Developing Efficient Lead Time Practice in the Supply Chain Process to Enhance Customers’ Satisfaction in FMCGs in Nigeria

The production and delivery of high quality goods and services that satisfy customers’ expectations are essential elements in determining the long term achievement of manufacturing companies. This paper aims to develop efficient lead time management in the supply chain processes of Nigerian fast moving consumer goods (FMCGs) manufacturing companies as this would enhance the production of indigenous products of high quality thereby satisfying the consumers’ needs, moreover, contributing to economic growth and development of the manufacturing industry in Nigeria. The study uses a quantitative approach consisting of a cross-sectional survey and the administration of structured questionnaires to staff of FMCGs in selected study areas of Lagos and Ogun State, Nigeria. The data available for this study was analyzed using ordered logistic regression model with the aid of different statistical tool; SPSS IBM version 24 and STATA 13.0. The outcome of the study indicated the underlying dimensions of lead times and the service response level of customers, with an overall objective to meet customers’ needs. Furthermore, the result showed that adequate stock level and reduction in lead times contributed to the availability of quality products to customers on time . Keywords: Supply chain, Lead time, Customer satisfaction, FMCGs, Manufacturing industry. DOI : 10.7176/JESD/10-2-13

attending to supplier and customer orders, processing these orders, maximizing the time required for production, loading, shipping, distribution and reducing backlogs of inventory as this would assist in cost reduction and capacity utilization (Mourtzis, Doukas, Fragou, Efthymiou, and Matzorou (2014). Cottleer and Bendoly (2006) investigate the influence of order lead time improvement on operational performance of firms in United States of America. Analysis of covariance (ANCOVA) was applied to the longitudinal data collected. The results suggest variation in lead time performance of firms under different conditions. Koschat (2008) also analyzed the effect of store inventory on demand using a magazine retailing as a case study. Data from segmented stores were subjected to factor analysis followed by regression model with OLS technique. The results showed that when an inventory decrease occur for one brand, it would result in a decrease in demand for that brand, and this would also result in an increase in demand for a competing brand. These observations support the expansion of the traditional Newsvendor model to include inventory effects, as well as the practice to make inventory decisions for retail categories rather than just individual brands.
A review by Mula, Poler, Sabater and Lario (as cited in Sadraoui and Mchirgui, 2014) reveals that the uncertainty in product lead time was less studied in the past. A simulation experiment carried out by Tim and Groenevelt (1984) also indicates the effect of safety stocks and lead times with uncertainty in the demand and supply of a product, however, he confirms that the benefit of safety stock is its responsiveness, whereas safety lead time increases flexibility indicating that safety stock is more effective with uncertainties in demand, and in the case of uncertainty in supply and demand; a safety lead time is more effective than an equivalent level of safety stock. The authors noted that adopting a safety lead time would result to higher inventory levels with a comparable level of safety stock when demand uncertainty is high or the number of stock keeping units is high. Grubor, Milicevic and Djokic (2016) analyzed the effect of inventory level and lead time on product availability and sale in the FMCG in Serbia. Data were collected from 16 retail stores using ERP information platform connected with store's POS terminals. Data analysis was carried out using structural equation model (SEM) and the results of the research show a much higher level of shelf-life when compared to store stock-out rate and this confirmed the existence of the problem in the realization of internal product flows within retail stores. However, despite the occurrence of this problem, besides direct positive effect of inventory level on sale, its indirect effect was also positive. Oyatoye and Fabson (2011) investigated the simulation approach in quantifying the bullwhip effect in the supply chain using various forecasting methods and stressed that there was inadequate information in the supply chain and there is a need to improve information management along the supply chain so as to effectively leverage lead times , which also agrees with the study carried out by Sadraoui et al (2014) on the importance of information thereby developing a framework based on the Model Predictive Control (MPC) and a forecasting module that indicated the impact of forecast accuracy on the overall control performance of the supply chain in terms of optimizing lead time in production, and rendering better services, sales growth, lower costs of production and inventory. The study of Therefore this study views lead time in terms of order lead time and product lead time utilizing the application of the ordered logical regression model to develop an optimum lead time in the supply chain to enhance customer satisfaction in FMCGs in Nigeria.

