BMO3419: Supply Chain Analytics Literature Review Report

This report delves into the critical field of Supply Chain Analytics (SCA), examining its role in optimizing business performance. It defines SCA as a network of parties involved in the transformation of raw materials to finished products, highlighting the use of data and analytics tools to make better decisions. The report explores the benefits of SCA, including cost reduction, improved decision-making, and enhanced customer satisfaction. It differentiates between descriptive, predictive, and prescriptive analytics, and discusses the roles of Material Requirements Planning (MRP) and Distribution Requirements Planning (DRP) in managing inventory and distribution. The report also analyzes the impacts of MRP and DRP on supply chain and firm performance, emphasizing their roles in integration, demand forecasting, and cost reduction. In conclusion, SCA tools are vital for decision making, inventory controls, production planning, and distribution to enhance supply chain efficiency. References include a range of journal articles, books, and academic sources published from 2010 onwards.

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Supply Chain Analytics
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Introduction
Supply chain refers to a network with various parties, like manufacturers that are involved in the
transformation process of raw materials to finished products. The supply chain covers all
activities from raw materials processing to distribution of finished goods to end consumers.
There is a flow of various resources within the supply chain, like information. Supply chain
analytics, therefore, uses the information shared in the supply chain to make better decisions. To
make a better decision, SCA adopts various tools and technologies. Better decisions help reduce
costs and risks in the supply chain as they result in the production of quality services. SCA
benefits include reduced marketing, improve the decision-making process, reduce inventory,
improve customer satisfaction, and promote visibility in the supply chain (Srinivasan, and
Swink, 2018).
Types of analytics
companies can apply descriptive, predictive, and prescription analytics to help make a better
decision. These types are
 Descriptive analytics- these are supply chain analytics that promotes visibility within the
supply chain. Graphic analytics help in the identification of problems and opportunities
prevailing in company processes and functions.
 Predictive analytics- these are SCA, which predict the future using mathematics
algorithms and programming. Predictive analytics, when applied, helps companies
understand the possible outcomes and future demands. Predictive helps the organization
detect and mitigate risks (Souza, 2014).

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 Prescription analytics-these are analytics used in problem-solving and collaboration.
They provide alternative decisions that organizations can use to solve problems. Also,
prescription facilitates cooperation with other partners, such as logistics. Collaboration
reduces the time and effort used in eliminating disruptions.
Apart from the three analytics, companies can apply cognitive analytics will enable then
communicate appropriately with others. Cognitive analytics help in problem-solving of complex
problems. Companies can also adopt cognitive technologies such as artificial intelligence in their
supply chain. Companies nowadays take supply chain analytics with goals of improving
collaboration, integration, and supply chain visibility (Trkman et al. 2010). Companies enhance
script collaboration among all parties in a company’s supply chain network. Supply chain parties
like manufacturers and retailers use SCA to collect information from the point of sale, global
positioning system, and radio frequency identification devices. Collaboration and visibility are
essential in supply chains as they help in the optimization of decisions like customer fulfillment,
inventory tracking, and automatic purchase. Companies apply supply chain analytics by making
decisions through data analyses using charts and graphs (Ittmann, 2015).
Material Requirements Planning vs. Distribution Requirement Planning
SCA consists of Material requirement planning and distribution requirement planning. MRP is a
computerized system concerned with production scheduling and inventory control. MRP also
refers to the method used by companies to calculate the inventory required to produce a certain
amount of products. The system requires a master production schedule and Bill of material data
to make decisions. MRP follows three steps evaluating the available inventory, identifying the
number of resources to add, and finally scheduling when to produce them or buy. The network

