Page 19 - Logistics News - Issue 02 - 2024
P. 19

S U PPLY C H A I N I T / A I




          •  Present guidelines (in person or document) – your   Final shortlist – getting to the  nal decision
           selection process, specific focus, etc.            •  In writing, advise each of the shortlisted vendors of where
          •  Ensure project lead availability to provide answers to   you need clarification on any specific key area(s) they did
           their questions throughout.                         not cover well. Mention any other concerns.
          • Each vendor then returns to deliver on your guidelines:  •  Ask each vendor for suggested reference customers
             •  RFP or proposal addressing your needs, plus    (two or three) you could call or visit.
              adding their opportunities for you.             •  Vendor returns to present, demo and/or discuss
             •  Business presentation and software demo        clarification on feedback to them.
              according to your guidelines.                   •  Perform reference visit(s). Tip: speak to a customer
             •  Give them time to show what they believe they   who had issues regarding how fix went.
              offer – their differentiation.                  • Final commercial discussion/negotiation.
             •  Beware, functional shootouts don’t help – must   • Agreement and acceptance signing.
              include your relevant focus.
             •  Check their culture matches yours – meet their   And finally …
              people on your project.                         Successful new business IT selection requires the above
             •  Check budget/price for software, services,    process to be made appropriate for your project scale
              maintenance over five years for TCO.            and considers your future likely strategic roadmap as
         •  Review all. Some will be easy to remove, others not so,   well as the IT vendors you already deal with. Is this new
           but you must reduce the list.                      IT or extending existing? Good luck! •

          From big to better data: putting



          AI trends into practice





                                        By Tuna Yemisci, Regional Director Middle East, Africa & East Med at Qlik


          Organisations have traditionally valued volume, velocity and variety as the classic drivers of ‘big
          data’. However, as we start to explore the promise of increased efficiency and creativity from
          generative AI, we must also address the remaining two ‘vs’ – the value and validity of our data.


               ow can we trust all our data in the generative artificial   A hybrid AI scenario can offer the best of both
               intelligence (AI) economy? While trends in AI growth and   worlds: the predictability of traditional AI tools and the
         H its applications have taken centre stage, it is important   flexibility, scalability and adaptability of generative AI
         to define a roadmap to putting these trends into practice across   models.
         the organisation. In this way, it holds the potential to usher in a
         new era of productivity and prosperity.                 Don’t, however, become overly distracted by generative AI.
                                                              Any initiative should be rooted in real-life business challenges,
            Let’s look at some of these trends and how best to   while aligning with, and amplifying, any ongoing data and
         incorporate them into our business practices:        analytics efforts.

         1. Moving from historic to predictive analytics      2. Empowering non-technical workers
         Thinking that generative AI will replace all previous AI tools   Generative AI plays into the hands of individuals who want
         would be a mistake. Traditional AI has the potential to bridge   answers fast, but do not have the time or skills to perform
         the maturity gap in an organisation’s generative AI strategy,   analyses. As such, we will see an increase in auto-generated
         especially in well-established use cases such as fraud analytics   visualisations and insights, enhanced with explanations in
         and churn analysis.                                  natural language.

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