| dc.description.abstract | Predicting business operations is a critical task for small and medium enterprises (SMEs).
With increased unpredictability in the business environment, small enterprises find
themselves in the receiving end simply because they do not have the tools in decision-making
like their counterparts who have established business decision-making tools. With increased
use of ICT, SMEs can now tap into the power of data to support decision-making.
Transactional data like sales, purchases, payments, service requests, invoices, purchase
orders, delivery notes, repairs, and maintenance notes are collected by SMEs and are readily
available. Currently, SMEs in Kenya have limited on non-existent quantitative forecasting
systems in place, predictive analytics is a preserve of well-funded, large, well-established or
international companies. SME owners can now benefit from the power of predictive
analytics in areas like sales, purchases, business lead generation, recommendations systems,
and risk prediction. With predictive analytics, SME owners can have added confidence in
decision-making to help propel their business to successful ventures. Predictive analytics
algorithms like clustering algorithms, linear regression, and classification algorithms can be
used to aid SME owners and managers gain insights into their business by identifying
relationships and associations between the various variables in their business or identification
of trends. SMEs in Kenya, have in the past run out of business due to wrong decisions
associated with lack of information regarding their customers, their business, or the business
sector as a general. SMEs' contribution to Kenya’s GDP growth is vital and the use of
technology and ICT could mitigate the challenge of access to information that SMEs have.
The use of technology to predict business operations and performance is the next frontier in
ensuring business sustainability, job security, and a good business environment. This research
aimed to solve this information gap by designing a demand and supply forecasting model that
equips SME owners and managers with insightful information about the demand and supply
of the products they are selling, helping them make informed decisions based on their sales
and purchase data. The study was carried out on five (5) SMEs in Nairobi and Muranga who
had access to ICT infrastructure and had some knowledge of digital book-keeping methods
but had no forecasting or prediction systems in place. A fully functional web passed demand
and supply forecasting model accessible via a browser was designed. A beta test was
conducted by the five respondents with positive feedback. The majority of the respondents, as
the study found out, derived great value from forecasting their demand and supply and were
able to stock right and meet their clients’ needs better, the forecasting model was developed
using the agile prototyping method of software development with cascading style sheets
(CSS), hyper-text markup language (HTML), react graphical user interface and an R based
forecasting engine. The designed system allowed the users to interact with a user-friendly
graphical user interface on either a mobile device or a computer allowing more freedom and
flexibility in accessing the platform. It allowed SMEs to upload their sales and purchase data
in a predefined format that the forecasting model consumes and provide accurate demand and
supply forecasts and provided forecasting accuracies in the 95% quartiles for all the SMEs
sampled. The model was evaluated by the same SMEs with 100% of them indicating that
they would be relying on the forecasting model for all future forecasts. The designed model
allows SMEs to benefit from the demand and supply forecasts irrespective of its sector and
the type of enterprise engages in. | en_US |
