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Supply Chain Responsiveness and Hospital Performance: Linking

Just-In-Time Inventory Practices to Operational Outcomes in Kenya

Abuya, Joshua Olang’o

, Okello, Sharone Adhiambo

School of Business & Economics, Kibabii University, Kenya

School of Business & Economics, Jaramogi Oginga Odinga University of Science and Technology,

Kenya

DOI: https://doi.org/10.51583/IJLTEMAS.2026.15020000101

Received: 26 February 2026; Accepted: 03 March 2026; Published: 20 March 2026

ABSTRACT

Operational performance in Kenyan public hospitals remains a persistent managerial and policy concern,

particularly due to frequent medicine stock-outs, delayed replenishment cycles, and service delivery

inefficiencies. These challenges are closely associated with weak supply chain responsiveness within public

healthcare procurement and supplier systems. This study examines the relationship between supply chain

responsiveness, operationalized through Just-in-Time (JIT) inventory practices, and operational performance in

public hospitals in Siaya County, Kenya. The study is anchored on the Resource-Based View (RBV) and

Network Perspective Theory, which explain how internal replenishment capabilities and inter-organizational

supply chain relationships jointly influence hospital performance outcomes. A cross-sectional survey research

design was adopted involving 84 respondents drawn from procurement, pharmacy, stores, and hospital

administration departments across six public hospitals. Data were collected using structured questionnaires and

interview guides. Reliability testing yielded Cronbach’s alpha coefficients exceeding 0.80, while the content

validity index exceeded 0.70, confirming the robustness of the measurement instruments. Descriptive statistics

summarized institutional practices, while correlation and regression analysis examined relationships between

variables. The findings reveal a strong and statistically significant positive relationship between Just-in-Time

inventory practices and operational performance (β = 0.738; p < 0.05). The regression model explains 79.1% of

the variation in hospital operational performance (R² = 0.791), indicating that responsive replenishment practices

significantly contribute to improved medicine availability, reduced stock-outs, and enhanced service delivery

efficiency. The study concludes that strengthening supply chain responsiveness through responsive procurement

systems, flexible supplier relationships, and shortened replenishment lead times is essential for improving

operational outcomes in Kenyan public hospitals. Policy recommendations emphasize the need for improved

coordination between hospitals and national medical supply agencies, enhanced procurement flexibility, and the

integration of digital logistics management systems to strengthen healthcare supply chain responsiveness.

Keywords: Just-in-Time, Inventory Management, Operational Efficiency, Operational Performance

INTRODUCTION

Healthcare systems globally prioritize operational efficiency and service reliability as key indicators of

institutional performance. Public hospitals operate continuously and therefore require reliable availability of

essential medicines and medical supplies to sustain effective healthcare delivery. Ensuring such availability

depends not only on accurate demand forecasting but also on responsive inventory replenishment systems

capable of delivering required supplies within minimal lead times. Recent studies emphasize that effective

healthcare supply chains are critical for maintaining service continuity and improving institutional performance,

particularly in resource-constrained health systems (Apeh, 2024; Al-Khatib, 2024).

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Supply chain responsiveness refers to the ability of supply systems to respond rapidly and efficiently to

fluctuations in demand. In healthcare environments, responsiveness is particularly important because

unpredictable disease patterns and emergency cases require hospitals to maintain flexible inventory management

systems. When supply chains lack responsiveness, hospitals frequently experience stock-outs, delayed treatment,

and inefficient service delivery, ultimately compromising patient outcomes. Evidence from recent health supply

chain studies shows that inefficiencies in forecasting and distribution frequently lead to shortages of essential

medicines in many healthcare systems (Olaniran et al., 2022; Kalola et al., 2025). Just-in-Time (JIT) inventory

management has been widely recognized as a strategy for improving supply chain responsiveness. JIT

emphasizes the timely delivery of materials precisely when they are required, thereby reducing holding costs

and minimizing excess inventory while maintaining service continuity. Within healthcare systems, JIT practices

focus on supplier flexibility, shorter replenishment lead times, coordinated procurement processes, and effective

information sharing across supply chain actors. Recent research indicates that JIT-oriented inventory systems

can enhance supply chain responsiveness, reduce waste, and improve operational efficiency when supported by

effective coordination and reliable replenishment systems (Rahman & Zailani, 2017; Balkhi et al., 2022).

In Kenya, however, public healthcare supply chains often face structural challenges including bureaucratic

procurement procedures, delayed government funding disbursements, and limited supplier flexibility. These

institutional constraints frequently lead to prolonged replenishment cycles and inconsistent availability of

essential medicines. Consequently, hospitals may experience both inventory shortages and excess stock

accumulation, conditions that negatively affect operational performance. Studies on Kenya’s pharmaceutical

supply systems highlight that supply chain inefficiencies, delayed deliveries from central suppliers, and weak

coordination mechanisms contribute significantly to medicine stock-outs in public health facilities (Onyango et

al., 2024; Ayako et al., 2023).

