Over 50% of Companies Unable to Report Their Full Supply Chain

Supply Chain Weaknesses Causing Havoc in the Food, Pharmaceutical, and Consumer Goods Market

By Genevieve Robinson on Feb 25, 2021

Editor Note: Understanding the importance of this timely topic and to ensure that research is made available to the wider academic community, IGI Global has made a sample of related articles and chapters complimentary to access. View the end of this article to freely access this critical research.

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As you know, when you order products online, or shop for necessities, you may notice longer wait times, the rising costs of meat and eggs at the grocery store, increased prices on raw materials, as well as news reports on the challenges of distributing the Covid-19 vaccine. The Covid-19 shutdowns and limitations have disrupted suppliers, the availability of raw materials, and specialized workforces: thus affecting businesses, supply chains, and their ability to provide goods and services. According to a report commissioned by The Association for Supply Chain Management (ASCM), USA, 50% of publicly-listed retail, pharmaceuticals, and consumer electronic companies reported that they do not have a clean end-to-end picture of their supply chains, showcasing the serve limitation of businesses being able to implement emergency protocols.

Additionally, Mr. Andrew DiEugenio, General Transportation Manager at Walmart, mentions how the pandemic affected typical consumer shopping practices, which in turn required adjustments to distribution schedule causing further modification to businesses’ supply chains and management.

In response to these challenges and changes, businesses are implementing AI and automation and outsourcing their supply chains. Additionally, international governments are passing legislation that mandates industries to review their supply chains to produce a longer-term plan to prevent future supply-chain issues. Understanding the current challenges and importance of supply chain management, Prof. Nenad Stefanovic, from the University of Kragujevac, Serbia, explains how big data can be implemented in strengthening supply chain management in his chapter, “Big Data Analytics in Supply Chain Management” sourced from the Encyclopedia of Organizational Knowledge, Administration, and Technology (IGI Global).

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Big data analytics uncovers patterns in a wide variety of data and associates the patterns with business outcomes. Analysts use analytical techniques and tools to detect unusual, interesting, previously unknown, or new patterns in data. Big data is a result of interaction of four dimensions of scale (increasing data volumes, high velocity of data creation, increasing complexity of data types, and extreme time sensitivity of data diminishing its value if not treated at that moment) thereby posing different challenges to manage, not to mention applying analytics techniques to find new insights. Big data does not behave the same as other data. The challenges associated with analytics on big data require a different approach from traditional data analytics processes (Nguyen et al., 2018). Big Data Analytics in Supply Chain Management 2448 Big data analytics has to do more with ideas, question and value, than with technology. Therefore, the big data analytics methodology is a combination of sequential execution of tasks in certain phases and highly iterative execution steps in certain phases.

The big data analytics process lifecycle is a combination of sequential execution of tasks in certain phases and highly iterative execution steps in certain phases. Because of the scale issue associated with supply chain big data system, an incremental and agile approach is recommended, which include modifying and expanding processes gradually across several activities as opposed to designing a system all at once (Mohanty, 2013).

In other words, Jefferson is explaining that the object of hate – enslaved Africans – must be controlled and restricted by exerting educational, political and economic prejudice and bigotry or else the fear is that the person who is hated will one day take over and destroy the one who hates. This reasoning might help explain why homegrown terrorism became an extension of the personal hatred, racial prejudice, and bigotry stoked by the fear of reprisals.

In this section, an agile, iterative big data analytical process model to deliver supply chain predictive analytics solutions and intelligent applications efficiently is presented. The complete process model should encompass the lifecycle (phases, tasks, and workflows), roles, infrastructure, tools, and artifacts produced.

The proposed model is comprised of the following key components:

  • A big data lifecycle.
  • A standardized project structure.
  • Infrastructure and resources for big data projects.
  • Tools and utilities for project execution.

Figure 1 shows a proposed analytical lifecycle that can used to structure and execute various big data analytics projects (Ericson et al., 2017).

The lifecycle outlines the steps, from start to finish, that projects usually follow when they are executed. The process model includes the following stages that analytical projects typically execute, often iteratively:

  1. Business understanding
  2. Data acquisition and understanding
  3. Modeling
  4. Deployment
  5. Customer acceptance

The lifecycle defines goals, tasks, and documentation artifacts for each stage of the lifecycle. These tasks and artifacts are associated with project roles such as: project manager, solution architect, data scientist, project lead, IT manager, business analyst, DevOps specialist, application developer, tester, etc.

The goal of the business understanding phase is to specify the key variables that are to serve as the model targets and whose related metrics are used determine the success of the project, and to identify the relevant data sources that the supply chain has access to or needs to obtain.

The second phase includes the three main tasks:

  • Data ingestion from various supply chain data sources into the target analytic environment.
  • Data exploration in order to determine if the data quality is adequate to answer the question.
  • Solution architecture development of the data pipeline that refreshes and scores the data regularly.

The modeling phase addresses the following tasks:

  • Feature engineering - Create data features from the raw data to facilitate model training.
  • Model training - Find the model that answers the question most accurately by comparing their success metrics.
  • Model evaluation - Determine if the model is suitable for production.

Deployment phase refers to deploying the models with a data pipeline to a production or productionlike environment for final user acceptance and application usage. The final phase includes system validation (confirming that the deployed model and pipeline meet the customer’s needs) and project delivery (hand the project off to the entity that’s going to run the system in production.

The model also includes the standardized project structure so that projects share a common directory structure and use templates for project documents. This makes it easy for the team members to find information about their projects. All code and documents are stored in a version control system to enable more effective and efficient team collaboration. Tracking tasks and features in an agile project tracking system allows closer tracking of the code for individual features. The standardized structure for all projects helps build institutional knowledge across the supply chain.

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