The Study
The purpose of this study was to examine the need for FMCGs especially small and medium FMCGs in Nigeria to develop efficient lead time management in its supply chain processes as a competitive strategy in improving its operational processes in order to enhance product loyalty and patronage among its customers; it therefore uses a quantitative approach for the investigation. The data available for this study was analyzed using ordered logistic regression model with the aid of different statistical tool; SPSS IBM version 24 and STATA 13.0.
This study attempted to investigate best practices from the perspective of service staff and executives of some FMCGs industries. A key objective was to examine the impact of responsive supply chain processes on customer satisfaction.

Research Questions
i.
Will reducing the order processing lead time in the FMCG industry influence meeting the customers' request on time? ii.
Will reducing the product lead time in the FMCG industry affect product availability to customers?

Research Design
The current study has adopted a cross-sectional survey design as it can help explore the research problem and provide a profound knowledge of the research problem in a real life situation. This study adopts a quantitative research design which entails testing a hypothesis with quantitative data collections and analysis in order to 122 develop responsive supply chain processes to enhance customer satisfaction.

Research Setting and Participants
The participants have been randomly selected from 278 fast moving consumer goods (FMCGs) industries located in the south-western states of Lagos and Ogun states.

Sampling Size and Sampling Techniques
A two stage sampling techniques will be employed. At the first stage, manufacturing firms will be stratified across production sector. The second stage will involve using random sampling technique. Applying the Taro (1967) sampling formula, a total of 278 manufacturing firms will be sampled. The FMCG industry is categorized into 4 sub-sectors: food and beverages; chemical and pharmaceuticals; alcoholic drinks; as well as, home and personal care. For ease of sampling, approximately 70 manufacturing firms will be sampled from each of the FMCG industry sub-sector.
(1) Where, n = anticipated total sample size; N = population size; e = acceptable error term (0.05). Therefore, the total sample size was computed as: = 278

Data Collection
Data will be obtained through the use of a structured questionnaire using five point Likert scale type questions. Relevant data on order processing lead time, product lead time, stock level and customers satisfaction will be sourced from staff of sampled FMCGs manufacturing firms and customers. Operation managers and other employees in the production units will be particularly targeted.

Empirical Model (Ordered regression model)
In order to analyze data on the lead times quantitatively, the ordered logit model is applied based on the following specification: Where x i is the set of explanatory variables and ε i , is the error term. y i * is unobserved. What is observed; + Y i = 0 if y i * ≤ µ 0 , = 1 if µ 0 <y i * ≤ µ 1, = 2 if µ 1 <yi* ≤ µ 2, … … = n if y i * > µ 1n . The explanatory variables in ordered model are nearly all categorical variables. To avoid identification problems for each variable one level has to be omitted from the set of explanatory variables reflecting the various levels of influence an order processing and product lead time would have on the availability of products to customers. The explicit form of the model is expressed as: (3) Where y i represents measure of products' availability; x 1… x n is the measure of order lead time (product lead time) The relationship is expressed as: Y = α i + β 1 ctm 1 + β 2 adtm 2 + β 3 optm 3 + β 4 pktm 4 +β 5 pltm 5 +β 6 psvt 6 +β 7 vatm 7 +ε i (4) TOP = average time of order placement by customers ATO= administration time of order OPS = average time of order processing/production signal TRP = the time required to produce or process the order VATM= average 'value added' time on order PKTM = minimum time required to package a processed good TPG = average time distance between processed goods and service to customer