Operational performance within hospitals encompasses indicators such as medicine availability, patient waiting

time, treatment turnaround time, and overall service efficiency. When inventory replenishment systems are

inefficient, hospitals may struggle to provide timely treatment, leading to patient dissatisfaction and deteriorating

health outcomes. Strengthening supply chain responsiveness through effective inventory management practices

is therefore essential for improving healthcare delivery. Empirical studies in Kenyan hospitals have demonstrated

that supply chain flexibility and responsive logistics systems significantly influence hospital performance and

service delivery outcomes (Kuria & Ndeto, 2024; Khasakhala, 2024).

Theoretical grounding for this study is provided by the Resource-Based View and Network Perspective Theory.

These perspectives emphasize that organizational performance depends on the strategic deployment of internal

capabilities and effective coordination among supply chain actors. Within hospital supply chains, JIT capability

represents an operational resource that enhances efficiency through rapid replenishment and coordinated

procurement processes. Complementing this perspective, network-based approaches highlight the importance of

collaboration among hospitals, suppliers, and government institutions to ensure continuous availability of

medical commodities and resilient healthcare supply chains (Nalebe, 2024; Apeh, 2024).

Despite the theoretical relevance of JIT practices for improving supply chain responsiveness, empirical evidence

linking JIT inventory practices to operational outcomes in Kenyan public hospitals remains limited. This study

therefore investigates how Just-in-Time inventory practices influence operational performance within public

hospitals in Siaya County.

Statement of the Problem

Public hospitals operate continuously and must maintain uninterrupted availability of essential medicines to

ensure effective healthcare service delivery. Achieving this continuity depends largely on the responsiveness of

healthcare supply chains, particularly the ability of procurement and supplier systems to replenish medical

inventory rapidly and reliably. However, many Kenyan public hospitals experience persistent supply chain

inefficiencies including delayed procurement cycles, prolonged supplier lead times, and frequent stock-outs of

essential medicines. These challenges undermine hospital operational efficiency and disrupt patient care. In

many cases, patients are forced to purchase medicines from private pharmacies due to unavailability in public

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hospitals, increasing out-of-pocket healthcare costs and limiting access to treatment. Evidence from Siaya

County indicates that shortages of essential drugs remain a common challenge across public hospitals despite

the existence of inventory management systems. These shortages suggest weaknesses in supply chain

responsiveness, particularly in the implementation of responsive inventory replenishment approaches such as

Just-in-Time inventory management. While previous studies have examined inventory management practices

broadly, limited empirical research has specifically examined how JIT-based supply chain responsiveness

influences hospital operational performance within the Kenyan public healthcare system. Furthermore,

operational performance in hospitals may be influenced by multiple contextual factors including procurement

policies, funding delays, governance structures, and infrastructure limitations. Failure to account for these factors

may lead to incomplete understanding of hospital supply chain performance. This study therefore seeks to

examine the relationship between Just-in-Time inventory management practices and operational performance in

public hospitals in Siaya County, Kenya.

METHODOLOGY

Research Design

The study adopted a cross-sectional survey research design. According to Cooper and Schindler (2014), a cross-

sectional survey entails collecting data at a single point in time to describe and examine prevailing conditions

within a defined population. This design was considered appropriate because it enabled the researcher to obtain

a comprehensive snapshot of existing supply chain responsiveness, as reflected in Just-in-Time (JIT) inventory

practices, and associated operational outcomes across public hospitals in Siaya County. A cross-sectional

approach is particularly suitable for efficiently gathering primary data from a relatively large population through

the use of a representative sample. It facilitates the assessment of organizational practices, institutional processes,

and performance conditions without the need for prolonged follow-up. By capturing data concurrently from

multiple facilities, the design enabled comparative analysis and strengthened the reliability of findings regarding

prevailing hospital supply chain responsiveness. In alignment with the study objective, this design provided a

practical framework for examining the relationship between Just-in-Time–based supply chain responsiveness

and operational performance in public hospitals, thereby generating empirical evidence on how responsive

replenishment capabilities influence service delivery and efficiency outcomes within the county’s healthcare

system.

Area of study

Siaya County is one of Kenya’s 47 devolved units and is located in the former Nyanza region in the western part

of the country. Administratively, the county is divided into six sub-counties: Ugenya, Ugunja, Gem, Bondo,

Rarieda, and Alego-Usonga. Siaya County was selected as the study site due to its well-documented public health

challenges. Reports from the Ministry of Health (2018) indicate that the county has recorded among the highest

mortality rates nationally, with residents facing an elevated risk of premature death largely associated with the

high burden of HIV/AIDS and malaria. Similarly, data from the World Health Organization (2017) ranked Siaya

among the counties with the highest HIV prevalence in Kenya, second only to Homa Bay County. These

epidemiological conditions place significant pressure on public hospitals to maintain uninterrupted availability

of essential medicines and responsive supply systems. Consequently, the county provides an appropriate context

for examining how supply chain responsiveness, operationalized through Just-in-Time (JIT) inventory practices,

influences operational performance in public healthcare facilities.

