S&OP EVOLTION 2
S&OP Evolution at Adtran
Headquartered in Huntsville, Alabama, Adtran manufactures and designs telecom
components (Simchi-Levi et al., 2021). Adtran faced several challenges that eventually
contributed to excessive inventory and declining customer satisfaction. Some factors
contributing to Adtran’s problems were long supply lead times, engineering changes, and short
product life cycles. Forecasting was also a significant challenge for Adtran; some product
forecasts were less than 50% accurate (Simchi-Levi et al., 2021). A lack of effective
interdepartmental communication and Information Technology (IT) led to poor forecasting.
Dadmun created and executed a plan to form an effective Sales and Operation Plan (S&OP)
process to address Adtran's challenges in three phases.
Major Supply Chain Challenges Aided by IT
The four major goals of an effective supply chain IT system should address product
information availability, data access, supply chain partner collaboration, and total supply chain-
informed decision-making (Simchi-Levi et al., 2021). IT systems can significantly improve
processes throughout the supply chain and its sustainability (Schilling & Seuring, 2022). Product
information should be collected at various points in the supply chain, from production to sale.
That information or data should be accessible from a single source that is up to date. Having a
single source for data can avoid confusion and decision-making based on old and irrelevant data.
Organizations should also collaborate with external business partners to optimize the supply
chain and minimize uncertainty. Managers can then use the information, data, and partner input
to make more informed decisions based on the total supply chain (Simchi et al., 2021).
S&OP EVOLTION 3
Supply Chain Management
Sharing data across the supply chain is essential for minimizing the impact of market
volatility, complexity, and uncertainty (Gopal et al., 2022). As Supply Chain Management, IT
systems support corresponding business processes. Simchi-Levi et al. (2021) differentiate
business processes into four categories. Level one processes are typically independent systems
prone to creating redundant data and require manual decision processing. Level two processes
have improved data visibility and include adding planning tools, algorithms, and statistical
forecasting methods. Level three processes improve data visibility that can be shared throughout
the supply chain. Level four processes and data are shared across platforms through multi-
enterprise integration and promote improved collaboration across the supply chain.
AI and the Supply Chain
As supply chains modernize and become more complex, it becomes increasingly difficult
to efficiently manage with planning tools and statistical models as they still require human input
(Yan et al., 2019). Artificial intelligence (AI) has risen in popularity over the years as a tool to
simplify and automate processes traditionally performed by a human. Researchers found that
adding AI to supply chain management significantly improved efficiency and sustainability
(Wang, 2022). AI is best described as computer programs that can learn and reason
independently (Simchi-Levi et al., 2021). AI programs can make decisions based on the available
data, statistics, and algorithms. Because AI has these abilities, it can be applied to several areas
of the supply chain, including forecasting, planning, and calculating risk.
S&OP EVOLTION 4
Supply Chain Excellence Comparison
Figure 16-1 encompasses several parameters that are required to achieve supply chain
excellence. Organizations must understand that supply chain efficiency determines their
competitiveness in the market. They must manage their resources, suppliers, and capacity to
deliver their products to customers at a lower cost and greater speed than their competitors
(Salam & Khan, 2018). Figure 16-10 is used when considering factors such as decision focus,
data aggregation level, implementation time, and the number of users involved. The chart is
outlined in four major sections: strategic network design, tactical planning, operational planning,
and operational execution (Simchi-Levi et al., 2021). The chart illustrates that all parts of the
supply chain are intertwined, and decisions made in one area will inherently affect another. For
example, simple supply chain planning will increase the return on investment (ROI) and lengthen
the planning horizon.
Conclusion
Adtran’s challenges stemmed from a lack of proper IT systems, leading to poor customer
satisfaction and high inventory stocking levels. Adtran addressed these issues by introducing an
S&OP system to improve supply chain efficiency, forecasting, and communication. The
implementation of IT in supply chain management has significantly improved its efficiency. The
introduction of machine learning and AI has made further improvements in how firms can
manage supply chains that have grown increasingly complex. As technology continues to
improve, so will AI. There is a high likelihood that AI systems will become the norm in supply
chain systems going forward.
S&OP EVOLTION 5
References
Gopal, P., Kadari, P., Thakkar, J. J., & Mawandiya, B. K. (2022). Key performance factors for
integration of industry 4.0 and sustainable supply chains: A perspective of Indian
manufacturing industry. Journal of Science and Technology Policy Management,
https://doi.org/10.1108/JSTPM-10-2021-0151
Salam, M. A., & Khan, S. A. (2018). Achieving supply chain excellence through supplier
management: A case study of fast-moving consumer goods. Benchmarking: An
International Journal, 25(9), 4084-4102. https://doi.org/10.1108/BIJ-02-2018-0042
Schilling, L., & Seuring, S. (2022). Sustainable value creation through information technology-
enabled supply chains in emerging markets. The International Journal of Logistics
Management, 33(3), 1001-1016. https://doi.org/10.1108/IJLM-04-2021-0206
Simchi-Levi, D., Kaminsky, P., & Simchi-Levi, E. (2021).LDesigning and managing the supply
chain: Concepts, strategies and case studiesL(4th ed.). New York, NY: Richard D. Irwin,
Inc.
Wang, H. (2022). Linking AI supply chain strength to sustainable development and innovation:
A country‐level analysis. Expert Systems. https://doi.org/10.1111/exsy.12973
Yan, W., & He, J. (2019). Risk-aware supply chain intelligence: AI-enabled supply chain and
logistics management considering risk mitigation. Advanced Engineering Informatics, 42,
100976. https://doi.org/10.1016/j.aei.2019.100976
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