The Estimated Level of Influence an Order Processing Lead Time Would Have in Meeting Customer's Request on Time in the FMCG Industry Research Question One: Will reducing the order processing lead time in the FMCG industry influence meeting the customers' request on time?
To determine the level of influence an order processing lead time would have in meeting customer's request on time in the FMCG industry, ordinal logistics regression model was carried out using STATA 13. The results are presented in Table 1 (3) = 13.85, Prob> Chi 2 = 0.0031). This denotes that the specification is appropriate to achieve the stated objective. The estimated coefficients and the odd ratios of the measures of order processing lead time, which are represented by average time of order placement (TOP), administration time of order (ATO) and average time of order processing/production signal (OPS) are presented. The results show a positive and significant influence both administration time of order (ATO) and average time of order processing/production signal (OPS) would have in meeting customer's request on time in the FMCG industry. The positive and significant coefficient of ATO (β = 0.337, p < 0.05) indicates that a 0.337 unit increase in administration time of order would influence an increase in the log odds of time needed to meet customer request. Also, a 0.323 unit increase in average time of order processing/production signal (OPS) by the FMCG (β = 0.323, p < 0.05) would directly influence the log odds of time needed to meet customer request.
The average time of order placement has no significant influence on the time needed to meeting customer's request on time in the FMCG industry. The result highlights the relevance of adequate supply chain management that seeks to reduce order processing lead time in the FMCG. This is expected to expedite customers' response to efficient service delivery in the system.
Results of the odd ratios of the significant variables or order processing lead time in the FMCG shows that when there is a unit increase in ATO, the odds of meeting customer request on time is 1.40 unit of time more, if all other variables are held constant, Also, for a unit increase in OPS, the odds of meeting customer request on time is 1.38 unit of time more, if all other variables are held constant. This negates the hypothesis that "reduction in the order processing does not influence meeting customers request on time"

The Level of Influence the Product Lead Time Would have on the Availability of Quality Products to Customers in the FMCG Industry
Research Question Two: Will reducing the product lead time in the FMCG industry affect product availability to customers?
Results in Tables 2 show the estimated influence of the product lead time on the availability of quality products to customers in the FMCG industry using ordered logistic regression. The log likelihood and likelihood ratio are significant which indicates the appropriateness of the specification (LL = -140.891; LR chi 2 (2) = 6.57, Prob> chi 2 = 0.0375). The product lead time, TRP (the time required to produce or process the order) and the average time distance between processed goods and services to customer (TPG) are as denoted. The coefficient of TRP is positive and significant while that of TPG is not significant.
The significance of the parameter of TRP suggests that an increase in the time required to produce or process the order, is expected to result in 0.337 increases in the log odds of availability of quality products, and given that all other variables are constant. This suggests a direct level of influence of TRP on availability of quality product in the FMCG industry. Odd ratio estimates of the level of influence of the product lead time on the availability of quality products indicates that for a unit increase in the time required to produce or process the order, the odds of availability of quality products is 1.4015 greater, given that the remaining variables in the model are held constant. This confirms that reduction in product lead time in the FMCG industry have effect on product availability.

Discussion
The analysis of the availability of quality products to customers in the FMCG industry in response to lead time produced mixed results which hang on the supply chain activities being considered. The result lend credence to indirect influence of average time of order placement, average value added time on order, and average time distance between processed goods and service to customers. Utilization of minimum time required to package a processed good produce exert direct influence on availability of quality products. Therefore, the null hypothesis of research question three; "reduction in the order processing lead time in the FMCG industry does not influence meeting the customers' request on time" is rejected. Tables 1 and 2 show the estimated influence of the product lead time on the availability of quality products to customers in the FMCG manufacturing industry. The significance of the parameter of TRP suggests that an increase in the time required to produce or process the order, is expected to result in 0.337 increases in the log odds of availability of quality products, and given that all other variables are constant suggesting a direct level of influence of TRP on availability of quality product in the FMCG industry. Therefore, the null hypothesis of research question four; "reduction in product lead time in the FMCG industry does not significantly affect the availability of products to customers" is rejected.

Results in
Furthermore, the result shows the importance of having adequate stock of products in order to optimize lead times in the FMCG industry. While the components of product lead time such as administrative time of order, and average time distance between processed goods and service to customers are at positive level to the availability of quality products to customers, the average value added time on order is indirect to the availability of quality products to customers in the FMCG industry.

Conclusion
Developing efficient order and product lead times in the supply chain processes enable operators of the FMCGs to make available quality products on time to their customers by reducing bottlenecks and delay in production, as this would reflect in the repeat purchase and loyalty of consumers over a long period of time. The results from the ordered logistic regression indicates that the administrative time of order and average time distance between processed goods and service to customers are the route to obtaining positive influence on availability of quality products from the product lead time.
Therefore, developing efficient lead times of supply chain processes is a strategy for FMCGs to differentiate themselves, satisfy customers and remain competitive in business. Suggested further work includes extending studies on efficient lead time management to other sectors of the manufacturing sector in Nigeria and other developing nations and not limiting it to FMCGs. It is also important that further research consider other aspects of the supply chain processes with regards to satisfying the needs of customers.