Target Population of the Study

The study targeted a total population of 106 personnel drawn from key functional departments responsible for

supply chain and operational management across six public hospitals in Siaya County. The facilities included

Sigomere Sub-County Hospital, Bondo Sub-County Hospital, Malanya Sub-County Hospital, Yala Sub-County

Hospital, Madiany Sub-County Hospital, and Siaya County Referral Hospital. The target population comprised

staff from procurement, stores, pharmacy, and hospital administration, as these departments play a central role

in Just-in-Time (JIT) inventory replenishment and hospital supply chain responsiveness. Specifically, the

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population consisted of 34 administrators, 33 pharmacists, 18 procurement officers, and 21 storekeepers. The

distribution of the total target population of 106 respondents is presented in Table 2.1.

Table 2.1: Target Population

Section

Ungenya

(Sigomere

Sub-

County

Hospital)

Ugunja

(Malanya

Sub-

County

Hospital)

Gem

(Yala

Sub-

County

Hospital)

Bondo

(Bondo

Sub-

County

Hospital)

Rarieda

(Madiany Sub-

County Hospital)

Alego-Usonga

(Siaya County

Referral Hospital)

Total

Administration

Pharmacy

Procurement

Stores

Total

106

Source: Siaya County MoH, (2025)

Sample Size and Sampling Procedure

This section outlines the determination of the study’s sample size and describes the sampling procedures

employed. The details are presented in the subsections that follow:

Sample Size

The sample size is a representative of a large population (Bryman, 2012). Yamane, (1967) formula was used in

determining the sample size. The sample size in each stratum was obtained proportionately. In the field the

respondents were selected using random sampling.

According to Yamane, (1967): n=

 

 

1 Ne



…………………………………Eq.2.1

Where n = is the sample size

N = is the population

e = is the error limit (0.05 on the basis of 95% confidence level)

Therefore, n = 106 / [1 + 106 (0.05)

]

n = 106/1.265

n = 84

Using a population of 106 staff members in public hospitals in Siaya County and considering an error limit of

5%, a sample size of 84 was used in the study.

This sample size was representative enough and was spread in each stratum proportionately as illustrated in

Table 2.2.

Table 2.2: Sample Frame

Section

Population

(X)

Sample

Size

X/N x

Sigomere

Malanya

Yala

Bondo

Madiany

Siaya County Referral

Hospital)

Administration

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Pharmacy

Procurement

Stores

Total

106

Source: Researcher’s own conceptualization, (2025)

Sampling Procedure

In view of the researcher’s inability to reach out to the entire population, and in order to gain the advantage of

an in-depth study and effective coverage, Yamane formula was used to establish the sample size from the study

population. Stratified proportionate sampling was used to get sample size for each stratum. In the field,

respondents were selected using simple random sampling.

Data Collection Instruments

The study used structured questionnaires and interview guide in collecting primary data.

Questionnaires

The questionnaire items were structured using a five-point Likert scale. The instrument was designed to capture

data on the study variables and was organized into distinct sections. The preliminary section gathered

respondents’ general demographic and institutional information. Subsequent sections captured data on

operational performance indicators, while the final section assessed supply chain responsiveness, operationalized

through Just-in-Time (JIT) inventory practices, within public hospitals in Siaya County.

Interview Guide

Interviews were conducted with randomly selected respondents from the study sample to complement the

information obtained from the questionnaire. This qualitative component enhanced data collection by providing

deeper insights and enabling the capture of contextual information on supply chain responsiveness and Just-in-

Time practices that may not have been fully anticipated during questionnaire design.

Data Collection Procedure

Prior to data collection, the researcher obtained a research permit from the National Commission for Science,

Technology and Innovation (NACOSTI). This authorization enabled formal access to the selected public

hospitals for field data collection. Structured questionnaires were administered to respondents using a drop-and-

pick-later approach at their respective workstations to minimize disruption of routine hospital operations.

Follow-up was conducted through telephone communication to remind participants of the agreed collection

dates, after which the completed questionnaires were retrieved. In addition, group interviews were conducted

with selected categories of staff to complement the survey data and provide contextual insights into supply chain

responsiveness and Just-in-Time (JIT) practices within the hospitals. Upon retrieval, all questionnaires were

reviewed to ensure completeness and accuracy of responses before proceeding to data coding and analysis.