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receives customers' orders and their specifications. The system evaluates to make sure the
company has enough inventory to create products that meet the demands at the right time. Its
ability to predict future requirements determine MRP success, and the time to provide to meet
customer expectations. The main objective of MRP is to make sure that materials are available
when needed (Miclo et al. 2015).
DRP, on the other hand, is used to solve distribution problems as it controls all distribution
activities in the supply chain. DRP provides high service quality by comparing supply with
forecast and actual demand of the company. DRP improves distribution by using push and pull
methods (Erraoui et al. 2019). Pull delivery involves getting a customer order and moving the
products upward to meet their needs. Push distribution consists of sending the good down the
supply system. Pull methods are affected by the bullwhip effect because demands increase all the
time. DRP improves company performance by ensuring production efficiency, accuracy, and
order fulfillment within the stipulated time. DRP also enhances customer satisfaction as
customers receive goods at the right time (Rozados, and Tjahjono, 2014). Though DRP and MRP
help in planning different activities, they are operated differently. Some of these differences are
one; MRP is controlled by the firm while DRP is out of the firm's control. Another difference is
that MRP deals with inventory in the manufacturing process while DRP manages inventory after
manufacture by distributing the finished products (Erraoui et al. 2019).

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Impacts of MRP and DRP on performance
The two SCA tools have some impacts on supply chain and firm performance. MRP improves
company performance through the integration of supply chains. MRP involves the sharing of
information within the supply chain; therefore, it reduces bullwhip effects (Angolia et al. 2018).
MRP is used to forecast the company's future funding requirement by predicting demands and
inventory needs. MRP system is essential as it ensures the supply chain makes proper decisions
that reduce inefficiencies. DRP is useful in distribution planning and ensures the delivery of
customers' orders in time. DRP lowers costs like storage costs as it provides the distribution of
goods from warehouses. DRP has various impacts like fast decision making, promotion of
customer service, and deployment of demand forecast (Chae et al. 2014). Demand forecasts
make the company prepared to meet future needs by acquiring all the necessary materials.
Conclusion
SCA is very significant in improving company performance. SCA tools help in decision making,
inventory controls, production planning, and distribution. SCA enhances supply chain efficiency
by developing visibility and integration (Tiwari et al. 2018). There are four different analytics
that companies can apply to enhance performance. These analytics are descriptive, predictive,
prescription, and cognitive. This analytics helps improves performance through the identification
and mitigation of risks. The material requirement planning system enhances performance
through proper planning of inventory and production. MRP involves the identification of
customer needs, evaluation of stock, and production scheduling. DRP, on the other hand, is
concerned with push-pull delivery.

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References
Angolia, M.G., and Pagliari, L.R., 2018. Experiential Learning for Logistics and Supply Chain
Management Using an SAP ERP Software Simulation. Decision Sciences Journal of Innovative
Education, 16(2), pp.104-125.
Chae, B., Olson, D., and Sheu, C., 2014. The impact of supply chain analytics on operational
performance: a resource-based view. International Journal of Production Research, 52(16),
pp.4695-4710.
Erraoui, Y., Charkaoui, A., and Echchatbi, A., 2019. Demand-Driven DRP: Assessment of a
New Approach to Distribution. International Journal of Supply and Operations Management,
6(1), pp.1-10.
Ittmann, H.W., 2015. The impact of big data and business analytics on supply chain
management. Journal of Transport and Supply Chain Management, 9(1), pp.1-9.
Miclo, R., Fontanili, F., Lauras, M., Lamothe, J., and Milian, B., 2015, October. MRP vs.
demand-driven MRP: Towards an objective comparison. In 2015 International Conference on
Industrial Engineering and Systems Management (IESM) (pp. 1072-1080). IEEE.
Rozados, I.V., and Tjahjono, B., 2014, December. Big data analytics in supply chain
management: Trends and related research. In 6th International Conference on Operations and
Supply Chain Management, Bali.
Souza, G.C., 2014. Supply chain analytics. Business Horizons, 57(5), pp.595-605.
Srinivasan, R., and Swink, M., 2018. An investigation of visibility and flexibility as
complements to supply chain analytics: An organizational information processing theory
perspective: production and Operations Management, 27(10), pp.1849-1867.

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Tiwari, S., Wee, H.M., and Daryanto, Y., 2018. Big data analytics in supply chain management
between 2010 and 2016: Insights to industries. Computers & Industrial Engineering, 115,
pp.319-330.
Trkman, P., McCormack, K., De Oliveira, M.P.V. and Ladeira, M.B., 2010. The impact of
business analytics on supply chain performance. Decision Support Systems, 49(3), pp.318-327.