Data driven decisions for resilient and optimized supply chains

Connecting.Works has a partnership with Ab Ovo in the field of advanced data analytics to optimize supply chains.
Through this partnership we help organizations optimize and strengthen their supply chain using data, analytics and extensive supply chain knowledge.

Current global development forces organisations to rethink their supply chain. Covid-19, increased trade barriers and higher trade and geopolitical tensions take a heavy toll on growth, including the disruption of supply chains . Supply networks designed for low-cost and minimal inventory pose a major risk and refocus on resilience is required . Furthermore, sustainability and digitization are important topics for boards of directors transform to an efficient, digital and sustainable organisation.

We at Ab Ovo believe that leveraging the power of data and translating information to actionable insights helps organisations to become data driven, optimize and improve supply chain resilience.

While every supply chain is unique, we have created a magnitude of 50+ supply chain use cases in order to help supply chains in becoming data driven. Proven use-cases with measurable results. Our use-cases include predictive maintenance, real-time scheduling and self-learning demand planning. With the combination of the emerging cloud technology, extensive business knowledge and larger sets of supply chain data value driven analytics is within reach.

To help organisations start or expand the usage of data analytics in advanced decision-making, Ab Ovo delivers end-to-end supply chain analytics.

Supply Chain Analytics
There are multiple ways of delivering analytics, tailored to the situation at hand different project methodologies and technologies are applied. For large organizations with huge planning processes and issues, it develops and delivers corporate advanced planning & scheduling solutions (APS). These solutions facilitate 100s of planners with daily data driven decision making.

In the last few years, however, analytics have become more widely available through emerging technologies like big data, cloud technologies and analytical languages like Python or R. This standardized, partly open-source knowledge paves the way for a truly value driven/agile way of implementing advanced analytics. First results can be achieved in weeks, full use case implementation is a matter of around 3 months. Never have analytics been so tangible leading to concrete business cases and measurable results. This is what Ab Ovo means with Supply Chain Analytics.

Supply chain optimization at Ab Ovo

We have specifically chosen for analytics in the supply chain because:

  1. We at Ab Ovo have >20 years of experience in implementing decision support tools in this sector and is undisputed subject-matter expert.
  2. Our experts and teams, >100 econometricians, have extensive business knowledge about operational processes in manufacturing/rail/logistics/aviation and maritime.
  3. Global developments force companies to rethink their supply chains and although companies have marketing and finance analytics already quite successful implemented, companies are struggling to get value out of models and algorithms to optimize their supply chains.

Data science as an all-purpose word mostly comes down to what we call “predictive analytics”. Predictive analytics is using data to recognize patterns in order to predict what likely will happen. This in itself can be beneficial but we strongly believe that every prediction in a supply chain lands in a decision process with a lot of bottlenecks and constraints.

For example, in marketing analytics we predict that a certain customer is interested in product A. This prediction can be quite easily be translated into the decision of showing an advertisement of product A. In a supply chain we can also predict that a certain resource (e.g. a train, truck or an airplane) will need maintenance (this is what we call predictive maintenance). However, in order to schedule this triggered maintenance alert, we must find different available resources (workshop/people) and plan the logistics/down time to optimize downtime, service planning and total cost of maintenance.

Hence, a prediction in itself is difficult to implement in a supply chain. We also need analytics to suggest actions based on these predictions. This is what Ab Ovo calls “prescriptive analytics”. And the synergy between predictive and prescriptive analytics is where the true analytical value lies for supply chains. The combination is what is needed to perform business process analytics.

Business process analytics @ Ab Ovo

From experience we know that successfully implementing business process analytics also needs translators and embedding in the existing organization, IT and processes. It is crucial to analyse the whole business process in order to connect the predictive and prescriptive analytics back to the decision makers. Two worlds with their own “language”, so almost literal translation is a necessity.

On top of that, having a good fit with the current processes and technology landscape is required to embed the analytics into the organization. Many analytics companies underestimate the embedding process since normally the prediction model outcomes can already be beneficial. We therefore include change and stakeholder management into the implementation projects of analytics in order to ensure a good fit and a complete embedment.

Use cases
Business process analytics can be achieved by a wide range of use cases. We have implemented many use cases that combine different analytics and technologies in the last 20 years. We love working on complex puzzles in the field of, for example workforce rostering, advanced demand forecasting or building an algorithm for contract evaluation in the childcare industry.

This year we also implemented an optimization algorithm for Lufthansa Cargo. In it, based on predicted shipments, we optimize the handling of shipments at airports by allocating the best working stations for each of the daily 70.000 shipments. With this automated way of using algorithms we see an estimated handling efficiency increase of around 12% which leads to an average throughput time decrease of around 8%. Customer service levels go up while handling and storage costs go down. Data driven decisions in the aviation sector prove their value here.

Example supply chain analytics use cases @ Ab Ovo

About Ab Ovo & Connecting.Works

With the exclusive partnership between Ab Ovo and Connecting.Works, in-depth industry expertise is combined with advanced supply chain analytics and delivered end-to-end to organisations in Europe.

Ab Ovo, headquartered in Rotterdam, has been delivering advanced analytics in the field of supply chain to their customers for over 20 years. Their customer base is Europe based (and to some extend worldwide) and are mostly active in land logistics/aviation/maritime/rail and manufacturing and include KLM, Deutsche Bahn, Stork, Transavia, Maersk, Vopak, Wallenius Wilhelmsen and Lufthansa.

Connecting.Works is a fast-growing collective of strong individuals who have founded and scaled companies, in all sectors and around the world. Entrepreneurs, investors, futurists, thinkers and makers. Together, they ignite growth within companies and bring relevant knowledge and expertise in the fields of Integrated Supply Chain, Digital Technology, Procurement and E-Commerce to organisations. Because they do what they really love: working with their customers and prepare them for the future. Connecting.Works only collaborate with colleagues who have experience at the highest level to work on challenging projects with powerful customers.

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