Pilot Testing

A pilot study was conducted at Kombewa Sub-County Hospital involving staff drawn from the four departments

targeted in the main study. The pilot exercise aimed to assess the clarity, relevance, and reliability of the research

instruments, as well as to estimate the time required to complete the questionnaire and identify any structural or

content-related weaknesses. Hill (1998) suggests that between 10% and 30% of the intended sample is adequate

for pilot testing in survey research. Consistent with this guideline, 10% of the anticipated respondents were

selected for the pilot phase. Accordingly, ten staff members from Kombewa Sub-County Hospital were randomly

chosen to participate in the pre-test. Following the pilot exercise, response patterns were examined, participant

feedback was evaluated, and preliminary data were analyzed to identify ambiguities, inconsistencies, or

measurement gaps, particularly in items assessing supply chain responsiveness and Just-in-Time (JIT) practices.

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The insights obtained were subsequently used to refine and strengthen the final data collection instruments prior

to the main study.

Validity & Reliability Testing

Validity Test

Content validity was used to determine the validity index. The questionnaires were given to the two supply chain

management experts to evaluate and rate each item in relation to the objectives as “not relevant” or “relevant”

on a scale of the 1-4 such that; 1 = not relevant, 2 = somehow relevant, 3 = relevant and 4 = very relevant.

Content validity index would then be determined from the supervisors’ agreement scale as K/N, where K being

the number of items marked 3 or 4 and N the total number of items assessed.

The rated finding was used to calculate content validity index (CVI) using the formula:

CVI 

……………………………………………………. Eq.2.2

Where: K = Total number of items in the questionnaire declared valid by both experts; and N = Total number of

items in the questionnaire. This was solved as follows:

8125.0



CVI

The computed instrument content validity index (CVI) was ε=0.8125 > ε=0.7. The computed CVI was greater

than the minimum acceptable index of 0.70 as recommended in the survey studies by Amin, (2005) hence the

instrument was valid for the study.

Reliability Test

Reliability of the instrument was checked through split-half reliability coefficient test. The items in the

questionnaire was divided into; odd items represented by “x” and even items represented by “y”. The scores

from both halves would then be correlated. Usually, the internal consistency of a measurement scale is assessed

by using Cronbach’s co-efficient alpha (Cronbach 1951) which was calculated using Flanagan Formula shown

in Eq. 2.3.

]1[2









……………………………………. Eq.2.3

Where: R

= Reliability Coefficient of the Test; δ

= Standard Deviation (S.D.) of Scores of 1

Half; δ

= Standard

Deviation (S.D.) of Scores of 2

Half; and δ

= Standard Deviation (S.D.) of Scores of Whole Tests

To assess internal consistency reliability, questionnaire items corresponding to each study construct were

aggregated to form composite scales, upon which Cronbach’s alpha coefficients were computed. The resulting

reliability coefficients were evaluated against the minimum acceptable threshold of 0.70 recommended for

survey research (Nunnally & Bernstein, 1994). While coefficients in the range of 0.50–0.60 may be considered

adequate for exploratory studies (Nunnally & Bernstein, 1994), values of 0.70 and above indicate acceptable

reliability, and those exceeding 0.80 reflect good reliability (Sekaran, 2003). The reliability results for the study

constructs are presented in Tables 2.3a and 2.3b.

Table 2.3(a) Reliability Statistics

Cronbach's Alpha Based on Un-

Standardized Items

Cronbach's Alpha Based on Standardized Items

No. of Items

0.834

0.821

Source: Survey Data (2025)

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Table 2.3(b) Reliability Statistics

Factor

No. of Items

Cronbach's Alpha

Based on Un-

Standardized Items

Cronbach's Alpha Based on

Standardized Items

Background

0.987

0.983

Just-in-Time

0.896

0.873

Operational Performance

0.835

0.819

Overall

0.834

0.821

Source: Survey Data (2025)

The overall alpha for the Just-In-Time Inventory Management Practice items under investigation had a

Cronbach’s alpha of 0.873 indicating good internal consistency, operational performance had a good Cronbach’s

alpha coefficient of 0.819. The minimum alpha for the items was 0.819 while the highest alpha was 0.983 both

of which conformed to the project by George and Mallery (2003) thus the items formed a scale that had excellent

internal consistency reliability.

Data Analysis

After data collection, completed questionnaires were reviewed for accuracy, classified, coded, and captured into

a computerized database for analysis. Statistical processing was undertaken using the Statistical Package for

Social Sciences (SPSS), Version 24, a widely accepted analytical tool known for its strong data handling capacity

and versatility in performing statistical procedures across datasets of varying sizes (Muijs, 2004).

The study produced both qualitative and quantitative datasets. Qualitative information obtained from interviews

was examined through thematic and content analysis to extract recurring themes and contextual insights

concerning hospital supply chain responsiveness and Just-in-Time (JIT) replenishment practices. Quantitative

data were analyzed using both descriptive and inferential statistical techniques. Descriptive analysis employed

graphical and numerical summaries, including frequencies, percentages, means, and standard deviations, to

describe respondent views and institutional supply chain practices.

