internal data to verify the validity of the interview.The author will also utilize Pareto Analysis as a qualitative methodology to determine which consumer segment are the most vital few.
As for the quantitative methodology, the author will utilize the internal data (secondary data) the author need that are given by the company because all the internal data are related with numbers which therefore have to be analyzed further.Author will analyze which distributor have the biggest issue from calculating whether the amount of actual sales distribution reached the target that have been set previously.This can be used as a material that the author can study before proposing a strategy with the hope to increase the number of actual sales distribution of the company.
From that point, author will do a pareto analysis do find out the customers which stand at the top.Pareto will be conducted twice to determine customers that stand at the top of the top in each of the segments.From those top customers, author will check and analyze which products (SKUs) that they frequently order to find out which SKUs are the income generator for the company.Author will also focus on the top 2 SKU of products that fall in baby care category and family care category.From here, author will finally propose a solution that can be of help to the company through AHP method which involves the related stakeholders to vote on the solutions that have been proposed so that the proposed recommendation is not subjective based on only the author's opinion.Author will also create an implementation plan for the solution that have been proposed to function optimally.
Diagram 1 describes about the flow of data analysis that will be conducted in the results and discussion segment.

RESULTS AND DISCUSSION
This chapter comprises of three main parts, the analysis, proposed solutions, and the result of the accepted solution.The analysis part starts from the distributor analysis to determine which distributor is the weakest by utilizing the internal data (secondary data) that have been provided by the company.
After that, the next phase is to analyze the key customers and SKUs in that distributor that contributes the most income before proposing several solutions and come up with the best recommendation for the company by using voting with ranking by the company's stakeholders and the final part of this chapter is to propose the implementation plan that can be followed by the company in their future planning.

A. Distributor Analysis
Based on the interview, there are 5 distributors under the same group in region DKI-1, which all of these distributors are located in Jakarta and Tangerang area.Each of the distributors have its own targets which are different compared to the other distributors in the area.The author have been given data which are used in analyzing each of the distributor's performance.These below are the targets and actual sales achieved by each of the distributors for the year 2023 (January -November).These distributors are respectively named A, B, C, D and E. There is no target set for the month of May therefore the company set the target to be equal to the actual sales.Author measures the cumulative difference of each of the distributor's performance.Difference is obtained from the actual data subtracted by the target in each period before cumulated from January to November.Negative cumulative difference means that the distributor is performing below the predetermined target and vice versa.

Overall Distributor Performance
From table 2, it is clearly shown that there are three distributors that are performing lower than the target which is shown by the negative amount in cumulative difference.However, this thesis will only cover a deeper analysis of distributor A instead of distributor B and E based on three reasons.The first reason is because when the table above is sorted in a descending order based on the total actual, distributor A is the biggest distributor with negative cumulative difference.Distributor B is not picked because this distributor has the lowest amount of cumulative difference of all compared to distributor A and E. Despite having the biggest negative cumulative difference, distributor E is not picked because unlike distributor A and B, distributor E have always reached the target set by the company since June while distributor A and B experience negative difference for three consecutive months (September-October-November).Therefore, with these three reasons, author picked distributor A to be analyzed further.

B. Customer and SKU Analysis
After determining which distributor to focus on, customer analysis is conducted to find how big the contribution of those key customers (outlets) towards the actual revenue of the distributor.The goal of the customer analysis is to retain those key customers on the operational level like for instance delivery delay that leads to poor customer experience which might ultimately prompt customers to move to competitor brands.The customers of the distributor and company are not end customers but outlets that sell the products (SKUs) that have been ordered from the distributors which leads to the 3 segments that distributor A caters, which are baby shops, wholesale, and retailer.The pie chart in figure 2 describes the revenue proportion of each segment in distributor A. From the pie chart above, author decided to analyze 2 out of the 3 segments which are baby shop and wholesale segment because the proportion of the retailer segment is very small and less significant compared to the other two.

