4 pitfalls to avoid while deploying supply chain analytics
The consequence of efficient supply chain management activities and strategic corporate investments are directed towards procuring, developing, and configuring the necessary resources, procedures, and KPIs.
In The Thinking Supply Chain, IDC’s Simon Ellis identifies the five “Cs” of the effective supply chain analytics of the future:
Key features of effective supply chain optimization include:
All businesses require processes to help them succeed daily and to prosper in the long run. A supply chain analytics can be a valuable system for firms with complex supply chains since it simplifies daily operations and relieves administrative stress. Further, a seamless process can also help the firms with more time and space to make better decisions rather than being caught up in the supply chain complexities.
A bad supply chain management system, on the other hand, can be costly in more ways than one.
Here are the 4 pitfalls to avoid while deploying supply chain analytics:
Change management practices
Transitioning from a silo-based business model to supply chain management necessitates drastic changes in organizational structures, cultures, and business strategy. There could be frequent bottlenecks and greater resistance to supply chain reforms if such changes are not adequately handled. As a result, supply chain transformation without well-planned change management may be counterproductive to supply chain projects.
The change management practices can help to train people on the basic analytics concepts. This further moves on to provide change management programs to encourage user adoption.
Failure to plan the right use cases
Executives must have a clear understanding of why and where they wish to use artificial intelligence. Knowing where the process fits can help the firm to solve its problems quickly without leaving any room for errors.
One advantage of being so laser-focused on specific areas is that you may get results quickly. In reality, the correct artificial intelligence can predict where a quick return on investment would occur.
The executives should create a short-term roadmap and then prioritize use cases accordingly
For example, they should also define the product owner, IT spoc, etc. for each use case. Planning the right use cases should also include conducting an audit post-implementation to estimate business impact.
The executives should also ensure prioritize use cases based on ROI, complexity, and organization readiness. This could lead the firms to be able to quantify and visualize the end benefits.
Changing a supply chain management system necessitates a significant financial, time, and human resource investment. Wasted labor, service redundancy, and missed deadlines will all result in large expenses if not done appropriately.
High-quality logistics providers usually do thorough research before implementing supply chain improvements to refrain from these costs that can be avoided.
Not planning for the future
Another common blunder that can cost your firm a lot of money is failing to plan for the future. Your supply chain strategy isn’t very useful if it can’t react to unanticipated changes and develop with your company’s needs.
You should have a plan in place to cope with supply chain disruptions. That way, if something goes wrong in your supply chain, you’ll be able to deal with it promptly and avoid or minimize the impact on your customers.
It’s also crucial to match the supply chain’s design to your company’s long-term objectives. You may find it difficult to adapt your supply chain management plan in the future if you solely consider your company’s current needs. Keep your long-term growth strategy in mind so that your supply chain is ready when your firm expands or introduces new products and services.
The majority of manufacturing companies are organized as networks of manufacturing and distribution facilities that buy raw materials, transform them into intermediate and completed goods, and then distribute them to clients. The most basic network is made up of a single location that does both manufacturing and distribution. More complicated networks need a more intricate and detail-driven supply chain analytics process.
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