Inferential analysis applied correlation and regression procedures to evaluate the association between supply

chain responsiveness, conceptualized through Just-in-Time inventory practices, and hospital operational

performance. Correlation analysis determined the direction and magnitude of relationships among variables,

whereas regression analysis estimated the extent to which JIT-based responsiveness predicts operational

performance outcomes (Cooper & Schindler, 2014). Additionally, Analysis of Variance (ANOVA) was used to

assess the overall significance of the regression model. The significance of individual regression coefficients

was further examined to determine the degree to which Just-in-Time–driven supply chain responsiveness

contributes to operational performance in public hospitals. The study hypothesis guiding this analysis was

formulated as follows:

: Just-In-Time Inventory Management Practice has no statistically significant effect on operational

performance of public hospitals in Siaya County.

The following regression model to establish the relationship between the study variables guided the study:

Y= β

+ β

+e………………………………………Eq.2.4

Where: Y = Operational Performance; X

= Just-In-Time Inventory Management Practice; e- Error Term; β

represents the Model Constant; and β

1 -

are Regression Coefficients.

The regression model assumed independent, identical and normally distributed random variables with a zero

mean and a constant variance at 5% significance level.

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Diagnostic Tests for Inferential Statistics

Before conducting inferential procedures, several diagnostic checks were performed to ensure that the dataset

met the assumptions required for regression modelling. These diagnostic evaluations covered distributional

normality, inter-correlation among predictors, constancy of error variance, and linearity of relationships. The

distributional assumption was examined using the Kolmogorov–Smirnov (K–S) statistic within exploratory

analysis. This test evaluates whether the observed sample distribution differs significantly from a theoretical

normal distribution and is considered suitable for samples exceeding 50 observations. A probability value greater

than 0.05 indicates no significant departure from normality. The obtained K–S significance level of 0.55

therefore demonstrated that the data approximated a normal distribution. Potential collinearity among the

independent variables was investigated through pairwise correlation coefficients alongside Tolerance and

Variance Inflation Factor (VIF) indicators. All correlation probabilities were above 0.05, suggesting that the

predictor variables were not significantly interrelated. Correspondingly, the Tolerance and VIF results confirmed

that multi-collinearity was not present, indicating that the variables were appropriate for inclusion in the

regression model. The assumption of equal error variance was tested using a Breusch–Pagan–type procedure

based on the observed R-squared statistic. The resulting probability value of 0.3285 exceeded the 0.05 criterion,

implying that the null hypothesis of constant variance could not be rejected. This outcome confirmed

homoscedastic residuals, supporting the reliability of the regression estimates. Finally, linearity between

predicted and observed values was evaluated through residual scatterplots. The plotted residuals showed no

curvature or systematic pattern and were distributed evenly around the zero reference line with relatively uniform

dispersion. This distribution indicated that the relationship between the variables was adequately linear and

satisfied the regression assumption of linearity.

FINDINGS AND DISCUSSION

Descriptive Statistics on Just-In-Time Inventory Management Practice

Descriptive analysis was done on the effect of Just-In-Time Inventory Management Practice on operational

performance

. The results were summarized in table 3.1a

Table 3.1a: Descriptive Statistics for Just-In-Time Inventory Management Practice and Operational

Performance

Just-In-Time Inventory Management

Practice and Operational Performance

(Operational Efficiency)

Response

Frequency

(Agree)

Frequency

(Disagree)

Mean

Std. Dev

The flexibility of our vendors in inventory order

management have led to improved operational

efficiency through the pharmacy that is always

well stocked with required drugs

91(73)

09(07)

2.917

0.516

The lead time offered by our vendors and our

pharmacy in inventory order management have

led to improved operational efficiency through

the pharmacy that is always well stocked with

required drugs

87(70)

13(10)

2.109

0.317

The flexibility of our vendors in inventory order

management led to well stocked surgical and

non-surgical inventory required by medics hence

improved operational efficiency

92(74)

08(06)

2.917

0.214

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The lead time offered by our vendors and our

pharmacy in inventory order management have

led to well stocked surgical and non-surgical

inventory required by medics hence improved

operational efficiency

89(71)

11(09)

2.438

0.225

The flexibility of our vendors in inventory order

management have led to improved operational

efficiency through the pharmacy that is always

well stocked with required drugs

78(62)

22(18)

2.687

0.254

AVERAGE

87(70)

13(10)

2.614

0.305

Just-In-Time Inventory Management

Practice and Operational Performance

(Service Delivery)

Frequency

(Agree)

Frequency

(Disagree)

Mean

Std. Dev

The flexibility of our vendors in inventory order

management have led to improved operational

efficiency through the pharmacy that is always

well stocked with required drugs

73(58)

30(27)

2.735

0.325

The lead time offered by our vendors and our

pharmacy in inventory order management have

led to improved service delivery through the

pharmacy that is always well stocked with

required drugs

77(62)