Baby Shops Segment
As one of the company's products are baby diapers that come in various sizes and packaging for babies of all ages, baby shops become one of the most vital segments that generates income for the company.According to the pie chart above, in the region for the year 2023 (period January-November) specifically in distributor A, baby shops attributed 67.89% of the total revenue which is Rp58.88 billion.
In total, the revenue generated by the top 20% customers for this segment is Rp57.77 billion which is 98.12% of the total revenue of the baby shop segment.Distributor A caters 79 baby shops in its jurisdiction.According to the Pareto rule which is the 80-20 rule, both the company and distributor haven't reached the ideal pareto equilibrium.Author conducted a pareto analysis to determine who the top 20% customers are, resulting in 16 customers.Author decided to reconduct a pareto analysis of the top 16 customers, resulting to 4 key customers shown in figure 3 to narrow down the SKU that the 4 key customers ordered and check whether the SKUs that they ordered correspond with the top 2 SKUs of each of the categories to be analyzed.The pie chart in figure 4 describes the proportion of each category for this segment specifically for the key customers.In terms of product category in baby shop segment, the biggest income generator comes from baby care category which consists of baby diapers which comes in various types and sizes suitable for babies of all ages up to 4 years old as shown in the pie chart above with 76.30% which is then followed by family care.Family care with the proportion of 20,70% consists of products like hand sanitizers, antiseptic tissues and masks of various types such as daily masks, surgical masks etc. Next, feminine napkin with proportion of 1.56% consists of products specifically made for female hygiene.Last, with the least proportion for this segment is adult care which of only adult diapers of different types, sizes, and packaging.In short, baby care products become the backbone in the baby shop segment.
After further exploration, author also conducted an analysis whether the products (SKUs) that generated the most revenue for this segment are the same with the products that the key outlets in the segment ordered from the distributor.The top 2 SKUs are shown in figure 5.
The bar graph above describes the top 2 products (SKUs) that generates the most income in which for this thesis, author will only focus on the top 2 SKUs both in baby care category and family care category because these SKUs generate the most income for the company.Based on the findings, not all of the top 2 SKU in each category are ordered by each of the key customers.These below are the analysis results for baby care category and family care category.
In the baby care category, for the first SKU which is "SSPL 654", generates the most income for the company.Within the top 5 SKUs of those 4 key customers this SKU was ordered by 2 of them with the accumulation of Rp2.831 billion or 66.52% of the total revenue of this SKU.The second SKU "SSPM 660" is also ordered by two key customers based on the list of the top 5 SKU of key customers.Within the key customers, the income generated by this SKU adds up to Rp2.357 billion or 61.35% of this SKU's total revenue.
Both of these SKUs experienced trouble in September and October not because this SKU suddenly became less popular within the customers, but because the products became out of stock because of the unavailability of raw materials in the factory.Therefore, Same with the analysis on the baby shop segment, author also conducted an analysis whether the products (SKUs) that generated the most revenue for this segment are the same with the products that the outlets in the segment ordered from the distributor.The top 2 SKUs are shown in figure 8.
The graph above provides details of the top 2 products (SKUs) that contributes the most income.Based on the findings, not all of the top 2 SKU in each category are the same with the top 2 SKU in each key customer.These below are the analysis results for each category.Author will only focus on the family care category because this category represents majority 96.37% of the revenue generated by the key customers based on the pie chart above.
Moving on with the analysis, the top 2 SKUs are "SDM 2030" and "SSM3D 2030".An interesting fact is that the top 2 SKU in this segment are all the same with the baby shop segment, meaning that it can be claimed that these 2 SKUs are indeed popular."SDM 2030" is the SKU in this category that generates the most income in this segment.This SKU is ordered by all 5 key customers.
Based on the list of the top 5 SKU of key customers, this SKU contributes Rp6.947 billion which is equal to 96.26% of the income generated by this segment.The second SKU that generates the most income is "SSM3D 2030".This SKU is also ordered by all of the key customers.Just within the key customers, "SSM3D 2030" contributes Rp5.931 billion which is 83.53% of the total revenue of this SKU in this segment.
Another SKU that is worth to note which is not on the list of top 2 SKUs is "SDMP 230".This SKU is ordered by 4 out of 5 key customers and those 4 contributed Rp6.284 billion.This is equal to 94.77% of the total revenue generated by this SKU in this segment.

C. SKU Treatment Analysis
Starting with the order process, the distributor will conduct a monthly order booking (MOB).The ordering process to the factory or suppliers start from the 25th of each month until the 1st day of the next month.It will take approximately 5 days from the time the products (SKUs) are ordered until the products (SKUs) reached the warehouse before distributing to the customers.The sales and distribution department will also do an additional order booking under two conditions.The first condition is when there is an unexpected demand from consumers and the amount of products (SKUs) left that is ordered by the customers is not enough to cover the unexpected demand.The second condition is for products like masks because masks are deemed as seasonal products with unpredictable demand which leads to instability in income.Masks are only ordered to the factory (producers) when there is a demand or order from customers therefore orders for products like masks are always counted as an additional order.
Based on the interview that is conducted, every SKUs whether the SKU is fast-moving or slow-moving use the same method for the inventory management of the warehouse.Currently, the inventory forecasting method that the distributor is using is based on the 3-months moving average.So, the amount of each of the SKUs that the distributor ordered are based on the average of the actual sales of each SKU for the past 3 months.
However, different from the usual simple moving average, the amount of each of the SKU that is supposed to be ordered based on the forecasting is added by another 20% which is termed as buffer stock to prevent the chance of products (SKUs) to be out of stock.
Therefore, author would like to propose a method that can be used by the sales and distribution department.The solution that will be proposed will be explained in the next subchapter.

D. Solutions Proposed
In this subchapter, author will propose solutions that can overcome the current existing issue of SKUs being out of stock.These solutions are then processed with the help of the company's stakeholders to determine the most recommended solution that is feasible to be done by the sales distribution department.After the recommended solution is formulated, author will also propose an implementation plan that can fit the current distribution system of the sales distribution department.
In total, there are 3 solutions that the author can propose to resolve the current issue involving the SKUs.The goal of the solutions that are proposed is to reduce the probability of the SKUs being out of stock.Here are the solutions that author would like to propose.

Bridging System
The first solution is to create a bridging system that is separated from the two existing systems that can easily support in adjusting the data regarding the stock of each of the SKUs in the company's stock monitoring system.Currently, there are dual systems that are utilized, the first one is the company's system to monitor the amount of each SKUs that go in and out of the warehouse and the second one is the distributor's system to also check the amount of each SKU in the warehouse.There are dual systems because the distributor that is used by the company does not belong to the company but third-party distributors therefore it is impossible to integrate two systems to become one.These might cause a difference in the amount of stock between the company's data and the actual amount of products in the warehouse based on the distributor's system and leads to a failure in order by the distributor sales representative if the actual amount of products (SKUs) in the warehouse is less than the amount of each SKU that is ordered.Figure 9 shows a simple visualization on the bridging system that is meant by author.

Figure 1 .
Figure 1.Flow of Data Analysis

Figure 4 .Figure 5 .ISSN
Figure 4. Category Proportion of Key Customers in Baby Shops Segment in Distributor A

Figure 7 .Figure
Figure 7. Category Proportion of Key Customers in Wholesale Segment