Lokad.Cloud - O/C mapper for Azure

Every single week, a number of companies contact us asking whether Lokad could take care of their rolling weekly or monthly forecasts, say a few quarters ahead.

A few days ago, a prospect raised several sharp questions concerning the applicability of the Quantitative Supply Chain perspective to address the supply chain challenges as faced by many large manufacturing companies.

Every single SKU calls for mundane daily decisions, such as moving in more stock or changing the underlying price tag.

Supply chains are complex systems, possibly among the most complex ones ever engineered by mankind, encompassing people (many), machines (diverse) and software (tons).

Supply chains are complex systems, possibly among the most complex ones ever engineered by mankind, encompassing people (many), machines (diverse) and software (tons).

Markus Leopoldseder (Director of Knowledge - Global Manufacturing and Supply Chain Practice at McKinsey) raised two relevant questions concerning the applicability of Differentiable Programming (DP) for supply chain purposes.

Markus Leopoldseder (Director of Knowledge - Global Manufacturing and Supply Chain Practice at McKinsey) raised two relevant questions concerning the applicability of Differentiable Programming (DP) for supply chain purposes.

We are proud to announce the immediate availability of the Lokad private beta for differentiable programming intended for quantitative supply chain optimization. Differentiable programming is the descendent of deep learning, and represents the ... [More] convergence of two algorithmic fields: machine learning and numerical optimization. Differentiable programming unlocks a series of supply chain scenarios that were seen as largely intractable: joint optimization of prices and stocks, loyalty-driven assortment optimization, forecasting demand for non-standard products (e.g. precious stones, artworks), large scale multi-echelon flow optimization, many-channel joint optimization, stock optimization under partially incorrect electronic stock values, large scale flow maximization under many constraints, etc. For many other scenarios that were already approachable with alternative methods, differentiable programming delivers superior numerical results with only a fraction of the overhead, both in terms of data scientists’ efforts and computational resources. Application to Supply Chains At its core, Differentiable Programming (DP) offers a path to unify problems that have remained disconnected for too long and resolves them jointly: assortment, pricing, forecasting, planning, merchandising. While such unification may seem unrealistically ambitious, the reality is that companies are already applying an insane amount of duct-tape to their own processes to cope with the endless problems generated by the fact that those challenges have been siloed within the organization in the first place. For example, pricing obviously impacts the demand and yet both planning and forecasting are nearly always performed while ignoring prices altogether. [Less]

We are proud to announce the immediate availability of the Lokad private beta for differentiable programming intended for quantitative supply chain optimization. Differentiable programming is the descendent of deep learning, and represents the ... [More] convergence of two algorithmic fields: machine learning and numerical optimization. Differentiable programming unlocks a series of supply chain scenarios that were seen as largely intractable: joint optimization of prices and stocks, loyalty-driven assortment optimization, forecasting demand for non-standard products (e.g. precious stones, artworks), large scale multi-echelon flow optimization, many-channel joint optimization, stock optimization under partially incorrect electronic stock values, large scale flow maximization under many constraints, etc. For many other scenarios that were already approachable with alternative methods, differentiable programming delivers superior numerical results with only a fraction of the overhead, both in terms of data scientists’ efforts and computational resources. Application to Supply Chains At its core, Differentiable Programming (DP) offers a path to unify problems that have remained disconnected for too long and resolves them jointly: assortment, pricing, forecasting, planning, merchandising. While such unification may seem unrealistically ambitious, the reality is that companies are already applying an insane amount of duct-tape to their own processes to cope with the endless problems generated by the fact that those challenges have been siloed within the organization in the first place. For example, pricing obviously impacts the demand and yet both planning and forecasting are nearly always performed while ignoring prices altogether. [Less]

DDMRP stands for Demand Driven Material Requirements Planning. In the last few years, the popularity of DDMRP has been growing in certain industries; occupying the niche that lean manufacturing or six sigma used to occupy. Yet, what can really be ... [More] expected from DDMRP and how much novelty does it bring to the table as far as supply chain optimization is concerned? In order to address this question, let’s review DDMRP from a numerical perspective, i.e. looking at DDMRP as a set of numerical recipes1 to deliver a measurable performance optimization of a given supply chain. Indeed, as all the benefits put forward by the authors of DDMRP are all quantified targets (ex: achieve 97-100% on time fill rate performance2), it seems fair to adopt a numerical stance to assess the merits of this approach. The authors behind DDMRP state that this approach brings four key innovations to supply chain optimization, namely: decoupling the lead times3 the net flow equation4 the decoupled explosions5 the relative priority6 Jumping to conclusions, the careful review of each of those points - done in greater details in the following - indicates that there is very little substance to the bold claims of DDMRP. The numerical recipes proposed by DDMRP would not even have been considered state-of-the-art by the end of 1950’s as the nascent field of operations research had already uncovered arguably more sophisticated and better numerical optimization strategies at the time. The improvements claimed to be achieved by DDMRP start with a wrong baseline: MRPs - just like ERP - are typically not delivering any numerical optimization capabilities7. Their underlying relational database systems are simply unsuitable to carrying any sizeable data crunching workload, even when considering modern computing hardware. Thus, despite the discourse of many enterprise software vendors - operating in the transactional side of the problem - it is incorrect to take MRPs as a baseline as far as supply chain optimization is concerned. [Less]