23(18)

2.136

0.222

The flexibility of our vendors in inventory order

management led to well stocked surgical and

non-surgical inventory required by medics hence

improved service delivery

69(55)

34(25)

2.114

0.194

The lead time offered by our vendors and our

pharmacy in inventory order management have

led to well stocked surgical and non-surgical

inventory required by medics hence improved

service delivery

68(54)

32(26)

2.054

0.201

AVERAGE

72(58)

28(12)

2.236

0.236

Source: Survey Data (2025)

The study sought to investigate the effect of Just-in-Time practice on operational performance of public hospitals

in Siaya County. Table 4.5c shows that majority of Siaya County public hospitals believe that just-in-time

practice would have an effect on operational performance of public hospitals in Siaya County, with a mean

response of 2.614 (for operational efficiency) within the range of 2.109 ≤ µ ≤ 2.917 at 87% (S.D=.305) and a

mean response of 2.236 (for service delivery) within the range of 2.054 ≤ µ ≤ 2.735 at 72% (S.D=.236). This

finding is consistent with previous studies showing that Just-in-Time practices improve inventory efficiency,

reduce unnecessary holding costs, and enhance operational responsiveness. In healthcare settings, JIT has been

associated with reduced waste and improved resource utilization, while studies in operations management show

that JIT implementation contributes positively to operational performance and responsiveness (Balkhi et al.,

2022; Fullerton et al., 2001; Inman et al., 2011). Recent healthcare inventory literature similarly suggests that

JIT can reduce waste, improve productivity, and lower avoidable storage costs when implemented under

conditions of reliable replenishment and strong coordination (Balkhi et al., 2022). Fullerton et al. (2001) added

that Just in Time is an advantageous practice to the company and has great impact particularly on increasing

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revenue. Inman et al. (2011) also concluded that Just in Time should be given great priority in consumer goods

manufacturing firms as it greatly contributes to competitive advantage.

Descriptive Statistics on Operational Performance

Descriptive analysis was done on operational performance

. The results were summarized in table 3.1b

Table 3.1b: Descriptive Statistics for Operational Performance

Operational Performance (Operational

Efficiency)

Frequency

(Agree)

Frequency

(Disagree)

Mean

Std. Dev

The hospital always handles a large number of out-

patient’s cases on daily basis

55(44)

45(36)

2.933

1.216

It takes the shortest time possible for patients to go

through the treatment process (for out-patient)

58(46)

42(34)

2.628

1.137

For the last two years the hospital has recorded

reduction in mortality rates

56(57)

44(43)

2.917

1.313

In-patients cases normally stay in the hospitals for

a shorter period before being discharged

51(41)

49(39)

3.007

1.144

AVERAGE

55(44)

21(17)

2.871

1.203

Performance in Terms of Service Delivery

Frequency

(Agree)

Frequency

(Disagree)

Mean

Std. Dev

The suggestion box is easily accessible to patients

60(48)

40(32)

3.726

1.321

On average, patients are always satisfied with the

hospital services

51(41)

49(39)

4.238

1.421

Action is always taken on feedbacks from the

suggestion box

76(61)

24(19)

2.416

1.232

On average, patients do get the prescribed drugs in

the hospital pharmacy

87(70)

13(10)

2.118

1.279

AVERAGE

69(55)

31(25)

3.125

1.313

Source: Survey Data (2025)

Table 3.1b shows that majority of Siaya County public hospitals believe that operational performance of public

hospitals in Siaya County is good. Specifically, operational efficiency had a mean response of 2.871 within the

range of 2.628 ≤ µ ≤ 3.007 at 55% (S.D=1.203). This implies that 55% of the respondents in Siaya County public

hospitals do agree that operational efficiency was considerably good. In addition, service delivery was also

considerably good at a mean response of 2.522 within the range of 2.118 ≤ µ ≤ 2.726 at 69% (S.D=1.313).

Inferential Statistics

Hypothesis stated that there is no significant statistical effect of Just-In-Time Inventory Management Practice

on operational performance of public hospitals in Siaya County. The effect of Just-In-Time Inventory

Management Practice on operational performance of public hospitals in Siaya County was investigated through

linear regression analysis using the model in equation 3.0:

1110



 XP

……………………………………………………………Eq. (3.0)

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where P is operational performance, while β

is the intercept (a constant), β

, is the slope associated to the

independent variables X

, and ε is the error term which is assumed to be independent, identical normally

distributed random variable with a zero mean and a constant variance.

