PurposeThis　study　explores　the　interaction　between　operations　management　and　information　systems　by　applying　the　Design　Science　Research　(DSR)　methodology　for　intelligent　early　fault　management.　Prior　research　primarily　addressed　post-fault　identification　and　classification　but　has　struggled　with　catastrophic　forgetting.　Thus,　this　work　proposes　an　innovative　data-driven　artifact　that　leverages　a　deep　learning　(DL)-based　approach　for　early　fault　detection　and　future　fault　forecasting.Design/methodology/approachFollowing　the　DSR　methodology,　the　work　proposes　an　innovative　data-driven　artifact　for　early　fault　management.　The　proposed　artifact　extracts　key　features　from　industrial　sensor　data　in　real　time　using　a　Deep　Sparse　Autoencoder　with　a　sparsity　penalty.　These　features　are　then　processed　using　an　Exponentially　Weighted　Moving　Average　method　for　monitoring　process　variations,　while　a　Transformer-based　Neural　Network　forecasts　potential　faults.　To　mitigate　catastrophic　forgetting,　the　Elastic　Weight　Consolidation　technique　is　applied　during　offline　training　to　preserve　previous　patterns　when　new　information　becomes　available.FindingsThe　artifact　enhances　operational　decision-making　by　generating　early　warning　alerts　and　delivering　actionable　insights.　Experimental　evaluation　using　real-world　sensor　data　validates　that　the　proposed　approach　outperforms　existing　DL　methods.Originality/valueUnlike　traditional　approaches　that　are　limited　to　fixed　fault　distributions,　this　work　introduces　novel　design　propositions　for　industrial　fault　management　systems,　enabling　dynamic　learning　and　continuous　improvement　with　new　data.