Table 3.2 Model Summary

Model

R Square

Adjusted R Square

Std. Error of the Estimate

.889

.791

.784

.025

Source: Survey Data (2025)

The Model Summary table 3.2 gives the R square (0.791) and Adjusted R square (0.784). Thus, in this model,

Just-In-Time Inventory Management Practice is predicting 79.1% of the variance in operational performance of

public hospitals in Siaya County. This leaves 29.9% of the variation in operational performance of public

hospitals in Siaya County being explained by the error-term or other variables other than Just-In-Time Inventory

Management Practice. This finding also indicated the model’s goodness of fit as exemplified by the coefficient

of determination value of (R

value) of 0.791 adjusted to of 0.784. The standard error of the estimate, of 0.025

being a measure of standard deviation around the fitted line suggests that about 95% of the prediction error in

Just-In-Time Inventory Management Practice - operational performance model of public hospitals in Siaya

County is less than ±1.96 (0.046) = 0.041.

Table 3.3 ANOVA

Model

F-Change

Sig.F-Change

Durbin-Watson

Regression

9.576

0.002

1.823

Residual

Total

Source: Survey Data (2025)

The ANOVA table 3.3 shows that the computed F-statistic was 9.576, with an observed significance level (p-

value) of 0.002 which was also less than p<0. 05. This shows that the significance can be extended to 99.99%

confidence interval. The independence of residuals in this model was analyzed using Durbin-Watson statistic.

Considering a Durbin-Watson statistic of 1.823, it was deduced that there was no serial correlation of the

residuals as the values were within the accepted threshold of between 1.5 to 2.5 as was recommended by Hayes,

(2013).

Table 3.4: Regression Coefficients of Just-In-Time Inventory Management Practice and Operational

Performance

Model

Unstandardized Coefficients

Stand. Coef.

Sig.

Std. Er.

Beta

1(Constant)

Just-In-Time Inventory Management

Practice

2.473

1.031

2.809

.002

.738

.173

.704

4.271

.001

0.889

R-squared

0.791

Adjusted R-squared

0.784

F-statistics

9.576

Prob(F-statistics)

0.002

Source: Survey Data (2025)

The findings of this study indicate that Just-in-Time (JIT) inventory practices have a statistically significant

positive effect on operational performance in public hospitals in Siaya County (β = 0.738; p < 0.05). The

regression model further shows that JIT practices explain approximately 79.1% of the variation in hospital

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operational performance (R² = 0.791). This result suggests that supply chain responsiveness plays a substantial

role in shaping service delivery efficiency within public healthcare institutions.

These findings are consistent with earlier studies that have demonstrated the positive influence of responsive

inventory systems on organizational performance. For example, Isaksson and Seifert (2014) established that lean

inventory systems improve operational efficiency and financial performance by reducing excess inventory and

improving process coordination. Similarly, Rotimi, Bochie-Mensah, and Olubimmi (2016) found that supply

chain management practices significantly improved service delivery performance in healthcare institutions by

enhancing coordination between suppliers and health facilities.

Evidence from healthcare logistics research also suggests that reducing procurement lead times and improving

supplier responsiveness contributes to improved medicine availability and operational efficiency within hospitals

(John, 2016). This finding is consistent with studies showing that JIT implementation improves operational

performance through lower inventory levels, greater customer responsiveness, and better process coordination.

Evidence also indicates that JIT purchasing contributes positively to agility and operational outcomes,

particularly where supplier responsiveness is strong (Fullerton et al., 2001; Inman et al., 2011).

The findings are also aligned with studies conducted in manufacturing and service industries which demonstrate

that Just-in-Time practices reduce inventory holding costs while improving operational responsiveness. Suleman

(2018) found that firms implementing responsive inventory systems experienced improved operational

efficiency and reduced inventory costs. Likewise, Afnatu and Aseta (2018) reported that inventory management

practices, including responsive replenishment strategies, significantly enhanced organizational competitiveness

and performance among small enterprises. Although these studies were conducted outside the healthcare sector,

they support the argument that responsive inventory systems enhance operational outcomes across different

institutional contexts.

However, some studies have reported mixed findings regarding the effectiveness of Just-in-Time systems. For

instance, Hong et al. (2017) cautioned that maintaining extremely low inventory levels may expose organizations

to supply risks, particularly when suppliers fail to deliver materials promptly.

In healthcare settings where emergency demand can arise unexpectedly, excessive reliance on JIT may therefore

increase the risk of treatment delays if suppliers cannot guarantee immediate replenishment. This observation is

particularly relevant for public hospitals operating within constrained procurement environments. Similarly,

Raeeda et al. (2017) observed that while supply chain management practices improve healthcare service quality,

the magnitude of their impact may vary depending on the strength of supplier relationships and the availability

of reliable logistics systems. Weak coordination among healthcare supply chain actors may limit the

effectiveness of responsive inventory practices. The findings of this study therefore support the theoretical

propositions of the Resource-Based View (RBV) and Network Perspective Theory.

From an RBV perspective, Just-in-Time capability can be viewed as a strategic operational resource that

enhances institutional performance. RBV argues that organizations achieve superior performance when they

effectively deploy valuable operational capabilities and resources (Wernerfelt, 1984; Barney, 1991). In the

context of hospital supply chains, JIT systems represent an operational capability that improves inventory

efficiency, reduces procurement delays, and enhances service delivery outcomes.

At the same time, the findings also reinforce the relevance of Network Perspective Theory, which emphasizes

the importance of relationships among interconnected supply chain actors (Wasserman & Faust, 2014).

Healthcare supply chains involve multiple actors including hospitals, pharmaceutical suppliers, procurement

agencies, and regulatory institutions.

Effective implementation of Just-in-Time practices therefore depends not only on internal hospital capabilities

but also on the quality of coordination and information sharing across the healthcare supply network. Qualitative

insights obtained from respondents further highlight the practical complexities of implementing JIT within

healthcare environments. Several respondents indicated that certain medicines cannot be procured strictly

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through JIT because of their critical role in emergency treatment. Maintaining limited safety stock for such items

may therefore be necessary to mitigate supply risks. This observation supports the argument by Lysons and

Farrington (2016) that hybrid inventory strategies, combining responsive replenishment with strategic safety

stock, are often necessary in environments characterized by uncertain demand and supply constraints.

Overall, the results of this study suggest that strengthening supply chain responsiveness through improved

supplier coordination, shorter procurement lead times, and more flexible inventory systems can significantly

enhance operational performance within public hospitals. However, effective implementation of JIT practices

requires careful balancing between cost efficiency and service reliability to ensure uninterrupted availability of

essential medicines.

SUMMARY OF FINDINGS

The objective sought to establish the effect of Just-in-time practices on Operational performances of Public

hospitals in Siaya County. 87% (Mean 2.614: SD=.305) of the public hospital workers believe that Just-in-Time

inventory management practice influences the level of operational efficiency in public hospitals. 72% (Mean

2.236: SD=.236) of the public hospital workers believe that Just-in-Time inventory management practice

influences the level of service delivery in public hospitals.

Hypothesis three stated that there is no significant statistical effect of Just-in-Time practice on operational

performance of public hospitals in Siaya County. This was rejected based on the findings which showed that

just-in-time had a statistically significant effect on performance of public hospitals in Siaya County with a

coefficient of β=.738. This implies that, when keeping the effects of inventory categorization practice and

demand forecasting practice constant, a unit increase in Just-in-time practices would increase operational

performances of Public hospitals in Siaya County by 0.738 units.

CONCLUSION

The study sought to establish the effect of Just-in-time on operational performances of public hospitals in Siaya

County. The study examined the relationship between Just-in-Time inventory management practices and

operational performance in public hospitals in Siaya County, Kenya. The findings demonstrate that supply chain

responsiveness, operationalized through JIT practices, has a statistically significant positive influence on hospital

operational outcomes.

Hospitals that maintain flexible supplier relationships, shorter replenishment lead times, and coordinated

procurement systems are more likely to achieve improved medicine availability, reduced stock-outs, and

enhanced service delivery efficiency. However, effective implementation of JIT practices requires careful

consideration of healthcare supply chain uncertainties to avoid excessive reliance on minimal inventory levels.

Overall, strengthening supply chain responsiveness represents a critical strategy for improving operational

performance within public healthcare systems.

RECOMMENDATION

The study recommends that healthcare policy makers and hospital administrators strengthen supply chain

responsiveness through the following strategies:

i. Improve coordination between public hospitals and national pharmaceutical supply agencies;

ii. Introduce flexible procurement mechanisms that allow hospitals to respond rapidly to emergency

medicine demand;

iii. Invest in digital logistics management information systems to enhance supply chain visibility and

forecasting accuracy; and finally,

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iv. Develop hybrid inventory strategies combining Just-in-Time replenishment with strategic safety stock

for critical medicines.

These measures would enhance the reliability of pharmaceutical supply chains and improve healthcare service

delivery outcomes within Kenyan public hospitals.

Limitation

While this study provides important insights into the relationship between supply chain responsiveness and

hospital operational performance, a couple of limitations were observed when interpreting the findings:

First, the cross-sectional research design captures relationships between variables at a single point in time and

therefore does not permit strong causal inference. Future research may benefit from longitudinal designs that

examine how supply chain responsiveness influences hospital performance over time.

Second, the study relies largely on perceptual responses obtained from hospital personnel rather than objective

operational performance data such as stock-out frequency, procurement lead-time statistics, or patient wait-time

records. Incorporating such objective indicators would provide stronger empirical evidence for evaluating supply

chain performance.

Third, the regression model included a single predictor variable representing Just-in-Time inventory practices.

While the results demonstrate a strong association between JIT responsiveness and operational performance,

other contextual factors, such as government funding delays, hospital size, procurement policies, and

infrastructure capacity, may also influence hospital performance outcomes.

Future research should therefore consider more comprehensive models incorporating these additional variables

to provide a more holistic understanding of healthcare supply chain performance.

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